Miracle-dz/UniFAR-FTUs huggingface dataset:
RadarFormer: Lightweight and Accurate Real-Time Radar Object Detection Model

The performance of perception systems developed for autonomous driving vehicles has seen significant improvements over the last few years. This improvement was associated with the increasing use of LiDAR sensors and point cloud data to facilitate the task of object detection and recognition in autonomous driving. However, LiDAR and camera systems show deteriorating performances when used in unfavorable conditions like dusty and rainy weather. Radars on the other hand operate on relatively longer wavelengths which allows for much more robust measurements in these conditions. Despite that, radar-centric data sets do not get a lot of attention in the development of deep learning techniques for radar perception. In this work, we consider the radar object detection problem, in which the radar frequency data is the only input into the detection framework. We further investigate the challenges of using radar-only data in deep learning models. We propose a transformers-based model, named RadarFormer, that utilizes state-of-the-art developments in vision deep learning. Our model also introduces a channel-chirp-time merging module that reduces the size and complexity of our models by more than 10 times without compromising accuracy. Comprehensive experiments on the CRUW radar dataset demonstrate the advantages of the proposed method. Our RadarFormer performs favorably against the state-of-the-art methods while being 2x faster during inference and requiring only one-tenth of their model parameters. The code associated with this paper is available at https://github.com/YahiDar/RadarFormer.
Wave Equation with p(x,t) - Laplacian and Damping Term: Existence and Blow-up

{( ) u(x; 0) = u0(x); ut(x; 0) = u1(x); x ∈ Ω; u| T = 0; ΓT = @Ω: (1) The coefficients a(x;t); (x;t); b(x;t); exponents p(x;t); (x;t) and the source term f (x;t) are given functions of their arguments satisfying 0 < a ≤ a(x;t) ≤ a+ < ∞; 0 < ≤ (x;t) ≤ + < ∞; |b(x;t)| ≤ b+ < ∞; (2) 1 < p ≤ p(x;t) ≤ p+ < ∞; 1 < ≤ (x;t) ≤ + < ∞; (3) f ∈ L 2 (QT ); u1 ∈ L 2 (Ω); u0 ∈ L 2 (Ω) ∩ L ( ;0) (Ω) ∩ W 1;p( ;0) (Ω): (4) Problem (1) appears in models of nonlinear viscoelasticity. The local and global existence theorems and blow- up effects for solutions hyperbolic equations of the type (1) with constant exponents of nonlinearity have been studied in many papers , (see, e.g., [5]). However, only papers [6, 7] are devoted to the study of hyperbolic equations of the type (1) with variable nonlinearities. In the present communication, we discuss how the variable character of nonlinearity influences the existence and blow-up theory for the EDPs of the type (1). The analysis is based on the methods developed in [1]-[4].
K\"ahler-Einstein metrics on families of Fano varieties

We provide an analytic proof of the openness of the existence of unique K\"ahler-Einstein metrics and establish uniform a priori estimates on the K\"ahler-Einstein potentials along degenerate families of $\mathbb{Q}$-Fano varieties. Moreover, we show that these K\"ahler-Einstein currents vary continuously, and we prove uniform Moser-Trudinger inequalities. The core of the article regards a notion of convergence of quasi-plurisubharmonic functions in families of normal K\"ahler varieties that we introduce and study here. We show that the Monge-Amp\`ere energy is upper semi-continuous with respect to this convergence, and we establish a Demailly-Koll\'ar result for functions with full Monge-Amp\`ere mass.
CoMaL: Conditional Maximum Likelihood Approach to Self-supervised Domain Adaptation in Long-tail Semantic Segmentation

