This paper addresses this methodological gap by proposing a threat modeling framework called AgentHeLLM (Agent Hazard Exploration for LLM Assistants) that formally separates asset identification from attack path analysis. We demonstrate our approach under different sized instances by evaluating the PRM framework and subsequent allocations both quantitatively and qualitatively. Although there have been quite a few score-based variational inference methods proposed in the community, most are not adequate for large-scale BNNs for various computational and technical reasons. While existing Test-Time Optimization (TTO) methods prove effective for images or short clips, we identify that they fail to mitigate drift in extended sequences due to unstable reward landscapes and the hypersensitivity of distilled parameters. Distilled autoregressive diffusion models facilitate real-time short video synthesis but suffer from severe error accumulation during long-sequence generation. In these models, the decoder produces future values conditioned on contextual inputs, typically either actual historical observations (ground truth) or previously generated predictions. Grouping edges into fixed-sized batches regardless of their occurrence time leads to information loss or leakage, depending on the temporal granularity of the data. Dynamic link prediction is an important problem considered in many recent works that propose approaches for learning temporal edge patterns. This method aligns more closely with the inherent in-context mechanisms, and is more parameter-efficient without the need of using pre-trained LLM parameters. This is particularly valuable for patients on weight management medications or those preparing for bariatric surgery consultations. Research shows that it takes approximately 12 weeks to form lasting habits and see measurable body composition changes. Many people find this timeframe ideal because it’s long enough to see significant changes but short enough to maintain motivation. It can help you to set a realistic goal for yourself and then track your progress over time using a structured weigh-in tracker system. thoughts on “The Complete Pakistani Diet Plan for Weight Loss” For the decomposed subproblem, we utilize the quadratic penalty method and successive convex approximation (SCA) for the solution derivation. Mathematically, taking the constraints on sensing performance and transmission power in consideration, the system secrecy rate maximization problem is formulated with respect to transmit beamforming, RIS reflection, and receive beamforming. Overall, this AI-driven framework establishes a scalable and reproducible computational platform for investigating cellular biomechanics and assessing therapeutic efficacy in microphysiological systems. By quantitatively tracking dynamic changes in RBC morphology, this approach can more than double the experimental throughput via densely packed cell suspensions, capture drug-dependent sickling behavior, and reveal distinct mechanobiological signatures of cellular morphological evolution. We test the effect of each of our proposed improvements through an ablation study and demonstrate our method's effectiveness for both global localization and loop closure detection. Performance of trained models positively compares to state of the art with multi-class Panoptic Quality (mPQ) of 0.569 for nucleus segmentation, macro-averaged F1 of 0.832 for nucleus classification and mean Intersection over Union (mIoU) of 0.907 for tumor region segmentation. We detail SynPAT's methodology and benchmark several open-source symbolic regression systems on our generated theories and data. Extensive experiments on a mixed dataset containing seven degradation types show that DPMambaIR achieves the best performance, with 27.69dB and 0.893 in PSNR and SSIM, respectively. All-in-One image restoration aims to address multiple image degradation problems using a single model, offering a more practical and versatile solution compared to designing dedicated models for each degradation type. Multimodal machine learning, mimicking the human brain's ability to integrate various modalities has seen rapid growth. To reduce resource consumption and accelerate inference, it is essential to eliminate redundant parameters without compromising performance. Large Language Models (LLMs) have demonstrated impressive reasoning capabilities, but their substantial size often demands significant computational resources. Our empirical results reject the universality of the "Manifold Dilution" hypothesis, as two of the three architectures maintained stable residual norms despite exhibiting significant performance degradation on factual queries. To resolve this, we conducted a layer-wise geometric analysis across Qwen-4B, Llama-3.1-8B, and GLM-4-9B, decomposing the residual stream updates induced by counter-factual contexts into radial (norm-based) and angular (cosine-based) components. Are you tired of feeling stuck in a cycle of trying and failing to lose weight?By following clean eating principles, people can lose weight and improve their health.We first perform a global sensitivity analysis using both Sobol and Morris indices to assess how parameter uncertainty impacts model predictions, and fix the value of parameters with negligible impact.This paper introduces a dimension-decomposed geometric learning framework called Sliced Learning for disturbance identification in quadrotor geometric attitude control.Compared to state-of-the-art methods, our approach reduces the error rate by 20\% on the MVTec AD dataset, achieving an image-level AUROC of 99.28\%.Before embarking on any training program, a thorough assessment is crucial.Recent studies indicate that most existing unlearning approaches primarily alter the final classifier, leaving intermediate representations largely unchanged and highly similar to those of the original model.We introduce a structured multi-evaluator framework for assessing LLM reasoning in Merchant Category Code (MCC)-based merchant risk assessment, combining a five-criterion rubric with Monte-Carlo scoring to evaluate rationale quality and evaluator stability.Combining questionnaires and semi-structured interviews, we characterize how shifting the primary explanation channel reshapes explanation access, perceived responsiveness, immersion, and workload, and how visitors negotiate responsibility handoffs among staff, the AI guide, and themselves.Use the BMI healthy weight calculator Daily Workout Routine for Rapid Weight Loss - Hour by Hour We introduce a Geometry-guided virtual Outlier Synthesis (GOS) strategy that models statistical properties using the von Mises-Fisher (vMF) distribution on a hypersphere.Fine-tuning is the primary mechanism for adapting foundation models to downstream tasks; however, standard approaches largely optimize task objectives in isolation and do not account for secondary yet critical alignment objectives (e.g., safety and hallucination avoidance).Existing methods primarily target resolving conflicts between task updates, leaving the failure mode of over-counting shared knowledge unaddressed.For better data mining efficiency and quality, we present a collaborative multi-agent framework, comprising InferAgent, OrchestraAgent, and JudgeAgent for guidance, acceleration, and evaluation.Our results establish step-level filtering as a key principle for scalable CUA training and construct two new datasets (WebSTAR, WebSCORE) and a lightweight process reward model (StepRM) as practical tools to advance robust and efficient CUAs.To leverage the capabilities of Large Multimodal Model (LMM), we concatenate the image layers into a single composite input, apply joint captioning, and fine-tune the model using task-specific LoRA rather than full-parameter training.