Miracle-dz/UniFAR-FTUs huggingface dataset:
Meta-Point Learning and Refining for Category-Agnostic Pose Estimation

Category-agnostic pose estimation (CAPE) aims to predict keypoints for arbitrary classes given a few support images annotated with keypoints. Existing methods only rely on the features extracted at support keypoints to predict or refine the keypoints on query image, but a few support feature vectors are local and inadequate for CAPE. Considering that human can quickly perceive potential keypoints of arbitrary objects, we propose a novel framework for CAPE based on such potential keypoints (named as meta-points). Specifically, we maintain learnable embeddings to capture inherent information of various keypoints, which interact with image feature maps to produce meta-points without any support. The produced meta-points could serve as meaningful potential keypoints for CAPE. Due to the inevitable gap between inherency and annotation, we finally utilize the identities and details offered by support keypoints to assign and refine meta-points to desired keypoints in query image. In addition, we propose a progressive deformable point decoder and a slacked regression loss for better prediction and supervision. Our novel framework not only reveals the inherency of keypoints but also outperforms existing methods of CAPE. Comprehensive experiments and in-depth studies on large-scale MP-100 dataset demonstrate the effectiveness of our framework.
New prospects on the NKG2D/NKG2DL system for oncology

The activating immunoreceptor NKG2D endows cytotoxic lymphocytes with the capacity to recognize and eliminate infected or malignant cells. The recognition of such harmful cells is enabled by binding of NKG2D to various MHC class I-related glycoproteins, which are upregulated in the course of viral infection or malignant transformation. The past years have witnessed substantial progress in our understanding of the mechanisms underlying the regulation of NKG2D ligands (NKG2DLs) by malignant cells, of tumor-associated countermeasures promoting escape from NKG2D-dependent immunosurveillance, and of therapeutic measures that may bolster the NKG2D/NKG2DL system against malignancies. Here, we summarize the current knowledge on the NKG2D/NKG2DL system and outline opportunities to exploit the tumoricidal function of NKG2D for anticancer immunotherapy.
Topological and geometric restrictions on hyperconvex representations

We study the geometry of hyperconvex representations of hyperbolic groups in ${\rm PSL}(d,\mathbb{C})$ and establish two structural results: a group admitting a hyperconvex representation is virtually isomorphic to a Kleinian group, and its hyperconvex limit set in the appropriate flag manifold has Hausdorff dimension strictly smaller than $2$.
Teaching Research: Encouraging Discoveries

Abstract What does it take to turn a learner into a discoverer? Or to turn a teacher into a co-adventurer? A handful of experiences—from teaching a middle-school math class to doing research with undergraduates—have changed the way that I would answer these questions. Some of the lessons I’ve learned have surprised me.
Using PageRank to Characterize Web Structure

Recent work on modeling the web graph has dwelt on capturing the degree distributions observed on the web. Pointing out that this represents a heavy reliance on "local" properties of the web graph, we study the distribution of PageRank values on the web. Our measurements suggest that PageRank values on the web follow a power law. We then develop generative models for the web graph that explain this observation and moreover remain faithful to previously studied degree distributions. We analyze these models and compare the analysis to both snapshots from the web and to graphs generated by simulations on the new models. To our knowledge this represents the first modeling of the web that goes beyond fitting degree distributions on the web.
Impact on cardiovascular disease events of the implementation of Argentina's national tobacco control law

