pwc-archive/papers-with-abstracts huggingface dataset:
A normative theory of social conflict
Date: 2023-03-07 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Bayesian Inference

Social hierarchy in animal groups carries a crucial adaptive function by reducing conflict and injury while protecting valuable group resources. Social hierarchy is dynamic and can be altered by social conflict, agonistic interactions, and aggression. Understanding social conflict and aggressive behavior is of profound importance to our society and welfare. In this study, we developed a quantitative theory of social conflict. We modeled individual agonistic interactions as a normal-form game between two agents. We assumed that the agents use Bayesian inference to update their beliefs about their strength or their opponent's strength and to derive optimal actions. We compared the results of our model to behavioral and whole-brain neural activity data obtained for a large (n=116) population of mice engaged in agonistic interactions. We find that both types of data are consistent with the first-level Theory of Mind model (1-ToM) in which mice form both "primary" beliefs about their and their opponent's strengths as well as the "secondary" beliefs about the beliefs of their opponents. Our model helps identify brain regions that carry information about these levels of beliefs. Overall, we both propose a model to describe agonistic interactions and support our quantitative results with behavioral and neural activity data.
Interactions between $β$-endorphin and kisspeptin neurons of the ewe arcuate nucleus are modulated by photoperiod
Authors: Vincent Hellier, Hugues Dardente, Didier Lomet, Juliette Cognié, Laurence Dufourny
Date: 2023-03-07 00:00:00
Tasks: ARC

Opioid peptides are well-known modulators of the central control of reproduction. Among them, dynorphin coexpressed in kisspeptin (KP) neurons of the arcuate nucleus (ARC) has been thoroughly studied for its autocrine effect on KP release through $\kappa$ opioid receptors. Other studies have suggested a role for $\beta$-endorphin (BEND), a peptide cleaved from the proopiomelanocortin (POMC) precursor, on food intake and central control of reproduction. Similarly to KP, BEND content in the ARC of sheep is modulated by day length and BEND modulates food intake in a dose-dependent manner. As KP levels in the ARC vary with photoperiodic and metabolic status, a photoperiod-driven influence of BEND neurons on neighboring KP neurons is plausible. The aim of this study was therefore to investigate a possible modulatory action of BEND on KP neurons located in the ovine ARC. Using confocal microscopy, numerous KP appositions on BEND neurons were found but there was no photoperiodic variation in the number of these interactions in ovariectomized, estradiol-replaced ewes. In contrast, BEND terminals on KP neurons were twice as numerous under short days (SD), in ewes having an activated gonadotropic axis, as compared to anestrus ewes under long days (LD). Injection of 5$\mu$g BEND into the third ventricle of SD ewes induced a significant and specific increase of activated KP neurons (16% versus 9% in controls) while the percentage of overall activated (c-Fos positive) neurons, was similar between both groups. These data suggest a photoperiod-dependent influence of BEND on KP neurons of the ARC, which may influence GnRH pulsatile secretion and inform KP neurons on the metabolic status.
A Dynamic Interference Model for Benham's Top
Authors: Yutaka Nishiyama
Date: 2023-01-25 00:00:00
Tasks: model

In 2023, I published a paper titled "A Dynamic Interference Model for Benham's Top." Here, I would like to concisely explain the most important aspect of dynamic interference by organizing and integrating the figures.
The Role of Contraction Mode in Determining Exercise Tolerance, Torque-Duration Relationship, and Neuromuscular Fatigue
Authors: Guillaume Ducrocq, Simon Al Assad, Nabil Kouzkouz, Thomas Hureau
Date: 2023-03-08 00:00:00