“For myself, I created the below flexible diet plan in a way that I didn't have to give up on my favourite foods.”At some point (exactly when differs for everyone), sustaining a healthier lifestyle becomes easy.Contrastive learning is employed to help the model focus on meaningful disc features and reduce sensitivity to irrelevant differences in image appearance. Second, FSTab provides a model-centric evaluation that quantifies how consistently a given model reproduces the same vulnerabilities across programs, semantics-preserving rephrasings, and application domains. Our results suggest that analyzing jailbreak features from a causal perspective is an effective and interpretable approach for improving LLM reliability. Extensive experiments, including baseline comparisons and causal structure validation, confirm the robustness of our causal analysis and its superiority over non-causal approaches. We introduce a comprehensive dataset comprising 35k jailbreak attempts across seven LLMs, systematically constructed from 100 attack templates and 50 harmful queries, annotated with 37 meticulously designed human-readable prompt features. Ensuring that API implementations and usage comply with natural language programming rules is critical for software correctness, security, and reliability. To address this gap, we propose the concept of Student-Tailored Personalized Safety and construct CASTLE based on educational theories.This in turn makes our method scalable to large-scale neural networks including Vision Transformers, and allows for richer variational density families.Weekly weigh-ins are more accurate, as they reflect your true weight over the course of a week using a consistent weigh in tracker schedule.If you weigh in daily, then this is a good daily weight tracker.You’ll need to know what makes you want to eat when you’re not hungry and have a plan for those moments.We propose an explainable deep statistical testing framework that augments deep two-sample tests with sample-level and feature-level explanations, revealing which individual samples and which input features drive statistically significant group differences.Large Language Models (LLMs) are commonly trained on multilingual corpora that include Greek, yet reliable evaluation benchmarks for Greek-particularly those based on authentic, native-sourced content-remain limited.It is safe to lose 10 kg in three months if you do it gradually with a balanced diet and frequent exercise. Listen to your body and adjust as needed. Strength training builds muscle and increases metabolism. For optimal results, aim to work out 4-5 times per week. Incorporate a balanced diet with proteins, carbs, and fats. Mix cardio with strength training for balanced results. To address this challenge, we introduce JSynFlow, a synthesised visual QA dataset for Japanese flowcharts, generated using large language models (LLMs). However, existing approaches fall short, with static methods risking irreversible information loss and dynamic strategies employing heuristics that insufficiently capture the query-dependent nature of token importance. Extensive experiments on multiple benchmarks demonstrate that our method achieves state-of-the-art performance while supporting real-time rendering up to 4K resolution at 100 FPS on one RTX 4090. Any tips for someone who’s trying to lose weight? Corpus-based verification further reveals a 19.6-point hallucination gap between top- and bottom-ranked MMLU-Pro Law models. Extensive experiments demonstrate that models trained on foundation model features consistently outperform the baseline in terms of predictive accuracy and generalization capabilities while exhibiting systematic differences among the foundation models. Foundation models pretrained on large-scale histopathology data have found great success in various fields of computational pathology, but their impact on regressive biomarker prediction remains underexplored. However, prior work suggests that these methods often falsely flag essays from non-native speakers as generated, due to their low perplexity extracted from an LLM, which is supposedly a key feature of the detectors. Scaling law validation across 8 to 256 GPUs demonstrates our method's excellent scalability under most conditions. On the LIBERO benchmark, the framework achieves throughput improvements of up to 59.25\% compared to existing synchronous strategies. Systematically drawing inspiration from asynchronous optimization ideas in large model RL, our framework designs a multi-level decoupled architecture. Continue to strive for slow and steady improvements over time, heading toward a more desirable result. A waist size of 35 inches (88 cm) is likely much healthier for a 5’ 8” male than 39 inches.31 Even though this may be your “end goal,” don’t expect to get there overnight. However, you shouldn’t define health as merely avoiding disease. Foods that are high in calories and low in nutrition percentage are everywhere, and they are far too easy to overeat.29 Low carb diets, keto diets, and intermittent fasting are other ways to naturally and sustainably reduce caloric intake.24 Based on this, we propose Context Preference Learning to calibrate model preferences to favor low-inertia responses over highinertia ones. Experiments on a controlled 2D study and high-fidelity 3D aerodynamic benchmarks (car and aircraft), validated by OpenFOAM simulations and miniature wind-tunnel tests with 3D-printed prototypes, demonstrate consistent gains in both optimization and guided generation. To address their inability to approximate conditional covariance in high dimensions, we develop a time- and memory-efficient algorithm for approximate covariance estimation. Drawing on Foucault's notion of the archive, Jameson's ideologeme, and Simondon's theory of individuation, we argue that training datasets function as quasi-archives whose discursive formations crystallize within latent space. Extensive experiments across multiple datasets and architectures show that GenLoRA attains higher effective LoRA ranks under smaller parameter budgets, resulting in superior fine-tuning performance. Our analysis further reveals that key configuration parameters introduce an inherent trade-off between performance and overhead, offering practical guidance for designing efficient and scalable RAG systems for entity matching and data integration. Machine learning practitioners frequently observe tension between predictive accuracy and group fairness constraints -- yet sometimes fairness interventions appear to improve accuracy. It is based on separation logic to represent abstract memory states and, unlike other separation-logic-based approaches, it employs a general-purpose separation logic solver Astral for satisfiability and entailment checking, which itself is based on translation to SMT. Experiments on datasets of up to 100 GB show that Bifrost achieves $2.54 \sim 22.32 imes$ speedup and reduces the communication by $84.15\% \sim 88.97\%$ compared to the SOTA redundancy-free secure data join protocol iPrivJoin.By partitioning computations based on the estimated Frequency of Change in the asynchronous inputs, large inference tasks can be decomposed into individually memoized sub-problems.The CareClinic app makes it simple to track your weight loss with automated reminders, detailed charts, and comprehensive logs.Consulting with a healthcare professional and setting realistic goals can help prevent excessive loose skin during weight loss.At DietDoctor, we believe that’s needless suffering, and likely a waste of your time and precious energy.Our experiments include two model families and a range of model sizes from each to construct a detailed picture of embedding scaling behavior. 