The research in self-supervised domain adaptation in semantic segmentation has recently received considerable attention. Although GAN-based methods have become one of the most popular approaches to domain adaptation, they have suffered from some limitations. They are insufficient to model both global and local structures of a given image, especially in small regions of tail classes. Moreover, they perform bad on the tail classes containing limited number of pixels or less training samples. In order to address these issues, we present a new self-supervised domain adaptation approach to tackle long-tail semantic segmentation in this paper. Firstly, a new metric is introduced to formulate long-tail domain adaptation in the segmentation problem. Secondly, a new Conditional Maximum Likelihood (CoMaL) approach in an autoregressive framework is presented to solve the problem of long-tail domain adaptation. Although other segmentation methods work under the pixel independence assumption, the long-tailed pixel distributions in CoMaL are generally solved in the context of structural dependency, as that is more realistic. Finally, the proposed method is evaluated on popular large-scale semantic segmentation benchmarks, i.e.,"SYNTHIA to Cityscapes"and"GTA to Cityscapes", and outperforms the prior methods by a large margin in both the standard and the proposed evaluation protocols.
Self-concept clarity and processing self-relevant information: An event-related potential study

ABSTRACT Self-concept clarity (SCC) refers to the extent to which self-beliefs are clearly and confidently defined, internally consistent, and stable. While there is an abundance of research showing an association between SCC, well-being, and effective self-regulation, there is little knowledge about how SCC relates to basic cognitive processes such as attention and memory. Drawing on the attentional function theory of cognitive control, we hypothesized that low SCC is associated with greater attentional control during a trait assessment task. We also expected that low SCC individuals retrieve self-related information from semantic memory less efficiently compared to high SCC individuals. Fifty participants took part in the ERP study. The P300 and N400 components were measured as electrophysiological indices of attentional and semantic processing. The results showed that individuals with low SCC had larger P300 amplitude in response to positive versus negative words, and marginally larger P300 amplitude in response to positive words compared to high SCC individuals. These results suggest greater attentional involvement in the processing of positive self-related information in people with low SCC. There were no significant differences between groups in N400 amplitude. The results are discussed in the context of the relationship of SCC to self-esteem and self-motive theory.
Spatial-Domain Transformations: Something Old and Something New

Summary Gravity and magnetic transformations can be computed either in the wavenumber domain via a use of the Fourier Transform or in the spatial domain. In this work, I explain operational differences between the two types of transformations and explore their pros and cons. In particular, I apply to gravity and magnetic data some spatial-domain transformations that were originally developed in image processing and seismic interpretation.
A study on Prompt Design, Advantages and Limitations of ChatGPT for Deep Learning Program Repair

ChatGPT has revolutionized many research and industrial fields. ChatGPT has shown great potential in software engineering to boost various traditional tasks such as program repair, code understanding, and code generation. However, whether automatic program repair (APR) applies to deep learning (DL) programs is still unknown. DL programs, whose decision logic is not explicitly encoded in the source code, have posed unique challenges to APR. While to repair DL programs, an APR approach needs to not only parse the source code syntactically but also needs to understand the code intention. With the best prior work, the performance of fault localization is still far less than satisfactory (only about 30\%). Therefore, in this paper, we explore ChatGPT's capability for DL program repair by asking three research questions. (1) Can ChatGPT debug DL programs effectively? (2) How can ChatGPT's repair performance be improved by prompting? (3) In which way can dialogue help facilitate the repair? On top of that, we categorize the common aspects useful for prompt design for DL program repair. Also, we propose various prompt templates to facilitate the performance and summarize the advantages and disadvantages of ChatGPT's abilities such as detecting bad code smell, code refactoring, and detecting API misuse/deprecation.
Identity Encoder for Personalized Diffusion

Many applications can benefit from personalized image generation models, including image enhancement, video conferences, just to name a few. Existing works achieved personalization by fine-tuning one model for each person. While being successful, this approach incurs additional computation and storage overhead for each new identity. Furthermore, it usually expects tens or hundreds of examples per identity to achieve the best performance. To overcome these challenges, we propose an encoder-based approach for personalization. We learn an identity encoder which can extract an identity representation from a set of reference images of a subject, together with a diffusion generator that can generate new images of the subject conditioned on the identity representation. Once being trained, the model can be used to generate images of arbitrary identities given a few examples even if the model hasn't been trained on the identity. Our approach greatly reduces the overhead for personalized image generation and is more applicable in many potential applications. Empirical results show that our approach consistently outperforms existing fine-tuning based approach in both image generation and reconstruction, and the outputs is preferred by users more than 95% of the time compared with the best performing baseline.
The Impact of Frame-Dropping on Performance and Energy Consumption for Multi-Object Tracking