Background Argentina's congress passed a tobacco control law that would enforce 100% smoke-free environments for the entire country, strong and pictorial health warnings on tobacco products and a comprehensive advertising ban. However, the Executive Branch continues to review the law and it has not been fully implemented. Our objective was to project the potential impact of full implementation of this tobacco control legislation on cardiovascular disease. Methods The Coronary Heart Disease (CHD) Policy Model was used to project future cardiovascular events. Data sources for the model included vital statistics, morbidity and mortality data, and tobacco use estimates from the National Risk Factor Survey. Estimated effectiveness of interventions was based on a literature review. Results were expressed as life-years, myocardial infarctions and strokes saved in an 8-year-period between 2012 and 2020. In addition we projected the incremental effectiveness on the same outcomes of a tobacco price increase not included in the law. Results In the period 2012–2020, 7500 CHD deaths, 16 900 myocardial infarctions and 4300 strokes could be avoided with the full implementation and enforcement of this law. Annual per cent reduction would be 3% for CHD deaths, 3% for myocardial infarctions and 1% for stroke. If a tobacco price increase is implemented the projected avoided CHD deaths, myocardial infarctions and strokes would be 15 500, 34 600 and 11 900, respectively. Conclusions Implementation of the tobacco control law would produce significant public health benefits in Argentina. Strong advocacy is needed at national and international levels to get this law implemented throughout Argentina.
Distributionally Robust Hospital Capacity Expansion Planning under Stochastic and Correlated Patient Demand

This paper investigates the optimal locations and capacities of hospital expansion facilities under uncertain future patient demands, considering both spatial and temporal correlations. We propose a novel two-stage distributionally robust optimization (DRO) model that integrates a Spatio-Temporal Neural Network (STNN). Specifically, we develop an STNN model that predicts future hospital occupancy levels considering spatial and temporal patterns in time-series datasets over a network of hospitals. The predictions of the STNN model are then used in the construction of the ambiguity set of the DRO model. To address computational challenges associated with two-stage DRO, we employ the linear-decision-rules technique to derive a tractable mixed-integer linear programming approximation. Extensive computational experiments conducted on real-world data demonstrate the superiority of the STNN model in minimizing forecast errors. Compared to neural network models built for each individual hospital, the proposed STNN model achieves a 53% improvement in average RMSE. Furthermore, the results demonstrate the value of incorporating spatiotemporal dependencies of demand uncertainty in the DRO model, as evidenced by out-of-sample analysis conducted with both ground truth data and under perfect information scenarios.
Meta-Learning for Fast Adaptation in Intent Inferral on a Robotic Hand Orthosis for Stroke

We propose MetaEMG, a meta-learning approach for fast adaptation in intent inferral on a robotic hand orthosis for stroke. One key challenge in machine learning for assistive and rehabilitative robotics with disabled-bodied subjects is the difficulty of collecting labeled training data. Muscle tone and spasticity often vary significantly among stroke subjects, and hand function can even change across different use sessions of the device for the same subject. We investigate the use of meta-learning to mitigate the burden of data collection needed to adapt high-capacity neural networks to a new session or subject. Our experiments on real clinical data collected from five stroke subjects show that MetaEMG can improve the intent inferral accuracy with a small session- or subject-specific dataset and very few fine-tuning epochs. To the best of our knowledge, we are the first to formulate intent inferral on stroke subjects as a meta-learning problem and demonstrate fast adaptation to a new session or subject for controlling a robotic hand orthosis with EMG signals.
Bounding Box Stability against Feature Dropout Reflects Detector Generalization across Environments

Bounding boxes uniquely characterize object detection, where a good detector gives accurate bounding boxes of categories of interest. However, in the real-world where test ground truths are not provided, it is non-trivial to find out whether bounding boxes are accurate, thus preventing us from assessing the detector generalization ability. In this work, we find under feature map dropout, good detectors tend to output bounding boxes whose locations do not change much, while bounding boxes of poor detectors will undergo noticeable position changes. We compute the box stability score (BoS score) to reflect this stability. Specifically, given an image, we compute a normal set of bounding boxes and a second set after feature map dropout. To obtain BoS score, we use bipartite matching to find the corresponding boxes between the two sets and compute the average Intersection over Union (IoU) across the entire test set. We contribute to finding that BoS score has a strong, positive correlation with detection accuracy measured by mean average precision (mAP) under various test environments. This relationship allows us to predict the accuracy of detectors on various real-world test sets without accessing test ground truths, verified on canonical detection tasks such as vehicle detection and pedestrian detection. Code and data are available at https://github.com/YangYangGirl/BoS.
The ability of hyperoxia to limit the effects of experimental detachment in cone-dominated retina.