50lb (23kgs) weight loss can be noticeable, depending on individual factors such as starting weight and body composition. Consulting with a healthcare professional and setting realistic goals can help prevent excessive loose skin during weight loss. Fast, drastic weight loss through crash diets or extreme exercise can increase the likelihood of loose skin (18). Gradual weight loss through a combination of exercise and balanced diet can help prevent excessive loose skin. What is the calorie target for this 14-day diet plan? If you’re just starting your fitness journey, strength training with compound movements is one of the most effective ways to build a strong foundation. Committing to a 12-week plan can teach you discipline and resilience—qualities that carry over into other aspects of your life. Over time, regular physical activity can help you fall asleep faster and improve the quality of your rest (10). Start using our app and you will see good results in a short time. We introduce the informed asymmetric actor-critic framework, allowing the critic to be conditioned on arbitrary state-dependent privileged signals without requiring access to the full state.Motivated by the Word Movers Distance (WMD) model, similarity is evaluated using the distance between individual words of queries and statements.Furthermore, on the MP3D and HM3D_OVON datasets, our method not only outperformed all TF methods but also surpassed all SFT methods, achieving comprehensive leadership in both SR (5% and 2%) and generalization.Search behaviour is characterised using synonymy and polysemy as users often want to search information based on meaning.As you progress into the sets of eight, make sure to be mindful of how close to failure you are using a 2 RIR approach to those sets.In parallel, physics-based models such as ordinary differential equations (ODEs) provide mechanistic structure for many dynamical processes. I work out consistently, most of the time. While the evidence is weak that either of these interventions will help with weight loss, there is likely little downside and you may see a small benefit. However, the group that took the multivitamin lost more weight – an average of 3.6 kg (8 pounds) more – and improved several health markers. On the other hand, reliable access to vitamins and minerals could perhaps mean decreased hunger levels and decreased cravings, thereby promoting weight loss.The above is speculation without strong supporting evidence. Low-resource machine translation (MT) has gained increasing attention as parallel data from low-resource language communities is collected, but many potential methods for improving low-resource MT remain unexplored. Projection-based model reduction enables efficient simulation of complex dynamical systems by constructing low-dimensional surrogate models from high-dimensional data. Human pairwise evaluations show that SolarGPT-QA outperforms general-purpose models in zero-shot settings and achieves competitive performance compared to instruction-tuned models for educational explanations in space weather and heliophysics. To our knowledge, TASTE is the first end-to-end approach that utilizes a reconstruction objective to automatically learn a text-aligned speech tokenization and embedding suitable for spoken language modeling. We propose a method that can achieve this through a attention-based aggregation mechanism and with speech reconstruction as the training objective. Recent efforts target spoken language models (SLMs) that not only listen but also speak for more natural human-LLM interaction. In particular, we use a particle-based approach whereby the solution to the Fokker-Planck equation is obtained by performing a series of density estimation tasks from the simulated trajectories, and we use a functional hierarchical tensor model to represent the density. The aim of this paper is to address issues such as multimodal data mixing, activity heterogeneity, and complex model deployment in sensor-based human activity recognition. Modern image generation models produce photorealistic images undermining the evidentiary foundation upon which journalism and public discourse depend.Given a novel object-task pair, the method retrieves a proxy exemplar from a database, establishes part-level correspondences via LLM reasoning, and texturizes affordances for feature-based point cloud transfer.When generating this plan, a central decision is how to physically represent all involved relations, an aspect in which existing Datalog engines are surprisingly restrictive and often resort to one-size-fits-all solutions.In this work, we combine local neighbor-aware patch features with a normalizing flow model and bridge the gap between the generic pretrained feature extractor and industrial product images by introducing an adapter module to increase the efficiency and accuracy of automated anomaly detection.In addition to weight loss, a four month time span will allow you to see improvements in your cardiovascular fitness if you're exercising regularly.Recent approaches usually require auxiliary training or specialization, and even training-free methods incur costly search or optimization.However, in-context examples may contain privacy-sensitive information that should not be revealed through model outputs.Lean protein on whole grain bread will fill you up, and skipping the top slice is an easy way to cut down on calories and carbs.One common challenge that arises in this setting is the out-of-distribution (OOD) error, which occurs when the policy leaves the training distribution. The system reconstructs binary event frames using run-length encoding, generates region proposals, and adaptively switches between frame mode and event mode based on object size and velocity. We present an energy-efficient anti-UAV system that integrates frame-based and event-driven object tracking to enable reliable detection of small and fast-moving drones. We show that these softly constrained denoisers exploit constraint knowledge to improve compliance over standard denoisers, while maintaining enough flexibility to deviate from it in case of misspecification with observed data. Theoretical analysis establishes information-theoretic bounds showing non-gamifiability - adversaries cannot improve through training due to fundamental space complexity constraints. Our approach introduces the Adversarial Robustness Quotient (ARQ), which quantifies the computational cost of verification relative to baseline generation, demonstrating exponential growth with orbit size. Still other people will prefer desiccated pig thyroid (contains T4 and T3), though this treatment remains controversial and is not embraced by all healthcare providers.89 If the free T4 is frankly low or the TPO antibody test returns high, it is possible – but not definite – that you will benefit from thyroid hormone treatment.87 In these cases, weight gain resulting from decreased metabolism usually does not exceed 15 pounds.86 Too much cortisol can increase hunger, bringing subsequent weight gain, especially around the midsection.95The most common causes of elevated cortisol are chronic stress and lack of sleep (see tip #11), or cortisone medication (tip #10). More broadly, this work contributes to the effort to incorporate biological principles into machine learning and supports the goals of neuro-inspired vision by illustrating how living neural systems can inform the design of efficient and biologically grounded computational models. In the era of large foundation models, the quality of embeddings has become a central determinant of downstream task performance and overall system capability. Along with the significant gains in performance of similarity ranking through WMD, we conclude that the use of pre-trained word embeddings, trained on vast amounts of data, result in domain agnostic language processing solutions that are portable to diverse business use-cases. Depth information can improve detection, but existing approaches require complex, model-specific architectural modifications. Evaluation on the MMEB and MMVP-VLM benchmarks shows that AGFF-Embed comprehensively achieves state-of-the-art performance in both general and fine-grained understanding compared to other multimodal