Purpose: Critical torque (CT) and work done above it (W') are key predictors of exercise performance associated with neuromuscular fatigue. The aim of the present study was to understand the role of the metabolic cost of exercise in determining exercise tolerance, CT and W' and the mechanisms of neuromuscular fatigue.Methods: Twelve subjects performed four knee extension time-trials (6, 8, 10, and 12-minutes) using eccentric, isometric, or concentric contractions (3 s-on/2 s-off at 90{\textdegree} or 30{\textdegree}/s) to modulate the metabolic cost of exercise. Exercise performance was quantified by total impulse and mean torque. CT and W' were determined using the linear relationship between total impulse and contraction time. Cardiometabolic, neuromuscular, and ventilatory responses were quantified. Neuromuscular function was evaluated by maximal voluntary contraction, resting potentiated single/doublet electrical stimulations, and superimposed single electrical stimulation to quantify neuromuscular, peripheral, and central fatigue, respectively.Results: Compared to isometric exercise, total impulse (+36 $\pm$ 21%; P < 0.001), CT (+27 $\pm$ 30%; P < 0.001), and W' (+67 $\pm$ 99%; P < 0.001) were increased during eccentric exercise whereas total impulse (-25 $\pm$ 7%; P < 0.001), critical torque (-26 $\pm$ 15%; P < 0.001) and W' (-18 $\pm$ 19%; P < 0.001) were reduced in concentric exercise. Conversely, the metabolic response and the degree of peripheral fatigue were reduced during eccentric exercise whereas they were increased during concentric exercise. CT was negatively associated with oxygen consumption gain (R2 = 0.636; P < 0.001) and W' was negatively associated with rates of neuromuscular and peripheral fatigue indices (R2 = [0.252-0.880]; P < 0.001).Conclusions: The contraction mode influenced both CT and W', and consequently exercise tolerance, indicating that the metabolic cost of contraction played a key role.
Optimal investment with insurable background risk and nonlinear portfolio allocation frictions
Authors: Hugo E. Ramirez, Rafael Serrano
Date: 2023-03-07 00:00:00
Tasks: Friction

We study investment and insurance demand decisions for an agent in a theoretical continuous-time expected utility maximization model that combines risky assets with an (exogenous) insurable background risk. This risk takes the form of a jump-diffusion process with negative jumps in the return rate of the (self-financed) wealth. The main distinctive feature of our model is that the agent's decision on portfolio choice and insurance demand causes nonlinear friction in the dynamics of the wealth process. We use the dynamic programming approach to find optimality conditions under which the agent assumes the insurable risk entirely, or partially, or purchases total insurance against it. In particular, we consider differential and piece-wise linear portfolio allocation frictions, with differential borrowing and lending rates as our most emblematic example. Finally, we present a mutual-fund separation result and illustrate our results with several numerical examples when the adverse jump risk has Beta distribution.
Understanding Natural Language Understanding Systems. A Critical Analysis
Authors: Alessandro Lenci
Date: 2023-03-01 00:00:00
Tasks: Natural Language Understanding

The development of machines that {\guillemotleft}talk like us{\guillemotright}, also known as Natural Language Understanding (NLU) systems, is the Holy Grail of Artificial Intelligence (AI), since language is the quintessence of human intelligence. The brief but intense life of NLU research in AI and Natural Language Processing (NLP) is full of ups and downs, with periods of high hopes that the Grail is finally within reach, typically followed by phases of equally deep despair and disillusion. But never has the trust that we can build {\guillemotleft}talking machines{\guillemotright} been stronger than the one engendered by the last generation of NLU systems. But is it gold all that glitters in AI? do state-of-the-art systems possess something comparable to the human knowledge of language? Are we at the dawn of a new era, in which the Grail is finally closer to us? In fact, the latest achievements of AI systems have sparkled, or better renewed, an intense scientific debate on their true language understanding capabilities. Some defend the idea that, yes, we are on the right track, despite the limits that computational models still show. Others are instead radically skeptic and even dismissal: The present limits are not just contingent and temporary problems of NLU systems, but the sign of the intrinsic inadequacy of the epistemological and technological paradigm grounding them. This paper aims at contributing to such debate by carrying out a critical analysis of the linguistic abilities of the most recent NLU systems. I contend that they incorporate important aspects of the way language is learnt and processed by humans, but at the same time they lack key interpretive and inferential skills that it is unlikely they can attain unless they are integrated with structured knowledge and the ability to exploit it for language use.
Map Projections Minimizing Distance Errors
Authors: J. Richard Gott III, Charles Mugnolo, Wesley N. Colley
Date: 2006-08-23 00:00:00