PURPOSE To determine the ability of oxygen supplementation to ameliorate the effects of retinal detachment in a cone-dominated retina. METHODS Retinal detachments were created in the right eyes of ground squirrels and the animals immediately placed in normoxic (room air) or hyperoxic (70% oxygen) conditions for 3 days. The retinas were sampled from different regions and investigated morphologically or immunocytochemically by light or confocal microscopy. Agarose embedded sections were immunostained with antibody probes to cytochrome oxidase, synaptophysin, medium-to-long wavelength-sensitive (M/L) cone opsin, rod opsin, excitatory amino acid transporter 1 (EAAT1), glutamate synthetase (GS), cellular retinaldehyde-binding protein (CRALBP), and peanut agglutinin (PNA) lectin. Retinal wholemounts were labeled with PNA and antibodies to short (S)-wavelength-sensitive cone opsin and rod opsin. Cell death was examined using a TUNEL assay on agarose sections or using toluidine blue staining on semithin sections. RESULTS The percentage of dying cells relative to the total nuclei in the photoreceptor layer was significantly reduced, and the total number of nuclei was greater in hyperoxic animals. Triple labeling using TUNEL, anti-M/L cone opsin and anti-rod opsin showed that hyperoxia had a remarkable effect both on the reduction of cone cell death and the maintenance of the overall structure of cone photoreceptors. Analysis of the retinal wholemounts demonstrated the preservation of PNA, S-cone, and rod opsin antibody labeling in the detachments maintained in hyperoxic conditions. Although the disruption of cytochrome oxidase and synaptophysin was seen in normoxic animals, there was minimal disruption in hyperoxic animals. Labeling with anti-EAAT1, anti-GS, and anti-CRALBP was increased in the Müller cells of normoxic animals with detachments, but was decreased in the hyperoxic animals. CONCLUSIONS Hyperoxia prevents the degeneration of both rods and cones in retinas heavily dominated by cones and mitigates the effect of detachment on Müller cell reactivity. The current results suggest that the rescue of cones is not secondary to that of rods.
POLICEd RL: Learning Closed-Loop Robot Control Policies with Provable Satisfaction of Hard Constraints

In this paper, we seek to learn a robot policy guaranteed to satisfy state constraints. To encourage constraint satisfaction, existing RL algorithms typically rely on Constrained Markov Decision Processes and discourage constraint violations through reward shaping. However, such soft constraints cannot offer verifiable safety guarantees. To address this gap, we propose POLICEd RL, a novel RL algorithm explicitly designed to enforce affine hard constraints in closed-loop with a black-box environment. Our key insight is to force the learned policy to be affine around the unsafe set and use this affine region as a repulsive buffer to prevent trajectories from violating the constraint. We prove that such policies exist and guarantee constraint satisfaction. Our proposed framework is applicable to both systems with continuous and discrete state and action spaces and is agnostic to the choice of the RL training algorithm. Our results demonstrate the capacity of POLICEd RL to enforce hard constraints in robotic tasks while significantly outperforming existing methods.
Chain-of-Interaction: Enhancing Large Language Models for Psychiatric Behavior Understanding by Dyadic Contexts