embedding models. Vision-Language-Action (VLA) models have been attracting the attention of researchers and practitioners thanks to their promise of generalization. The models are motivated by common defects in metal casting but can be transferred to other machining procedures that produce similar defect shapes. Non-destructive testing (NDT) methods are preferred as they do not influence the functionality of the object while inspecting. Leveraging this insight, we propose Stable Velocity, a unified framework that improves both training and sampling. Experimental results demonstrate that our scheme achieve significantly lower computation and communication overhead compared to existing schemes in large ring sizes and multi-account payment scenarios. Experiments on diverse node- and graph-level datasets show consistent gains over prior graph prompting methods in few-shot settings, while achieving performance competitive with fine-tuning in full-shot regimes. We further evaluate the dataset on a representative set of state-of-the art methods for instance-based and novel object 6D pose estimation, including also object detection, segmentation, showing that there is room for improvement in this domain. The extended set introduces additional data modalities to support the evaluation of model-free and sequence-based approaches. The dataset provides a realistic and application-relevant testbed for benchmarking these methods in the context of industrial robotics bridging the gap between lab-based research and deployment in real-world manufacturing scenarios. In this paper, we introduce SWE-Replay, the first efficient and generalizable test-time scaling technique for modern agents without reliance on potentially noisy value estimates.So, while on a low-carb diet the fasting periods may become both easier to do and more effective.We find that models are remarkably robust to intrusive renaming and code insertion-based transformations, but that composed transformations and deeper obfuscation affect performance by requiring more sophisticated use of tools.Less expensive cuts of meat can sometimes be a bit tough.The proposed model retains the analytical simplicity of existing approaches (e.g., Gamma-based approximations) while overcoming their limitations, particularly the underestimation of SINR variance.As its name suggests, this caps the number of daily calories that can be consumed to 1,200, which is significantly fewer than what the average adult needs to maintain their current weight.Experiments across diverse domains demonstrate that AgentXRay achieves higher proxy similarity and reduces token consumption compared to unpruned search, enabling deeper workflow exploration under fixed iteration budgets.Current aggregation paradigms, however, either rely on data-hungry supervision or simplistic first-order statistics, often neglecting intrinsic structural correlations. Despite the promise related to availability and scale, the single most pressing question in AI for mental health is whether these tools are safe. Crucially, the agent mitigates cold start by bootstrapping discovery from minimal initial labels where static approaches fail. On the 26-dataset SYNERGY benchmark, AutoDiscover achieves higher screening efficiency than static AL baselines. We present EchoJEPA, a foundation model trained on 18 million echocardiograms across 300K patients, representing the largest pretraining corpus for this modality to date. Foundation models for echocardiography often struggle to disentangle anatomical signal from the stochastic speckle and acquisition artifacts inherent to ultrasound. Finally, we extend our analysis to dishonest learning algorithms, introducing Information-Theoretic Admissibility (ITA) to characterize the fundamental limits of privacy when the learning algorithm is oblivious to specific dataset instances. Nuts, avocados, and olive oil are good sources of healthy fats that are crucial for general health and satisfaction. Lean protein foods such as fish, poultry, tofu, and beans can also assist in retaining muscle mass, which is essential for keeping your metabolism running smoothly even while you’re losing weight. Because these foods are low in calories and high in fiber, you may eat more significant portions without going overboard. Extensive experiments on 15+ leading LLMs reveal that even frontier models exhibit a deficiency in inductive scenarios, identifying a critical bottleneck in the pursuit of autonomous discovery in complex environments. Our method aligns simulated next states produced by the model with realized next states observed from the environment, encouraging consistency between internal world simulations and actual environment dynamics in a pre-trained embedding space. However, in agentic settings, LLMs often struggle to anticipate action consequences and adapt to environment dynamics, highlighting the need for world-modeling capabilities in LLM-based agents. Our paper highlights the development, deployment, and evaluation of our framework and its transformative impact on synchrotron data acquisition. We present the design, deployment, and evaluation of a framework that addresses CHESS's data acquisition and management issues. We argue that this inconsistency stems partly from constraints in existing evaluation methods, including the oversight of plausible responses, limited emotional taxonomies, neglect of contextual factors, and labor-intensive annotations. Empirically, for both linear and non-linear models, we demonstrate that our algorithm achieves a significantly lower price of recourse (up to several orders of magnitude) compared to prior work and also exhibits a better trade-off between the implementation cost of recourse and its validity. However, since the optimization problem of computing robust recourse is non-convex (even for linear models), most of the current approaches do not have any theoretical guarantee on the optimality of the recourse. However, in practice, models often get updated to reflect changes in the data distribution or environment, invalidating the recourse recommendations (i.e., following the recourse will not lead to the desirable outcome). The experimental results show that MAIFormer achieves the best performance across multiple metrics and outperforms other methods. However, prior hyperbolic GCD methods only use hyperbolic geometry for representation learning and transform back to Euclidean geometry when clustering. We show that decoder-only large language models exhibit a depth-wise transition from context-processing to prediction-forming phases of computation accompanied by a reorganization of representational geometry. This design enables dense, step-by-step steering toward high-reward regions, advancing beyond the unguided exploration in prior works, and theoretically encompasses existing sampling methods (e.g., Flow-GRPO, DanceGRPO) as special cases. Relying on undirected stochasticity and sparse outcome rewards, these methods struggle to discover high-reward samples, resulting in data-inefficient and slow optimization. We advocate replacing universality with a Causal Control Agent paradigm, where an agent leverages external context to orchestrate a hierarchy of specialized solvers, from frozen domain experts to lightweight Just-in-Time adaptors. Healthcare facility visit prediction is essential for optimizing healthcare resource allocation and informing public health policy. However, when the target lies in a low-density region of the prior, posterior sampling requires aggressive and brittle weighting of the likelihood guidance. Specifically, on Qwen2.5-7B, our method outperforms GRPO by 5.7% in Pass@1 and, notably, by 13.9% in Pass@32, highlighting its superior capability in generating diverse correct reasoning paths. By incorporating Prompt Perplexity and Answer Confidence into the advantage estimation, our method dynamically reshapes the reward signal to attenuate the gradient updates of over-confident reasoning paths, while redistributing probability mass toward under-explored correct solutions. Listening To Your Body Download the entire weight loss meal plan by clicking on this link. For personalised advice, seek the services of an Accredited Practising Dietitian.