The safety of automated vehicles (AVs) relies on the representation of their environment. Consequently, state-of-the-art AVs employ potent sensor systems to achieve the best possible environment representation at all times. Although these high-performing systems achieve impressive results, they induce significant requirements for the processing capabilities of an AV’s computational hardware components and their energy consumption.To enable a dynamic adaptation of such perception systems based on the situational perception requirements, we introduce a model-agnostic method for the scalable employment of single-frame object detection models using frame-dropping in tracking-by-detection systems. We evaluate our approach on the KITTI 3D Tracking Benchmark, showing that significant energy savings can be achieved at acceptable performance degradation, reaching up to 28% reduction of energy consumption at a performance decline of 6.6% in HOTA score.
Identifying Wisdom (of the Crowd): A Regression Approach

Experts in a population hold (a) beliefs over a state (call these state beliefs), as well as (b) beliefs over the distribution of beliefs in the population (call these hypothetical beliefs). If these are generated via updating a common prior using a fixed information structure, then the information structure can (generically) be derived by regressing hypothetical beliefs on state beliefs, provided there are at least as many signals as states. In addition, the prior solves an eigenvector equation derived from a matrix determined by the state beliefs and the hypothetical beliefs. Thus, the ex-ante informational environment (i.e., how signals are generated) can be determined using ex-post data (i.e., the beliefs in the population). I discuss implications of this finding, as well as what is identified when there are more states than signals.
Detection of magnetic galactic binaries in quasi-circular orbit with LISA

Laser Interferometer Space Antenna (LISA) will observe gravitational waves from galactic binaries (GBs) of white dwarfs or neutron stars. Some of these objects are among the most magnetic astrophysical objects in the Universe. Magnetism, by secularly disrupting the orbit, can eventually affect the gravitational waves emission and could then be potentially detected and characterized after several years of observations by LISA. Currently, the data processing pipeline of the LISA Data Challenge (LDC) for GBs does not consider either magnetism or eccentricity. Recently, it was shown [Bourgoin et al. PRD 105, 124042 (2022)] that magnetism induces a shift on the gravitational wave frequencies. Additionally, it was argued that, if the binary's orbit is eccentric, the presence of magnetism could be detected by LISA. In this work, we explore the consequences of a future data analysis conducted on quasi-circular and magnetic GB systems using the current LDC tools. We first show that a single eccentric GB can be interpreted as several GBs and this can eventually bias population studies deduced from LISA's future catalog. Then, we confirm that for quasi-circular orbits, the secular magnetic energy of the system can be inferred if the signal-to-noise ratio of the second harmonic is high enough to be detected by traditional quasi-monochromatic source searching algorithms. LISA observations could therefore bring new insights on the nature and origin of magnetic fields in white dwarfs or neutron stars.
Associations between resting‐state neural connectivity and positive affect in social anxiety disorder

Social anxiety disorder (SAD) has been characterized by deficits in social motivation and lack of reactivity to pleasurable stimuli (i.e., positive affect; [PA]). Recent neuroimaging work has shifted toward examining positively valenced motivational systems in SAD focused on reward responses. However, little is known about the associations of reward connectivity and PA in individuals with SAD. As such, the purpose of the current study was to determine whether connectivity among key units of reward neurocircuitry meaningfully relate to PA and whether these key units are more heterogeneous in SAD as compared to controls.
TagCLIP: Improving Discrimination Ability of Open-Vocabulary Semantic Segmentation

Recent success of Contrastive Language-Image Pre-training~(CLIP) has shown great promise in pixel-level open-vocabulary learning tasks. A general paradigm utilizes CLIP's text and patch embeddings to generate semantic masks. However, existing models easily misidentify input pixels from unseen classes, thus confusing novel classes with semantically-similar ones. In our work, we disentangle the ill-posed optimization problem into two parallel processes: one performs semantic matching individually, and the other judges reliability for improving discrimination ability. Motivated by special tokens in language modeling that represents sentence-level embeddings, we design a trusty token that decouples the known and novel category prediction tendency. With almost no extra overhead, we upgrade the pixel-level generalization capacity of existing models effectively. Our TagCLIP (CLIP adapting with Trusty-guidance) boosts the IoU of unseen classes by 7.4% and 1.7% on PASCAL VOC 2012 and COCO-Stuff 164K.
Detached and continuous circumstellar matter in Type Ibc supernovae from mass eruption