Automatic coding patient behaviors is essential to support decision making for psychotherapists during the motivational interviewing (MI), a collaborative communication intervention approach to address psychiatric issues, such as alcohol and drug addiction. While the behavior coding task has rapidly adapted machine learning to predict patient states during the MI sessions, lacking of domain-specific knowledge and overlooking patient-therapist interactions are major challenges in developing and deploying those models in real practice. To encounter those challenges, we introduce the Chain-of-Interaction (CoI) prompting method aiming to contextualize large language models (LLMs) for psychiatric decision support by the dyadic interactions. The CoI prompting approach systematically breaks down the coding task into three key reasoning steps, extract patient engagement, learn therapist question strategies, and integrates dyadic interactions between patients and therapists. This approach enables large language models to leverage the coding scheme, patient state, and domain knowledge for patient behavioral coding. Experiments on real-world datasets can prove the effectiveness and flexibility of our prompting method with multiple state-of-the-art LLMs over existing prompting baselines. We have conducted extensive ablation analysis and demonstrate the critical role of dyadic interactions in applying LLMs for psychotherapy behavior understanding.
Unveiling the composition of the single-charm molecular pentaquarks: insights from M1 radiative decays and magnetic moments

In order to unravel the composition of the isoscalar $DN$, $D^*N$, $D_1N$, and $D_2^*N$ molecular pentaquarks, we carry out a systematic investigation of their M1 radiative decays and magnetic moment properties. Using the constituent quark model and taking into account the $S$-$D$ wave mixing effect and the coupled channel effect, our analysis yields numerical results indicating that the M1 radiative decays and the magnetic moment properties of these molecular pentaquarks provide some insights into their inner structures. These results can serve as crucial clues for distinguishing their spin-parity quantum numbers and configurations in experimental studies. In addition, we highlight the importance of the electromagnetic properties as key observables for elucidating the inner structures of the observed $\Lambda_c^{+} (2940)$ and $\Lambda_c^{+} (2910)$. We hope that these discoveries will inspire our experimental colleagues to further explore the family of the single-charm molecular pentaquarks and to investigate the inner structures of the $\Lambda_c^{+} (2940)$ and $\Lambda_c^{+} (2910)$ in future studies.
Gradings on associative triple systems of the second kind

On this work we study associative triple systems of the second kind. We show that for simple triple systems the automorphism group scheme is isomorphic to the automorphism group scheme of the $3$-graded associative algebra with involution constructed by Loos. This result will allow us to prove our main result which is a complete classification up to isomorphism of the gradings of structurable algebras.
The Limits of Identification in Discrete Choice

We study identification and linear independence in random utility models. We characterize the dimension of the random utility model as the cyclomatic complexity of a specific graphical representation of stochastic choice data. We show that, as the number of alternatives grows, any linearly independent set of preferences is a vanishingly small subset of the set of all preferences. We introduce a new condition on sets of preferences which is sufficient for linear independence. We demonstrate by example that the condition is not necessary, but is strictly weaker than other existing sufficient conditions.
CMP off-chip bandwidth scheduling guided by instruction criticality

This paper explores the benefits of scheduling off-chip memory operations in a Chip Multiprocessor (CMP) according to their execution relevance. Assuming the scenario of having many out-of-order execution cores in the CMP, from the processor perspective, the importance of the instruction that triggers an access to off-chip memory may vary considerably. Consequently, it makes sense to consider this point of view at the memory controller level to reorder outgoing memory accesses. After exploring different processor-centric sorting criteria, we reach the conclusion that the most simple and useful metric for scheduling a memory operation is the position in the reorder buffer of the instruction that triggers the on-chip miss. We propose a simple memory controller scheduling policy that employs this information as its main parameter. This proposal significantly improves system responsiveness, both in terms of throughput and fairness. The idea is analyzed through full-system simulation, running a broad set of workloads with diverse memory behavior. When it is compared with other scheduling algorithms with similar complexity, throughput can be improved by an average of 10% and fairness enhanced by an average of 15% even in very adverse usage scenarios. Moreover, the idea supports the possibility of dynamically favoring throughput or fairness, according to the end-user requirements.
Longitudinal changes in deep learning-estimated breast density and their impact on the risk of screen-detected breast cancer: The DeepJoint Algorithm