It is useful to have mathematical criteria for evaluating errors in map projections. The Chebyshev criterion for minimizing rms (root mean square) local scale factor errors for conformal maps has been useful in developing conformal map projections of continents. Any local error criterion will be minimized ultimately by map projections with multiple interruptions, on which some pairs of points that are close on the globe are far apart on the map. Since it is as bad to have two points on the map at two times their proper separation as to have them at half their proper separation, it is the rms logarithmic distance, s, between random points in the mapped region that we will minimize. The best previously known projection of the entire sphere for distances is the Lambert equal-area azimuthal with an rms logarithmic distance error of s=0.343. For comparison, the Mercator has s=0.444, and the Mollweide has s=0.390. We present new projections: the "Gott equal-area elliptical" with perfect shapes on the central meridian, the "Gott-Mugnolo equal-area elliptical" and the "Gott-Mugnolo azimuthal" with rms logarithmic distance errors of s=0.365, s=0.348, and s=0.341 respectively, which improve on previous projections of their type. The "Gott-Mugnolo azimuthal" has the lowest distance errors of any map and is produced by a new technique using "forces" between pairs of points on a map which make them move so as to minimize s. The "Gott equal-area elliptical" projection produces a particularly attractive map of Mars, and the "Gott-Mugnolo azimuthal" projection produces an interesting map of the moon.
Intention-based Long-Term Human Motion Anticipation
Authors: Julian Tanke, Chintan Zaveri, Juergen Gall
Date: 2021-12-01 00:00:00
Proceeding: 3DV 2021 12
Tasks: Diversity, Human Pose Forecasting

Recently, a few works have been proposed to model the
uncertainty of the future human motion. These works do
not forecast a single sequence but multiple sequences for
the same observation. While these works focused on in-
creasing the diversity, this work focuses on keeping a high
quality of the forecast sequences even for very long time
horizons of up to 30 seconds. In order to achieve this goal,
we propose to forecast the intention of the person ahead of
time. This has the advantage that the generated human mo-
tion remains goal oriented and that the motion transitions
between two actions are smooth and highly realistic. We
furthermore propose a new quality score for evaluation that
correlates better with human perception than other metrics.
The results and a user study show that our approach fore-
casts multiple sequences that are more plausible compared
to the state-of-the-art.
Rate Loss due to Beam Cusping in Grid of Beams
Authors: Krishan Kumar Tiwari, Giuseppe Caire
Date: 2022-11-07 00:00:00
Proceeding: IEEE VTC 2022 11

We present mean communication rate loss (MCRL) values due to the beam cusping phenomenon inherent to grid of beams based wireless systems, which are widely used/proposed in millimeter-wave and sub-THz bands. We consider array antenna elements with non-zero aperture and axisymmetric radiation pattern with a certain directivity, unlike the ideal isotropic antenna elements widely considered in theoretical papers on array processing and RF beamforming. The array antenna elements have a half power beam width of ninety degrees in this work which is typical of commonly used microstrip patch antenna elements. We perform Monte Carlo numerical experiments to obtain MCRL values for different spatial dimensions of multiple input multiple output (MIMO) systems, different angular fields-of view, and different beamforming codebook sizes. We show that increasing the beam grid density beyond a certain threshold does not help and therefore a certain cusping loss is unavoidable even for continuous beam steering with directive antenna elements. Further, we also show the quantitative impact on the MCRL values as the axisymmetric radiation pattern directivity of the modelled antenna element is increased.
Invertible Kernel PCA with Random Fourier Features
Authors: Daniel Gedon, Antôni H. Ribeiro, Niklas Wahlström, Thomas B. Schön
Date: 2023-03-09 00:00:00
Tasks: Denoising