• Those with a medical issue should seek advice from a medical practitioner before commencing a weight loss plan. The NHS weight loss guide has been developed under the supervision and advice of specialist dietitians from the British Dietetic Association, which represents registered dietitians in the UK. In this cross sectional study, 180 ophthalmology patient queries were answered by each model, generating 2160 responses. This study evaluated four small medical LLMs Meerkat-7B, BioMistral-7B, OpenBioLLM-8B, and MedLLaMA3-v20 in answering ophthalmology related patient queries and assessed the feasibility of LLM based evaluation against clinician grading. To ensure robust learning and prevent catastrophic forgetting, we introduce a ranked preference mixup strategy that carefully balances exploration with adherence to initial human priors. Experimental results show that EC not only achieves effective logit-level forgetting, but also substantially reduces representational similarity to the original model across intermediate layers. EC integrates multi-layer contrastive unlearning on the forget set with retain set preservation through deeply supervised learning. The Mayo Clinic Diet: A weight-loss program for life Capitalizing on this insight, we propose introducing the video generation model into this field for the first time. However, this raises a critical challenge of authorship, as users and models jointly shape text across interaction turns. Experimental results on benchmark datasets with three open-source LLMs show that TKG-Thinker achieves state-of-the-art performance and exhibits strong generalization across complex TKGQA settings. We first apply Supervised Fine-Tuning (SFT) with chain-of thought data to instill core planning capabilities, followed by a Reinforcement Learning (RL) stage that leverages multi-dimensional rewards to refine reasoning policies under intricate temporal constraints. These tournaments are aggregated into a global preference graph, whose transitive closure yields many additional orderings without further model invocations. Through a user study, we show that DiLLS significantly improves developers' effectiveness and efficiency in identifying, diagnosing, and understanding failures in LLM-based multi-agent systems. To address this challenge, we propose a framework and an interactive system, DiLLS, designed to reveal and structure the behaviors of multi-agent systems. It is thanks to recent advances in approximate nearest neighbor search-with the emergence of highly efficient algorithms such as the inverted index-based Seismic and the graph-based Hnsw-that retrieval with sparse representations became viable in practice. Minestrone soup with a grilled chicken salad makes a healthy choice for lunch. For energy and continued hunger control, eat three nutritionally balanced meals plus one to two snacks each day, advises the Mayo Clinic. Limit or avoid unhealthy saturated fats that are found in butter, stick margarine, meat fats, and palm oil. Finally, focus on healthy unsaturated fats, which provide energy and heart health benefits. Most people have – stress and lack of sleep can be bad news for weight.Chronic stress and inadequate sleep may increase levels of stress hormones such as cortisol in your body. There are many different options within these two categories; what you need to know is the drugs in these classes reduce the need for insulin and may also cause weight loss by other mechanisms — beyond just the effect of using less insulin. The fewer carbs you eat the less insulin you need.46 Remember to work closely with your healthcare provider to ensure you safely lower your doses. A. Eat fewer carbs, which makes it easier to lose weight. This study explores the integration of multiple Explainable AI (XAI) techniques to enhance the interpretability of deep learning models for brain tumour detection. These results indicate that CDAS is complementary to preference-optimization approaches and conditionally constitutes a robust approach to intervention-based model steering. On AxBench, a large-scale model steering benchmark, we show that CDAS does not always outperform preference-optimization methods but may benefit more from increased model scale. If you have been significantly overweight or obese for a long time, then you might have concerns about what the extra weight could be doing to your health.We propose message-adaptive graph prompt tuning, which injects learnable prompts into the message passing step to reweight incoming neighbor messages and add task-specific prompt vectors during message aggregation, while keeping the backbone GNN frozen.In this paper we propose an extension of the canonical, string-based formulation which augments pure concatenation with templated assembly steps.We compare their diagnostic performance against specialised EEG models and assess the quality of the extracted features.There are a few reasons why protein should form the foundation of your approach when it comes to healthy weight loss as a woman.We propose a Real-Sim-Real data collection and data editing pipeline that transforms human demonstrations into robot-executable, environment-specific training data without direct robot teleoperation.We give a theoretical account of our definition of GOP-SF, an implementation that solves potential numerical issues as well as a proper normalization which allows the use of acoustic models with different peakiness over time. thoughts on “1200 Calorie Meal Plan for Weight Loss” The fact that you read this blog post shows that you have some sort of motivation to lose weight or change your lifestyle into a healthier one already, so go you. For me, I enjoy mainly weight training (bodybuilding style) as well as plyometrics and cardio. This person did extensive amount of research on how to workout and eat properly, and he taught me the healthy, sustainable way to lose weight. In particular, achieving both secure aggregation and Byzantine resilience remains challenging, as existing solutions often address these aspects independently. Notably, the proposed framework has been deployed in a large-scale industrial search system, yielding substantial improvements in online user engagement rates and satisfaction metrics. Then, the LLM-based re-ranker performs the holistic evaluation based on these principles and integrated signals. Our approach begins with a Query Planner that analyzes the sequence of query refinements within a session to capture genuine search intents, decomposing the query into clear and complementary sub-queries to enable broader coverage of users' potential intents. Through FHIR integration, H-AdminSim provides a unified and interoperable environment for testing administrative workflows across heterogeneous hospital settings, serving as a standardized testbed for assessing the feasibility and performance of LLM-driven administrative automation. The approach is stable, generally applicable across backbones, and consistently improves the performance of ResNet and ViT models on ImageNet.For optimal results, aim to work out 4-5 times per week.For such models, the results show that the UxHw method leads to as much as 37.7x speedup compared to the Monte Carlo alternative.WaveTrainerFit builds upon the WaveFit vocoder, which integrates diffusion model and generative adversarial network.Initial pilot studies indicated that users value this mixed-initiative approach, finding the balance between AI suggestions and direct manipulation crucial for maintaining interpretability and trust.Check out our full keto family-friendly meal plan, which provides less than 20 grams of net