Some hydrogen-poor supernovae (SNe) are found to undergo interaction with dense circumstellar matter (CSM) that may originate from mass eruption(s) just prior to core-collapse. We model the interaction between the remaining star and the bound part of the erupted CSM that eventually falls back to the star. We find that while fallback initially results in a continuous CSM down to the star, feedback processes from the star can push the CSM to large radii of ≳1015 cm for several years after the eruption. In the latter case, a tenuous bubble surrounded by a dense and detached CSM extending to ≳1016 cm is expected. Our model offers a natural unifying explanation for the diverse CSM structures seen in hydrogen-poor SNe, such as Type Ibn/Icn SNe that show CSM signatures soon after explosion, and the recently discovered Type Ic SNe 2021ocs and 2022xxf (the “Bactrian”) with CSM signatures seen only at late times.
Visual Instruction Tuning

Instruction tuning large language models (LLMs) using machine-generated instruction-following data has improved zero-shot capabilities on new tasks, but the idea is less explored in the multimodal field. In this paper, we present the first attempt to use language-only GPT-4 to generate multimodal language-image instruction-following data. By instruction tuning on such generated data, we introduce LLaVA: Large Language and Vision Assistant, an end-to-end trained large multimodal model that connects a vision encoder and LLM for general-purpose visual and language understanding.Our early experiments show that LLaVA demonstrates impressive multimodel chat abilities, sometimes exhibiting the behaviors of multimodal GPT-4 on unseen images/instructions, and yields a 85.1% relative score compared with GPT-4 on a synthetic multimodal instruction-following dataset. When fine-tuned on Science QA, the synergy of LLaVA and GPT-4 achieves a new state-of-the-art accuracy of 92.53%. We make GPT-4 generated visual instruction tuning data, our model and code base publicly available.
Towards Better Evaluation of GNN Expressiveness with BREC Dataset

Research on the theoretical expressiveness of Graph Neural Networks (GNNs) has developed rapidly, and many methods have been proposed to enhance the expressiveness. However, most methods do not have a uniform expressiveness measure except for a few that strictly follow the $k$-dimensional Weisfeiler-Lehman ($k$-WL) test hierarchy. Their theoretical analyses are often limited to distinguishing certain families of non-isomorphic graphs, leading to difficulties in quantitatively comparing their expressiveness. In contrast to theoretical analysis, another way to measure expressiveness is by evaluating model performance on certain datasets containing 1-WL-indistinguishable graphs. Previous datasets specifically designed for this purpose, however, face problems with difficulty (any model surpassing 1-WL has nearly 100% accuracy), granularity (models tend to be either 100% correct or near random guess), and scale (only a few essentially different graphs in each dataset). To address these limitations, we propose a new expressiveness dataset, $\textbf{BREC}$, which includes 400 pairs of non-isomorphic graphs carefully selected from four primary categories (Basic, Regular, Extension, and CFI). These graphs have higher difficulty (up to 4-WL-indistinguishable), finer granularity (able to compare models between 1-WL and 3-WL), and a larger scale (400 pairs). Further, we synthetically test 23 models with higher-than-1-WL expressiveness on our BREC dataset. Our experiment gives the first thorough comparison of the expressiveness of those state-of-the-art beyond-1-WL GNN models. We expect this dataset to serve as a benchmark for testing the expressiveness of future GNNs. Our dataset and evaluation code are released at: https://github.com/GraphPKU/BREC.
The Relevance of Thrombo-Inflammatory Biomarkers and Their Relationship with Circulating Glycosaminoglycans in End-Stage Renal Disease Patients