Kernel principal component analysis (kPCA) is a widely studied method to construct a low-dimensional data representation after a nonlinear transformation. The prevailing method to reconstruct the original input signal from kPCA -- an important task for denoising -- requires us to solve a supervised learning problem. In this paper, we present an alternative method where the reconstruction follows naturally from the compression step. We first approximate the kernel with random Fourier features. Then, we exploit the fact that the nonlinear transformation is invertible in a certain subdomain. Hence, the name \emph{invertible kernel PCA (ikPCA)}. We experiment with different data modalities and show that ikPCA performs similarly to kPCA with supervised reconstruction on denoising tasks, making it a strong alternative.
LMR: A Large-Scale Multi-Reference Dataset for Reference-based Super-Resolution
Authors: Lin Zhang, Xin Li, Dongliang He, Errui Ding, Zhaoxiang Zhang
Date: 2023-03-09 00:00:00
Proceeding: ICCV 2023 1
Tasks: feature selection, Image Super-Resolution, Reference-based Super-Resolution, Super-Resolution

It is widely agreed that reference-based super-resolution (RefSR) achieves superior results by referring to similar high quality images, compared to single image super-resolution (SISR). Intuitively, the more references, the better performance. However, previous RefSR methods have all focused on single-reference image training, while multiple reference images are often available in testing or practical applications. The root cause of such training-testing mismatch is the absence of publicly available multi-reference SR training datasets, which greatly hinders research efforts on multi-reference super-resolution. To this end, we construct a large-scale, multi-reference super-resolution dataset, named LMR. It contains 112,142 groups of 300x300 training images, which is 10x of the existing largest RefSR dataset. The image size is also much larger. More importantly, each group is equipped with 5 reference images with different similarity levels. Furthermore, we propose a new baseline method for multi-reference super-resolution: MRefSR, including a Multi-Reference Attention Module (MAM) for feature fusion of an arbitrary number of reference images, and a Spatial Aware Filtering Module (SAFM) for the fused feature selection. The proposed MRefSR achieves significant improvements over state-of-the-art approaches on both quantitative and qualitative evaluations. Our code and data would be made available soon.
Fast Non-Local Neural Networks with Spectral Residual Learning
Authors: Lu Chi, Guiyu Tian, Yadong Mu, Lingxi Xie, Qi Tian
Date: 2019-10-15 00:00:00
Proceeding: MM '19: Proceedings of the 27th ACM International Conference on Multimedia 2019 10
Tasks: Pose Estimation, Video Classification

Effectively modeling long-range spatial correlation is crucial in context-sensitive visual computing tasks, such as human pose estimation and video classification. Enlarging receptive field is popularly adopted in building such non-local deep networks. However, current solutions, including dilation convolution or self-attention based operators, mostly suffer from either low computational efficacy or insufficient receptive field. This paper proposes spectral residual learning (SRL), a novel network architectural design for achieving fully global receptive field. A neural block that implements SRL has three key components: a local-to-global transform that projects some ordinary local features into a spectral domain, compiled operations in the spectral domain, and a global-to-local transform that converts all data back to the original local format. We show its equivalence to conducting residual learning in some spectral domain and carefully re-formulate a variety of neural layers into their spectral forms, such as ReLU or convolutions. The benefits of SRL is three-fold: first, all operations have global receptive field, namely any update affects all image positions. This can extract richer context information in various vision tasks; Secondly, the local-to-global / global-to-local transforms in SRL are defined by bi-linear unitary matrices, which is both computation and parameter economic; Lastly, SRL is a generic formulation, here instantiated by Fourier transform and real orthogonal matrix. We conduct comprehensive evaluations on two challenging tasks, including human pose estimation from images and video classification. All experiments clearly show performance improvement by large margins in comparison with conventional non-local network designs.
ARS-DETR: Aspect Ratio-Sensitive Detection Transformer for Aerial Oriented Object Detection
Authors: Ying Zeng, Yushi Chen, Xue Yang, Qingyun Li, Junchi Yan
Date: 2023-03-09 00:00:00
Tasks: Object, object-detection, Object Detection, Oriented Object Detection

