Miracle-dz/UniFAR-FTUs huggingface dataset:
CycleACR: Cycle Modeling of Actor-Context Relations for Video Action Detection

The relation modeling between actors and scene context advances video action detection where the correlation of multiple actors makes their action recognition challenging. Existing studies model each actor and scene relation to improve action recognition. However, the scene variations and background interference limit the effectiveness of this relation modeling. In this paper, we propose to select actor-related scene context, rather than directly leverage raw video scenario, to improve relation modeling. We develop a Cycle Actor-Context Relation network (CycleACR) where there is a symmetric graph that models the actor and context relations in a bidirectional form. Our CycleACR consists of the Actor-to-Context Reorganization (A2C-R) that collects actor features for context feature reorganizations, and the Context-to-Actor Enhancement (C2A-E) that dynamically utilizes reorganized context features for actor feature enhancement. Compared to existing designs that focus on C2A-E, our CycleACR introduces A2C-R for a more effective relation modeling. This modeling advances our CycleACR to achieve state-of-the-art performance on two popular action detection datasets (i.e., AVA and UCF101-24). We also provide ablation studies and visualizations as well to show how our cycle actor-context relation modeling improves video action detection. Code is available at https://github.com/MCG-NJU/CycleACR.
Measuring Categorical Perception in Color-Coded Scatterplots

Scatterplots commonly use color to encode categorical data. However, as datasets increase in size and complexity, the efficacy of these channels may vary. Designers lack insight into how robust different design choices are to variations in category numbers. This paper presents a crowdsourced experiment measuring how the number of categories and choice of color encodings used in multiclass scatterplots influences the viewers’ abilities to analyze data across classes. Participants estimated relative means in a series of scatterplots with 2 to 10 categories encoded using ten color palettes drawn from popular design tools. Our results show that the number of categories and color discriminability within a color palette notably impact people’s perception of categorical data in scatterplots and that the judgments become harder as the number of categories grows. We examine existing palette design heuristics in light of our results to help designers make robust color choices informed by the parameters of their data.
LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention

We present LLaMA-Adapter, a lightweight adaption method to efficiently fine-tune LLaMA into an instruction-following model. Using 52K self-instruct demonstrations, LLaMA-Adapter only introduces 1.2M learnable parameters upon the frozen LLaMA 7B model, and costs less than one hour for fine-tuning on 8 A100 GPUs. Specifically, we adopt a set of learnable adaption prompts, and prepend them to the word tokens at higher transformer layers. Then, a zero-initialized attention mechanism with zero gating is proposed, which adaptively injects the new instructional cues into LLaMA, while effectively preserves its pre-trained knowledge. With our efficient training, LLaMA-Adapter can generate high-quality responses, comparable to Alpaca with fully fine-tuned 7B parameters. Besides language commands, our approach can be simply extended to multi-modal instructions for learning image-conditioned LLaMA model, which achieves superior reasoning performance on ScienceQA and COCO Caption benchmarks. Furthermore, we also evaluate the zero-initialized attention mechanism for fine-tuning other pre-trained models (ViT, RoBERTa) on traditional vision and language tasks, demonstrating the superior generalization capacity of our approach. Code is released at https://github.com/OpenGVLab/LLaMA-Adapter.
Multi-Behavior Recommendation with Cascading Graph Convolution Networks

Multi-behavior recommendation, which exploits auxiliary behaviors (e.g., click and cart) to help predict users’ potential interactions on the target behavior (e.g., buy), is regarded as an effective way to alleviate the data sparsity or cold-start issues in recommendation. Multi-behaviors are often taken in certain orders in real-world applications (e.g., click>cart>buy). In a behavior chain, a latter behavior usually exhibits a stronger signal of user preference than the former one does. Most existing multi-behavior models fail to capture such dependencies in a behavior chain for embedding learning. In this work, we propose a novel multi-behavior recommendation model with cascading graph convolution networks (named MB-CGCN). In MB-CGCN, the embeddings learned from one behavior are used as the input features for the next behavior’s embedding learning after a feature transformation operation. In this way, our model explicitly utilizes the behavior dependencies in embedding learning. Experiments on two benchmark datasets demonstrate the effectiveness of our model on exploiting multi-behavior data. It outperforms the best baseline by 33.7% and 35.9% on average over the two datasets in terms of Recall@10 and NDCG@10, respectively.
Efficient Quality Diversity Optimization of 3D Buildings through 2D Pre-Optimization