carbs per day.In this paper, we propose Amadeus, a training-free framework that can significantly enhance persona consistency even when responding to questions that lie beyond a character's knowledge.The 14-day diet plan in this article helps you lose weight quickly.We propose Evolving Retrieval Memory (ERM), a training-free framework that transforms transient query-time gains into persistent retrieval improvements. Proteus achieves performance in line with regular TEE-enabled consensus protocols, while guaranteeing integrity in the face of TEE platform compromises. Distributed ledgers are increasingly relied upon by industry to provide trustworthy accountability, strong integrity protection, and high availability for critical data without centralizing trust. Through human gait and respiration experiments in indoor environments, we demonstrate that phase-coherent channel impulse responses and corresponding delay--Doppler responses are obtained using only commodity Wi-Fi devices. Several numerical experiments are presented to validate the theoretical results and show performance on fine grids. From these analyses, we establish training strategies that guarantee optimal convergence rates under grid refinement. Unlike previous hybrid approaches that integrate Attention and Mamba modules in fixed proportions, our unified design flexibly combines their capabilities within a single cohesive architecture, eliminating the need for manual ratio tuning and improving encode capability. Inspired by the recent success of the Mamba architecture in vision and language domains, we introduce a Unified Attention-Mamba (UAM) backbone. We first model a decoupled screw motion for each joint without type prior, and jointly optimize part-aware Gaussians with joint parameters through part motion blending. Towards their part-level surface reconstruction and joint parameter estimation, REArtGS introduces a category-agnostic approach using multi-view RGB images at two different states. This formulation enables not only geometric localization but also the estimation of task-relevant properties for parametric objects, such as a gripper's opening, where the 3D model is adjusted according to simple, predefined rules. Our results suggest that VLLM-based pilots may dramatically reduce operator workload while improving safety and mission flexibility in constrained indoor environments. Experimental results highlight the promise of replacing remote drone pilots with a language-guided autonomous agent, opening avenues for scalable, human-friendly control of indoor UAVs in tasks such as inspection, search-and-rescue, and facility monitoring. All the related resources, including research papers, open-source data, and projects, are collected for the community in this https URL. This survey aims to offer actionable insights to advance the development of more efficient and reliable graph-based agent memory systems. First, we introduce a taxonomy of agent memory, including short-term vs. long-term memory, knowledge vs. experience memory, non-structural vs. structural memory, with an implementation view of graph-based memory. Among diverse paradigms, graph stands out as a powerful structure for agent memory due to the intrinsic capabilities to model relational dependencies, organize hierarchical information, and support efficient retrieval. Drink at least 64 ounces of water daily to stay hydrated and to help you feel full longer, and get minutes of a moderate activity like running or swimming every day. Whatever the reason, you’re hoping to take a big step forward in your weight-loss goals—and fast. This article has been viewed 3,219,947 times. She also has experience as a Clinical Instructor at the University of Tennessee, teaching physical health assessment, medical-surgical and community nursing, and supervising and guiding nursing undergraduate students. Luba Lee, FNP-BC is a Board-Certified Family Nurse Practitioner (FNP) and educator based in Tennessee. While model merging provides an effective transfer mechanism, most existing approaches assume architecture-compatible models and therefore cannot directly transfer knowledge from large high-resource LLMs to heterogeneous low-resource targets. We theoretically demonstrate that the KL3-based constraint is mathematically equivalent to an asymmetric ratio-based clipping that reallocates probability mass toward high-confidence actions, promoting stronger exploration while retaining the simplicity of GRPO-style methods. This paper introduces a unified clipping framework that characterizes existing methods via a general notion of policy divergence, encompassing both likelihood ratios and Kullback-Leibler (KL) divergences and extending to alternative measures. Most existing RLVR methods, such as GRPO and its variants, ensure stable updates by constraining policy divergence through clipping likelihood ratios. The proposed methodology leverages transfer learning to adapt the pre-trained model to the idiosyncratic linguistic features of urban Latin music, including slang, metaphors, and culturally specific double entendres that evade conventional dictionary-based filtering systems. Extensive experiments on two large-scale real-world datasets, TAOBAO-MM and KuaiRec, demonstrate that GLASS outperforms state-of-the-art baselines, achieving significant gains in recommendation quality. Unlike traditional retrieval models that struggle with massive item spaces, SID-Tier leverages the compact nature of the semantic codebook to incorporate cross features between the user's long-term history and candidate semantic codes. The results demonstrate consistent improvements in forecast accuracy, calibration, and prediction interval quality, underscoring the suitability of the proposed method for uncertainty-aware energy management and operational decision-making in renewable-dominated power systems. The approach is evaluated using 30 years of hourly PV generation data from 259 European regions and compared against established statistical and neural probabilistic baselines. Existing approaches typically treat tool retrieval as a traditional ad-hoc retrieval task, matching user queries against the entire raw tool documentation. These findings validate the proposed framework's ability to deliver practical benefits of orchestrated multi-operator collaboration in future NTN environments. Unlike centralized orchestration frameworks, where the orchestrator determines the entire route from source to destination, the proposed framework allows each operator to select preferred routes from multiple candidates provided by the orchestrator. Additionally, consuming protein causes your body to burn more calories than digesting fats or carbohydrates, which speeds up your metabolism. Water is a far healthier option than sugary drinks, and you’ll avoid consuming many extra calories. Regularly consuming large amounts of water can be highly beneficial for weight loss. Furthermore, GeoThinker demonstrates robust generalization and significantly improved spatial perception across complex downstream scenarios, including embodied referring and autonomous driving. Instead of feature mixing, GeoThinker enables the model to selectively retrieve geometric evidence conditioned on its internal reasoning demands. By generating draft tokens in a single forward pass and conditioning the draft model on context features extracted from the target model, DFlash enables efficient drafting with high-quality outputs and higher acceptance rates. We further demonstrate its effectiveness for user-interactive control and its potential for real robot deployment. This meal plan is the second week of our free 14-day keto diet plan. This meal plan includes all of the recipes from the first week of our free 14-day keto diet plan. This diet plan is for adults with health issues, including obesity, that could benefit from a keto diet. Specifically, we design a lightweight adapter to