Existing oriented object detection methods commonly use metric AP$_{50}$ to measure the performance of the model. We argue that AP$_{50}$ is inherently unsuitable for oriented object detection due to its large tolerance in angle deviation. Therefore, we advocate using high-precision metric, e.g. AP$_{75}$, to measure the performance of models. In this paper, we propose an Aspect Ratio Sensitive Oriented Object Detector with Transformer, termed ARS-DETR, which exhibits a competitive performance in high-precision oriented object detection. Specifically, a new angle classification method, calling Aspect Ratio aware Circle Smooth Label (AR-CSL), is proposed to smooth the angle label in a more reasonable way and discard the hyperparameter that introduced by previous work (e.g. CSL). Then, a rotated deformable attention module is designed to rotate the sampling points with the corresponding angles and eliminate the misalignment between region features and sampling points. Moreover, a dynamic weight coefficient according to the aspect ratio is adopted to calculate the angle loss. Comprehensive experiments on several challenging datasets show that our method achieves competitive performance on the high-precision oriented object detection task.
GPGait: Generalized Pose-based Gait Recognition
Authors: Yang Fu, Shibei Meng, Saihui Hou, Xuecai Hu, Yongzhen Huang
Date: 2023-03-09 00:00:00
Proceeding: ICCV 2023 1
Tasks: Gait Recognition

Recent works on pose-based gait recognition have demonstrated the potential of using such simple information to achieve results comparable to silhouette-based methods. However, the generalization ability of pose-based methods on different datasets is undesirably inferior to that of silhouette-based ones, which has received little attention but hinders the application of these methods in real-world scenarios. To improve the generalization ability of pose-based methods across datasets, we propose a \textbf{G}eneralized \textbf{P}ose-based \textbf{Gait} recognition (\textbf{GPGait}) framework. First, a Human-Oriented Transformation (HOT) and a series of Human-Oriented Descriptors (HOD) are proposed to obtain a unified pose representation with discriminative multi-features. Then, given the slight variations in the unified representation after HOT and HOD, it becomes crucial for the network to extract local-global relationships between the keypoints. To this end, a Part-Aware Graph Convolutional Network (PAGCN) is proposed to enable efficient graph partition and local-global spatial feature extraction. Experiments on four public gait recognition datasets, CASIA-B, OUMVLP-Pose, Gait3D and GREW, show that our model demonstrates better and more stable cross-domain capabilities compared to existing skeleton-based methods, achieving comparable recognition results to silhouette-based ones. Code is available at https://github.com/BNU-IVC/FastPoseGait.
RMMDet: Road-Side Multitype and Multigroup Sensor Detection System for Autonomous Driving
Authors: Xiuyu Yang, Zhuangyan Zhang, Haikuo Du, Sui Yang, Fengping Sun, Yanbo Liu, Ling Pei, Wenchao Xu, Weiqi Sun, Zhengyu Li
Date: 2023-03-09 00:00:00
Tasks: Autonomous Driving, Diversity, Sand, Scheduling

Autonomous driving has now made great strides thanks to artificial intelligence, and numerous advanced methods have been proposed for vehicle end target detection, including single sensor or multi sensor detection methods. However, the complexity and diversity of real traffic situations necessitate an examination of how to use these methods in real road conditions. In this paper, we propose RMMDet, a road-side multitype and multigroup sensor detection system for autonomous driving. We use a ROS-based virtual environment to simulate real-world conditions, in particular the physical and functional construction of the sensors. Then we implement muti-type sensor detection and multi-group sensors fusion in this environment, including camera-radar and camera-lidar detection based on result-level fusion. We produce local datasets and real sand table field, and conduct various experiments. Furthermore, we link a multi-agent collaborative scheduling system to the fusion detection system. Hence, the whole roadside detection system is formed by roadside perception, fusion detection, and scheduling planning. Through the experiments, it can be seen that RMMDet system we built plays an important role in vehicle-road collaboration and its optimization. The code and supplementary materials can be found at: https://github.com/OrangeSodahub/RMMDet