Abstract Quality diversity algorithms can be used to efficiently create a diverse set of solutions to inform engineers' intuition. But quality diversity is not efficient in very expensive problems, needing hundreds of thousands of evaluations. Even with the assistance of surrogate models, quality diversity needs hundreds or even thousands of evaluations, which can make its use infeasible. In this study, we try to tackle this problem by using a pre-optimization strategy on a lower-dimensional optimization problem and then map the solutions to a higher-dimensional case. For a use case to design buildings that minimize wind nuisance, we show that we can predict flow features around 3D buildings from 2D flow features around building footprints. For a diverse set of building designs, by sampling the space of 2D footprints with a quality diversity algorithm, a predictive model can be trained that is more accurate than when trained on a set of footprints that were selected with a space-filling algorithm like the Sobol sequence. Simulating only 16 buildings in 3D, a set of 1,024 building designs with low predicted wind nuisance is created. We show that we can produce better machine learning models by producing training data with quality diversity instead of using common sampling techniques. The method can bootstrap generative design in a computationally expensive 3D domain and allow engineers to sweep the design space, understanding wind nuisance in early design phases.
Mean-variance hybrid portfolio optimization with quantile-based risk measure

This paper addresses the importance of incorporating various risk measures in portfolio management and proposes a dynamic hybrid portfolio optimization model that combines the spectral risk measure and the Value-at-Risk in the mean-variance formulation. By utilizing the quantile optimization technique and martingale representation, we offer a solution framework for these issues and also develop a closed-form portfolio policy when all market parameters are deterministic. Our hybrid model outperforms the classical continuous-time mean-variance portfolio policy by allocating a higher position of the risky asset in favorable market states and a less risky asset in unfavorable market states. This desirable property leads to promising numerical experiment results, including improved Sortino ratio and reduced downside risk compared to the benchmark models.
Randomly Initialized Subnetworks with Iterative Weight Recycling

The Multi-Prize Lottery Ticket Hypothesis posits that randomly initialized neural networks contain several subnetworks that achieve comparable accuracy to fully trained models of the same architecture. However, current methods require that the network is sufficiently overparameterized. In this work, we propose a modification to two state-of-the-art algorithms (Edge-Popup and Biprop) that finds high-accuracy subnetworks with no additional storage cost or scaling. The algorithm, Iterative Weight Recycling, identifies subsets of important weights within a randomly initialized network for intra-layer reuse. Empirically we show improvements on smaller network architectures and higher prune rates, finding that model sparsity can be increased through the"recycling"of existing weights. In addition to Iterative Weight Recycling, we complement the Multi-Prize Lottery Ticket Hypothesis with a reciprocal finding: high-accuracy, randomly initialized subnetwork's produce diverse masks, despite being generated with the same hyperparameter's and pruning strategy. We explore the landscapes of these masks, which show high variability.
Characterizing the Performance of Emerging Deep Learning, Graph, and High Performance Computing Workloads Under Interference

Throughput-oriented computing via co-running multiple applications in the same machine has been widely adopted to achieve high hardware utilization and energy saving on modern supercomputers and data centers. However, efficiently co-running applications raises new design challenges, mainly because applications with diverse requirements can stress out shared hardware resources (IO, Network and Cache) at various levels. The disparities in resource usage can result in interference, which in turn can lead to unpredictable co-running behaviors. To better understand application interference, prior work provided detailed execution characterization. However, these characterization approaches either emphasize on traditional benchmarks or fall into a single application domain. To address this issue, we study 25 up-to-date applications and benchmarks from various application domains and form 625 consolidation pairs to thoroughly analyze the execution interference caused by application co-running. Moreover, we leverage mini-benchmarks and real applications to pinpoint the provenance of co-running interference in both hardware and software aspects.
TOI-5375 B: A Very Low Mass Star at the Hydrogen-burning Limit Orbiting an Early M-type Star

The Transiting Exoplanet Survey Satellite (TESS) mission detected a companion orbiting TIC 71268730, categorized it as a planet candidate, and designated the system TOI-5375. Our follow-up analysis using radial-velocity data from the Habitable-zone Planet Finder, photometric data from Red Buttes Observatory, and speckle imaging with NN-EXPLORE Exoplanet Stellar Speckle Imager determined that the companion is a very low mass star near the hydrogen-burning mass limit with a mass of 0.080 ± 0.002M ☉ (83.81 ± 2.10M J ), a radius of 0.1114−0.0050+0.0048R☉ (1.0841 0.04870.0467RJ ), and brightness temperature of 2600 ± 70 K. This object orbits with a period of 1.721553 ± 0.000001 days around an early M dwarf star (0.62 ± 0.016M ☉). TESS photometry shows regular variations in the host star’s TESS light curve, which we interpreted as an activity-induced variation of ∼2%, and used this variability to measure the host star’s stellar rotation period of 1.9716−0.0083+0.0080 days. The TOI-5375 system provides tight constraints on stellar models of low-mass stars at the hydrogen-burning limit and adds to the population in this important region.
Scale-invariant inflation