unify the different conditions in different tasks, then employ a joint image-video learning strategy to progressively train the model from scratch. Our experiments on instance generative models demonstrate that instances with high structural similarity scores can still exhibit drastically divergent solver interactions and difficulty levels. We propose a Trefftz discontinuous Galerkin (TDG) method for the approximation of plane wave scattering by periodic diffraction gratings, modelled by the two-dimensional Helmholtz equation. The results show that, while most models perform well in binary polarization detection, they achieve substantially lower performance when predicting polarization types and manifestations. Realising how far you’ve come in your weight loss journey can also be motivating. Water also affects metabolism, making it easier for your body to burn calories.5. Have a balanced diet- Consuming a diet high in fruits, vegetables, whole grains, and lean meats will keep you fully satisfied while providing your body with essential nutrients. However, most existing systems depend on pre-scanned, static environments and rely heavily on continuous tracking or marker-based solutions, which limit adaptability in dynamic physical spaces. Autoregressive attention-based time series forecasting (TSF) has drawn increasing interest, with mechanisms like linear attention sometimes outperforming vanilla attention. This enables rapid adaptation to new tasks by learning only lightweight coefficients on the principal components of the subspace-eliminating the need to finetune entire adapters. We introduce EigenLoRAx, a parameter-efficient finetuning method that recycles existing adapters to create a principal subspace aligned with their shared domain knowledge which can be further augmented with orthogonal basis vectors in low-resource scenarios. This combination ensures comprehensive fitness and weight loss benefits. Cardio exercises boost heart health and burn calories. It helps in establishing habits, improving fitness levels, and promoting weight loss. Across diverse all-atom benchmarks, the proposed approach yields consistent gains in heterogeneous structure-grounded reasoning. The method first constructs variable-size structural patches on molecular graphs using an instruction-conditioned gating policy, enabling complexity-aware allocation of query tokens. Rather than relying on predefined sociological taxonomies, these structures emerge directly from machine-generated data traces. Using programmatic and non-intrusive data acquisition, we collected and analyzed the textual descriptions of 12,758 submolts, which represent proactive sub-community partitioning activities within the ecosystem. While these studies don’t prove that the veggies caused the weight loss, they do show that low-carb, fiber-filled veggies can be part of a rapid weight loss diet. This advice is designed to produce fast weight loss during the initial stage of the diet. For example, a woman who currently weighs 170 pounds but whose ideal body weight (or reference weight) is 130 pounds (59 kilos) would aim to eat 69 to 118 grams of protein per day.19 “A healthy jumpstart should always include at least 50 percent fresh vegetables,” Kaufman says. Eat the bulk of your calories before 3 p.m. For the entire diet—complete with recipes and a grocery guide—check out The 14-Day No Sugar Diet on sale now! Or down an extra glass of wine with your BFF at happy hour, aim to omit one or two of those triggers from your agenda each day. “That helps me clean up my choices and also prevents any of those not-worth it bites that do add up over the course of a day.” Reliance on images for dietary assessment is an important strategy to accurately and conveniently monitor an individual's health, making it a vital mechanism in the prevention and care of chronic diseases and obesity. Simulated ADR scenarios derived from the Iridium 33 debris dataset are used for evaluation, covering diverse orbital configurations and debris distributions to demonstrate robustness and adaptability. The proposed approach employs a masked Proximal Policy Optimization (PPO) algorithm, enabling the RL agent to dynamically adjust maneuvers in response to real-time orbital conditions. HBO provides a comprehensive solution to the challenges of data imbalance and heterogeneity in LLM fine-tuning, enabling more effective training across diverse datasets. Our in-depth analysis further demonstrates that both the global actor and local actors of HBO effectively adjust data usage during fine-tuning. We introduce Hierarchical Balancing Optimization (HBO), a novel method that enables LLMs to autonomously adjust data allocation during fine-tuning both across datasets (globally) and within each individual dataset (locally). Each week, you’ll have a combination of resistance training, mobility, and cardio workouts that are designed to challenge your body and help you achieve your goals. Get ready to sweat, because the 12 week weight loss gym routine for females is here to guide you towards your goals every step of the way. You should listen to your body, make necessary modifications, and consult healthcare or fitness professionals if needed. In other words, the key is to get healthy, and by getting healthy, your body will transform into a lean mean beach ready machine. Listen to your body, make adjustments as needed, and consult with healthcare professionals or your doctor if you have any specific concerns or conditions. Many people who want to lose weight have more than 5-10% to lose. While GLP-1 analogues like liraglutide and semaglutide show promise, they are incredibly expensive and, like all weight loss drugs, they only work for as long as you take them. There is no weight loss drug that easily makes people thin. It has also been found to promote substantial weight loss – likely better than any drug currently available – in a trial of overweight people without diabetes.103 TThe FDA approved semaglutide for use with weight loss, and many feel this it is a “gamechanger” when it comes to medical weight loss. Existing hierarchical forecasting methods typically generate base forecasts independently for each series and then apply a reconciliation procedure to adjust them so that the resulting forecast values are coherent across the hierarchy. This paper focuses on forecasting hierarchical time-series data, where each higher-level observation equals the sum of its corresponding lower-level time series. While standard Transformers are limited by quadratic complexity and poor length extrapolation, alternative architectures like sliding window attention and state space models sacrifice the ability to effectively utilize the full context due to their fixed-size memory. This dual connection system enables the model to exploit correlations among different datasets while ensuring that each level makes an additive correction to the previous level without altering it. However, in practical situations, data may be different in types, come from sources of different modalities, and not be concurrently available, further complicating the modeling process. Pull-ups are a fantastic upper-body exercise that works your back, biceps, and core. The overhead press targets your shoulders, triceps, and core, helping build upper body strength and stability. Still, compound movements are a good starting point if you want to transform your body in 12 weeks as a beginner. In the multi-stage ranking phase, point-wise and pair-wise ranking strategies are used one after another based on model continual pre-trained on general knowledge and document-level data. We further compare normalization with existing regularization-based approaches for handling dual degeneracy and explain why normalization offers key advantages. We complement the theoretical analysis with simulation results showing that our methods significantly outperform existing benchmarks. To