En-stage renal disease (ESRD) is a growing public health problem. The atherosclerotic cardiovascular complications are the leading causes of mortality and morbidity in the ESRD. In this study, we sought to quantify the levels of thrombo-inflammatory biomarkers in an ESRD patients in comparison to healthy controls to determine their relevance in thrombo-inflammation and adverse outcomes. The levels of D-Dimer, C-reactive protein (CRP), plasminogen activator inhibitor 1 (PAI-1) antigen, functional PAI-1, thrombin activatable fibrinolysis inhibitor, tissue plasminogen activator, von Willebrand factor, and anti-PF4 IgG and microparticle (MP) activity were quantified by using commercially available ELISA immunoassays for each of the ESRD (n = 73) and control plasma samples (n = 10). The levels of endogenous glycosaminoglycans (GAGs) were quantified by utilizing a Heparin Red Probe (Redprobes UG, Germany). The collected data were analyzed to demonstrate the relationship between various parameters. All the tested biomarkers were increased in ESRD patients in comparison to healthy controls (p < 0.05). These biomarkers have shown significant correlations within each other except for anti-PF4 Ig G and MPs. The CRP levels were significantly higher in patients who had coronary artery disease (CAD) (p < 0.05), but there was no significant difference in other biomarkers according to the cardiovascular outcomes. In the multivariate analysis, the CRP (odds ratio: 1.19; 95% confidence interval: 1.01–1.41; p: 0.03) value was an independent predictor of CAD. In this study, we demonstrated increased levels of 10 different biomarkers in ESRD patients. The CRP levels can be a good predictor of CAD in ESRD patients.
Latent-KalmanNet: Learned Kalman Filtering for Tracking From High-Dimensional Signals

The Kalman filter (KF) is a widely used algorithm for tracking dynamic systems that are captured by state space (SS) models. The need to fully describe an SS model limits its applicability under complex settings, e.g., when tracking based on visual data, and the processing of high-dimensional signals often induces notable latency. These challenges can be treated by mapping the measurements into latent features obeying some postulated closed-form SS model, and applying the KF in the latent space. However, the validity of this approximated SS model may constitute a limiting factor. In this work, we study tracking from high-dimensional measurements under complex settings using a hybrid model-based/data-driven approach. By gradually tackling the challenges in handling the measurement model and the task, we develop Latent-KalmanNet, which implements tracking from high-dimensional measurements by leveraging data to jointly learn the KF along with the latent space mapping. Latent-KalmanNet combines a learned encoder with data-driven tracking in the latent space using the recently proposed-KalmanNet, while identifying the ability of each of these trainable modules to assist its counterpart via providing a suitable prior (by KalmanNet) and by learning a latent representation that facilitates data-aided tracking (by the encoder). Our empirical results demonstrate that the proposed Latent-KalmanNet achieves improved accuracy and run-time performance over both model-based and data-driven techniques by learning a surrogate latent representation that most facilitates tracking, while operating with limited complexity and latency.
Reward-free Policy Imitation Learning for Conversational Search

Existing conversational search studies mainly focused on asking better clarifying questions and/or improving search result quality. These works aim at retrieving better responses according to the search context, and their performances are evaluated on either single-turn tasks or multi-turn tasks under naive conversation policy settings. This leaves some questions about their applicability in real-world multi-turn conversations where realistically, each and every action needs to be made by the system itself, and search session efficiency is often an important concern of conversational search systems. While some recent works have identified the need for improving search efficiency in conversational search, they mostly require extensive data annotations and use hand-crafted rewards or heuristics to train systems that can achieve reasonable performance in a restricted number of turns, which has limited generalizability in practice. In this paper, we propose a reward-free conversation policy imitation learning framework, which can train a conversation policy without annotated conversation data or manually designed rewards. The trained conversation policy can be used to guide the conversational retrieval models to balance conversational search quality and efficiency. To evaluate the proposed conversational search system, we propose a new multi-turn-multi-response conversational evaluation metric named Expected Conversational Reciprocal Rank (ECRR). ECRR is designed to evaluate entire multi-turn conversational search sessions towards comprehensively evaluating both search result quality and search efficiency.