We examine a scalar-tensor model of gravity that is globally scale-invariant. When adapted to a spatially flat Robertson-Walker metric, the equations of motion describe a dynamical system that flows from an unstable de Sitter space to a stable one. We show that during this transition inflation can occur. Moreover, at the final fixed point, a mass scale naturally emerges that can be identified with the Planck mass. We compute the inflationary spectral indices and the tensor perturbation and we compare them with observations. We also study the possibility that primordial magnetic fields are generated during inflation.
RESVECH 2.0: cross-cultural adaptation for Brazil, reliability and validity for the evaluation of venous ulcers

ABSTRACT Objectives: to cross-culturally adapt the scale Resultados en la valoración y evolución de la cicatrización de las heridas - RESVECH 2.0 for Brazilian Portuguese; to estimate the internal consistency and construct and criterion validity of the scale in the evaluation of venous ulcers. Methods: methodological study, based on international guidelines for studies of this type. Wounds were evaluated using the RESVECH 2.0 and Pressure Ulcer Scale of Healing 3.0 (PUSH). Descriptive analysis, confirmatory factor analysis, Cronbach’s alpha and Spearman’s correlation (p<0.05) were used. Results: 12 nurses and 77 people with 153 venous ulcers participated in the study. The translation was successful, the proposed factor model was validated, and Cronbach ‘s alpha = 0.832 (95%CI, 0.780-0.880) and correlation coefficient (RESVECH 2.0 and PUSH 3.0) = 0.74 were obtained. Conclusions: the adaptation of RESVECH 2.0 to Brazilian Portuguese is robust. Reliability and validity show compatibility for use in the country in the evaluation of venous ulcers.
CREPES: Cooperative RElative Pose Estimation System

Mutual localization plays a crucial role in multi-robot cooperation. CREPES, a novel system that focuses on six degrees of freedom (DOF) relative pose estimation for multi-robot systems, is proposed in this paper. CREPES has a compact hardware design using active infrared (IR) LEDs, an IR fish-eye camera, an ultra-wideband (UWB) module and an inertial measurement unit (IMU). By leveraging IR light communication, the system solves data association between visual detection and UWB ranging. Ranging measurements from the UWB and directional information from the camera offer relative 3-DOF position estimation. Combining the mutual relative position with neighbors and the gravity constraints provided by IMUs, we can estimate the 6-DOF relative pose from a single frame of sensor measurements. In addition, we design an estimator based on the error-state Kalman filter (ESKF) to enhance system accuracy and robustness. When multiple neighbors are available, a Pose Graph Optimization (PGO) algorithm is applied to further improve system accuracy. We conduct enormous experiments to demonstrate CREPES’ accuracy between robot pairs and a team of robots, as well as performance under challenging conditions.
A General Unfolding Speech Enhancement Method Motivated by Taylor's Theorem

While deep neural networks have facilitated significant advancements in the field of speech enhancement, most existing methods are developed following either empirical or relatively blind criteria, lacking adequate guidelines in pipeline design. Inspired by Taylor's theorem, we propose a general unfolding framework for both single- and multi-channel speech enhancement tasks. Concretely, we formulate the complex spectrum recovery into the spectral magnitude mapping in the neighborhood space of the noisy mixture, in which an unknown sparse term is introduced and applied for phase modification in advance. Based on that, the mapping function is decomposed into the superimposition of the 0th-order and high-order polynomials in Taylor's series, where the former coarsely removes the interference in the magnitude domain and the latter progressively complements the remaining spectral detail in the complex spectrum domain. In addition, we study the relation between adjacent order terms and reveal that each high-order term can be recursively estimated with its lower-order term, and each high-order term is then proposed to evaluate using a surrogate function with trainable weights, so that the whole system can be trained in an end-to-end manner. Given that the proposed framework is devised with the motivation of Taylor's theorem, it possesses improved internal flexibility. Extensive experiments are conducted on WSJ0-SI84, DNS-Challenge, Voicebank+Demand, spatialized Librispeech, and L3DAS22 multi-channel speech enhancement challenge datasets. Quantitative results show that the proposed approach yields competitive performance over existing top-performing approaches in terms of multiple objective metrics.
An improved fast radiative transfer model for assimilation of satellite radiance observations

