All models can be trained entirely on CPUs on consumer grade Laptop machines within minutes orhours. Execution times per epoch for the single-cell data with 529 features are as follows: Base=0.9, Centering the first frame: For golfing and waving, the root point of the first frame is movedtotheorigin(0,0,0). To map putative transcription factor (TF) and target gene relationships, we use as a reference a regulatory network generated using the gene expression and chromatin accessibility features 15 available inthehuman immune cells dataset. Ourruleforsuccessfully mapping aTFtoatargetgene through achromatin peak isthatall TF, chromatin peak, and target gene, have to be simultaneously in the list of features selected in therank_genes_groupsfunction for cell type of interest, and there haveto be TF motifs linked to that transcription factor in the chromatin peak.

Recent curriculum techniques in the post-training stage of LLMs have been widely observed to outperform non-curriculum approaches in enhancing reasoning performance, yet a principled understanding of why and to what extent they work remains elusive. To address this gap, we develop a theoretical framework grounded in the intuition that progressively learning through manageable steps is more efficient than directly tackling a hard reasoning task, provided each stage stays within the model's effective competence. Under mild complexity conditions linking consecutive curriculum stages, we show that curriculum post-training avoids the exponential complexity bottleneck. To substantiate this result, drawing insights from the Chain-of-Thoughts (CoTs) solving mathematical problems such as Countdown and parity, we model CoT generation as a states-conditioned autoregressive reasoning tree, define a uniform-branching base model to capture pretrained behavior, and formalize curriculum stages as either depth-increasing (longer reasoning chains) or hint-decreasing (shorter prefixes) subtasks. Our analysis shows that, under outcome-only reward signals, reinforcement learning finetuning achieves high accuracy with polynomial sample complexity, whereas direct learning suffers from an exponential bottleneck. We further establish analogous guarantees for test-time scaling, where curriculum-aware querying reduces both reward oracle calls and sampling cost from exponential to polynomial order.

The goal of clustering is to group similar objects into meaningful partitions.

Unimodal vision models are known to rely on spurious correlations, but it remains unclear to what extent Multimodal Large Language Models (MLLMs) exhibit similar biases despite language supervision. In this paper, we investigate spurious bias in MLLMs and introduce SpurLens, a pipeline that leverages GPT-4 and open-set object detectors to automatically identify spurious visual cues without human supervision. Our findings reveal that spurious correlations cause two major failure modes in MLLMs: (1) over-reliance on spurious cues for object recognition, where removing these cues reduces accuracy, and (2) object hallucination, where spurious cues amplify the hallucination by over 10x. We validate our findings in various MLLMs and datasets. Beyond diagnosing these failures, we explore potential mitigation strategies, such as prompt ensembling and reasoning-based prompting, and conduct ablation studies to examine the root causes of spurious bias in MLLMs. By exposing the persistence of spurious correlations, our study calls for more rigorous evaluation methods and mitigation strategies to enhance the reliability of MLLMs.

Accurate and interpretable user satisfaction estimation (USE) is critical for understanding, evaluating, and continuously improving conversational systems. Users express their satisfaction or dissatisfaction with diverse conversational patterns in both general-purpose (ChatGPT and Bing Copilot) and task-oriented (customer service chatbot) conversational systems. Existing approaches based on featurized ML models or text embeddings fall short in extracting generalizable patterns and are hard to interpret. In this work, we show that LLMs can extract interpretable signals of user satisfaction from their natural language utterances more effectively than embedding-based approaches. Moreover, an LLM can be tailored for USE via an iterative prompting framework using supervision from labeled examples. The resulting method, Supervised Prompting for User satisfaction Rubrics (SPUR), not only has higher accuracy but is more interpretable as it scores user satisfaction via learned rubrics with a detailed breakdown.

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Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A robot dog armed with a sniper rifle was unveiled this week in Washington, D.C. at the annual meeting of the Association of the United States Army. The robot, developed by Ghost Robotics, carries a SWORD Defense Systems Special Purpose Unmanned Rifle (SPUR). Check out the latest partner payloads @AUSAorg Wash DC.

Boston Dynamics, the company most commonly associated with robot dogs, prohibits the weaponization of its Spot devices. One of them, Ghost Robotics, showed off a version of its Q-UGV device that many will have been dreading. It's a robot dog with a gun attached to it. Ghost Robotics has made robot dogs for the military, and it displayed this deadly model at the Association of the United States Army's 2021 annual conference in Washington DC this week. A company called Sword International built the "special purpose unmanned rifle" (or SPUR) module. According to The Verge, it has a thermal camera for nighttime operation, an effective range of 1.2km (just under three quarters of a mile) and a 30x optical zoom.

A robot dog design armed with a 6.5 mm Creedmoor sniper rifle capable of precisely hitting targets from 3,940 feet away has been unveiled at the US Army trade show. The'Special Purpose Unmanned Rifle' (SPUR) is the brainchild of Philadelphia-based Ghost Robotics and arms manufacturer SWORD International of Sparks, Nevada. Placed on top of one of Ghost Robotics' existing'quadrupedal unmanned ground vehicle' designs, SPUR can be remotely instructed to load, unload and fire its rifle. The firms have yet to reveal the exact configuration of the weapon, nor how much ammunition the machine is capable of carrying or its reload rate. However, tests have shown that the 6.5mm rounds used in the Creedmoor rifle offer an increase in range over the 7.62x51mm cartridges currently used by US forces. It is also presently unclear how much each robot unit and SPUR attachment will cost to purchase and maintain.

Iterative Magnitude Pruning (IMP) is a network pruning method that repeats the process of removing weights with the least magnitudes and retraining the model. When visualizing the weight matrices of language models pruned by IMP, previous research has shown that a structured pattern emerges, wherein the resulting surviving weights tend to prominently cluster in a select few rows and columns of the matrix. Though the need for further research in utilizing these structured patterns for potential performance gains has previously been indicated, it has yet to be thoroughly studied. We propose SPUR (Structured Pattern pruning Using Regularization), a novel pruning mechanism that preemptively induces structured patterns in compression by adding a regularization term to the objective function in the IMP. Our results show that SPUR can significantly preserve model performance under high sparsity settings regardless of the language or the task. Our contributions are as follows: (i) We propose SPUR, a network pruning mechanism that improves upon IMP regardless of the language or the task. (ii) We are the first to empirically verify the efficacy of "structured patterns" observed previously in pruning research. (iii) SPUR is a resource-efficient mechanism in that it does not require significant additional computations.