address this issue, we propose an innovative weakly supervised waveform simulation and reconstruction approach based on a bidirectional conditional diffusion network framework. We illustrate the applicability and sharpness of our results in (auto-) regression problems with linear models, basis approximations, and neural networks, recovering minimax-optimal rates (up to logarithmic factors) when specialized to unweighted and stationary settings. In addition, the reconstructions are obtained with a substantially reduced computational cost compared to alternative methods. Despite recent advances, existing approaches still face limitations in both reconstruction quality and computational efficiency. However, existing NAC-based online LM systems are designed for voice conversion (VC) rather than anonymization, lacking the techniques required for privacy protection. Large language models rely on kv-caches to avoid redundant computation during autoregressive decoding, but as context length grows, reading and writing the cache can quickly saturate GPU memory bandwidth. Experiments on four real-world datasets show that 2DReach achieves faster index construction than 3DReach, with the compressed variant yielding the smallest index size among all methods. Second, we provide data-driven models that map pre-impact speed to impulse and contact duration, enabling direct computation of speed bounds for a target force limit. These findings highlight the potential of hybrid quantum-classical reinforcement learning models for addressing complex combinatorial optimization problems such as the CVRP. The methodology achieves significant reductions in Syntax Error Rates, enhances feature alignment throughout migration iterations, and leverages dataset sampling to ensure continual improvement. Special attention is given to a fine-tuning approach, which enhances the adaptability and compatibility with migration requirements across the entire database. Qualitatively, these reconstructions help investigate the physical scene attributes to which models are sensitive or invariant. When it comes to fast weight loss, it’s important to take a healthy approach — one that promotes loss of fat, retention of muscle, and increases your likelihood of keeping the weight off. Remember to consult with healthcare professionals, including doctors, registered dietitians, and certified personal trainers, for personalized guidance and support throughout your weight loss journey. Anyone considering a diet like this should definitely consult a healthcare provider — especially if aiming for rapid weight loss. Get instant access to healthy low-carb and keto meal plans, fast and easy recipes, weight loss advice from medical experts, and so much more. In addition, to underpin the development and rigorous evaluation of RAG-based RPAs, we manually construct CharacterRAG, a role-playing dataset that consists of persona documents for 15 distinct fictional characters totaling 976K written characters, and 450 question-answer pairs. In this paper, we propose Amadeus, a training-free framework that can significantly enhance persona consistency even when responding to questions that lie beyond a character's knowledge. Furthermore, PSA exhibits superior instruction adherence compared to prompt-engineering methods, establishing personalization as a vital direction for creating adaptive, user-centered, and responsible generative AI. Specifically, we employ a generative model to capture the data generation process and identify the underlying bias factors, which are then used to construct a bias-aware predictor. Second, AlphaCC adopts a modified attention-based encoder based on AlphaFold to model dependencies within and across token sequences. To learn more about weight loss, buy Healthy Solutions to Lose Weight and Keep It Off, a Special Health Report from Harvard Medical School. Even if you don't reach your ideal weight-loss goal, you want to succeed in living a heart-healthy lifestyle. For some people, hunger-suppressing medications or weight-loss surgery can help them lose a significant amount of weight and keep it off. No matter what your workout routine looks like, the most important thing is to get your body moving regularly. While you can’t spot reduce body fat, you can target and strengthen specific muscle groups. Your BMR is your base metabolic rate, the amount of energy expended while your body is at rest. How do you know when your body is burning fat? Losing weight is an individual journey that looks different for everyone, but don’t be discouraged. The effectiveness and privacy-preserving properties of the proposed control strategy are demonstrated through simulation results. The proposed power allocation law based on these estimators ensures asymptotic SoC balancing and global power delivery while safeguarding agent privacy from external eavesdroppers. The average unit state estimator is designed via the state decomposition method without disclosing sensitive internal states. While distributed control frameworks offer scalability and resilience, they also raise significant privacy concerns due to the need for inter-agent information exchange. This enables abnormal patterns to be injected across different temporal segments at varying scales based on variational reparameterization. We propose a synthetic anomaly generation method named Generator for Instantiating Anomalies in Time Series (GenIAS), which generates realistic and diverse anomalies via a novel learnable perturbation in the latent space of a variational autoencoder. Then, we propose Structural Aligned Mixture of VAR (SAMoVAR), a linear Transformer variant that integrates interpretable dynamic VAR weights for multivariate TSF. Team up with an Anytime Fitness Coach to get extra support for your weight loss journey, including personalized workout plans, nutrition advice, and accountability. When you set action-based goals, you’ll be more likely to stick to your weight loss workout plan and see the results you want. Incorporate active recovery into your gym routine to improve your flexibility, boost your mood, increase your energy levels, and help you stick to your weight loss training plan. Regular sleep patterns can not only help you lose weight, but also maintain a healthy weight over time. We contribute a decision theoretic framework that treats explanations as information signals valued by the expected improvement they enable on a specified decision task. Explanations of model behavior are commonly evaluated via proxy properties weakly tied to the purposes explanations serve in practice. A TS framework is proposed in this work to reduce control costs through online exploration and exploitation, and the convergence guarantees are further provided for the learning process. We establish a new size-performance trade-off -- unlocking a potential $11.6 imes$ inference speedup relative to FP16 -- and render powerful LLMs practical for resource-constrained environments. Based on extensive profiling, we implemented four targeted optimization strategies, including the replacement of inefficient data structures, reorganization of memory layouts to improve cache hit rates, and the use of hardware-accelerated bit-wise operations. Most remarkably, its zero-shot performance on pediatric patients surpasses fully fine-tuned baselines, establishing latent prediction as a superior paradigm for robust, generalizable medical AI. Crucially, EchoJEPA demonstrates superior generalization, degrading by only 2% under physics-informed acoustic perturbations compared to 17% for competitors. Eat foods that nourish and fuel your body to ensure a balanced source of energy throughout the day. Our exclusive slimdown program has all the tools you need to be unstoppable and on the road to achieving permanent weight loss success! This program provides a framework, but individual adjustments might be necessary based on personal needs and preferences.