To assimilate atmospheric and surface radiance measurements from satellites in a numerical weather prediction model, a fast radiative transfer model is required to compute radiances from the model first guess fields at every observation point. Such a model for satellite infrared and microwave radiance measurements is used operationally for the assimilation of TIROS operational vertical sounder radiances at the European Centre for Medium‐Range Weather Forecasts. An improved version of this model has been developed which requires ozone, in addition to temperature and water vapour, in the input profile and it has been generalized to compute radiances for other satellite radiometers using the same code. Instruments such as the high resolution infrared radiation sounder and the advanced microwave sounding unit on the National Oceanic and Atmospheric Administration polar orbiters, the METEOSAT water vapour imager and the Geostationary Operational Environmental Satellite infrared sounder have been simulated. It is demonstrated, by comparisons with line‐by‐line model computed radiances, that the fast model can reproduce the line‐by‐line model radiances for the TIROS operational vertical sounder stratospheric temperature sounding channels to an accuracy below the instrumental noise. The tropospheric, surface sensing, water vapour and ozone channel radiances cannot be predicted to such an accuracy, but still accurately enough for numerical weather prediction assimilation. A comparison of measured TIROS operational vertical sounder radiances with predicted values from numerical weather prediction model analyses gives larger differences than would be expected from the combination of the fast model and instrument related errors for most channels. The validity of the tangent linear approximation of the model gradient for typical radiance departures is also explored, with several examples, for the high resolution infrared radiation sounder/advanced microwave sounding unit instrument combination. The tangent‐linear approximation is valid for temperature but significant departures from linearity about the first guess profile are observed for water vapour and ozone. Cloud affected infrared radiances have a highly non‐linear response.
SK and Kv4 Channels Limit Spike Timing Perturbations in Pacemaking Dopamine Neurons

Abstract Midbrain dopamine (DA) neurons are among the best characterized pacemaker neurons, having intrinsic, rhythmic firing activity even in the absence of synaptic input. However, the mechanisms of DA neuron pacemaking have not been systematically related to how these cells respond to synaptic input. The input–output properties of pacemaking neurons can be characterized by the phase-resetting curve (PRC), which describes the sensitivity of interspike interval (ISI) length to inputs arriving at different phases of the firing cycle. Here we determined PRCs of putative DA neurons in the substantia nigra pars compacta in brain slices from male and female mice using gramicidin-perforated current-clamp recordings with electrical noise stimuli applied through the patch pipette. On average, and compared with nearby putative GABA neurons, DA neurons showed a low, nearly constant level of sensitivity across most of the ISI, but individual cells had PRCs showing relatively greater sensitivity at early or late phases. Pharmacological experiments showed that DA neuron PRCs are shaped by small-conductance calcium-activated potassium and Kv4 channels, which limit input sensitivity across early and late phases of the ISI. Our results establish the PRC as a tractable experimental measurement of individual DA neuron input–output relationships and identify two of the major ionic conductances that limit perturbations to rhythmic firing. These findings have applications in modeling and for identifying biophysical changes in response to disease or environmental manipulations.
Circadian rhythm and aneurysmal subarachnoid hemorrhage: Is there an alarm clock for the rupture timing?

Data on the temporal distribution of the bleeding time of intracranial aneurysms are limited to a few small studies. With this study, the aim was to analyze time patterns of the occurrence of aneurysmal subarachnoid hemorrhage (SAH), particularly focusing on the impact of patients' socio‐demographic and clinical characteristics on the ictus timing.
Porcine Enteric Alphacoronavirus Entry through Multiple Pathways (Caveolae, Clathrin, and Macropinocytosis) Requires Rab GTPases for Endosomal Transport

Emerging and reemerging coronaviruses cause severe human and animal epidemics worldwide. PEAV is the first bat-like coronavirus to cause infection in domestic animals. ABSTRACT Porcine enteric alphacoronavirus (PEAV) is a new bat HKU2-like porcine coronavirus, and its endemic outbreak has caused severe economic losses to the pig industry. Its broad cellular tropism suggests a potential risk of cross-species transmission. A limited understanding of PEAV entry mechanisms may hinder a rapid response to potential outbreaks. This study analyzed PEAV entry events using chemical inhibitors, RNA interference, and dominant-negative mutants. PEAV entry into Vero cells depended on three endocytic pathways: caveolae, clathrin, and macropinocytosis. Endocytosis requires dynamin, cholesterol, and a low pH. Rab5, Rab7, and Rab9 GTPases (but not Rab11) regulate PEAV endocytosis. PEAV particles colocalize with EEA1, Rab5, Rab7, Rab9, and Lamp-1, suggesting that PEAV translocates into early endosomes after internalization, and Rab5, Rab7, and Rab9 regulate trafficking to lysosomes before viral genome release. PEAV enters porcine intestinal cells (IPI-2I) through the same endocytic pathway, suggesting that PEAV may enter various cells through multiple endocytic pathways. This study provides new insights into the PEAV life cycle. IMPORTANCE Emerging and reemerging coronaviruses cause severe human and animal epidemics worldwide. PEAV is the first bat-like coronavirus to cause infection in domestic animals. However, the PEAV entry mechanism into host cells remains unknown. This study demonstrates that PEAV enters into Vero or IPI-2I cells through caveola/clathrin-mediated endocytosis and macropinocytosis, which does not require a specific receptor. Subsequently, Rab5, Rab7, and Rab9 regulate PEAV trafficking from early endosomes to lysosomes, which is pH dependent. The results advance our understanding of the disease and help to develop potential new drug targets against PEAV.