Mammography-based screening programs play a crucial role in reducing breast cancer mortality through early detection. Their efficacy is influenced by breast density, a dynamic factor that evolves over time, modifying breast cancer risk. Women with high breast density face increased breast cancer risk, coupled with reduced mammographic sensitivity. In this work, we present the DeepJoint algorithm, a pipeline for quantitative breast density assessment, investigating its association with breast cancer risk. First, we develop a lightweight deep-learning segmentation model that uses processed mammography images acquired from multiple manufacturers to assess quantitative breast density metrics such as dense area and percent density. Then, we fit a joint model to evaluate the association of these metrics with the time-to-breast cancer occurrence using an extensive database of 77,298 women participating in breast cancer screening in the United States. We demonstrate the impact of the current value and slope of the biomarker on breast cancer risk. We also derive individual and dynamic breast cancer risk predictions to describe how the individual longitudinal evolution of dense area and percent density impacts the risk of breast cancer. This innovative approach aligns with the growing interest in personalized risk monitoring during screening, offering valuable insights for improving breast cancer prevention strategies.
vid-TLDR: Training Free Token merging for Light-weight Video Transformer

Video Transformers have become the prevalent solution for various video downstream tasks with superior expressive power and flexibility. However, these video transformers suffer from heavy computational costs induced by the massive number of tokens across the entire video frames, which has been the major barrier to training the model. Further, the patches irrelevant to the main contents, e.g., backgrounds, degrade the generalization performance of models. To tackle these issues, we propose training free token merging for lightweight video Transformer (vid-TLDR) that aims to enhance the efficiency of video Transformers by merging the background tokens without additional training. For vid-TLDR, we introduce a novel approach to capture the salient regions in videos only with the attention map. Further, we introduce the saliency-aware token merging strategy by dropping the background tokens and sharpening the object scores. Our experiments show that vid-TLDR significantly mitigates the computational complexity of video Transformers while achieving competitive performance compared to the base model without vid-TLDR. Code is available at https://github.com/mlvlab/vid-TLDR.
Sloan School of Management Working Paper 4319-03 June 2003 Selfish Routing in Capacitated Networks

According to Wardrop’s first principle, agents in a congested network choose their routes selfishly, a behavior that is captured by the Nash equilibrium of the underlying noncooperative game. A Nash equilibrium does not optimize any global criterion per se, and so there is no apparent reason why it should be close to a solution of minimal total travel time, i.e. the system optimum. In this paper, we offer extensions of recent positive results on the efficiency of Nash equilibria in traffic networks. In contrast to prior work, we present results for networks with capacities and for latency functions that are nonconvex, nondifferentiable and even discontinuous. The inclusion of upper bounds on arc flows has early been recognized as an important means to provide a more accurate description of traffic flows. In this more general model, multiple Nash equilibria may exist and an arbitrary equilibrium does not need to be nearly efficient. Nonetheless, our main result shows that the best equilibrium is as efficient as in the model without capacities. Moreover, this holds true for broader classes of travel cost functions than considered hitherto.
Noise and light adaptation in rods of the macaque monkey

Membrane voltage was recorded in rod photoreceptors in retina isolated from macaque monkey. The size of the single photon response and the magnitude of membrane voltage fluctuations were assessed in dark- and light-adapted retina. The “dark light” rate ID, defined as the rate of spontaneous photopigment isomerizations that would produce a variance equivalent to that of the noise measured in the dark, was calculated after matched filtering. The average value of 0.08 s−1 fell at the higher end of psychophysical estimates of dark light in human observers. In light-adapted rods the photon response decreased in amplitude and duration, and the magnitude of the voltage fluctuations increased with increasing background light intensity. The signal-to-noise ratio (SNR) for single rods was defined as the ratio of the peak amplitude of the photon response to the standard deviation of the noise fluctuations. The signal-to-noise ratio for dark-adapted rods SNRD was about 7. With increasing background intensity I, the SNR fell as SNRD(1 + I/ID)−1/2. This function may account for the increment thresholds measured with small brief test flashes in human psychophysical experiments.