Miracle-dz/UniFAR-FTUs huggingface dataset:
Time Series Forecasting via Semi-Asymmetric Convolutional Architecture with Global Atrous Sliding Window

The proposed method in this paper is designed to address the problem of time series forecasting. Although some exquisitely designed models achieve excellent prediction performances, how to extract more useful information and make accurate predictions is still an open issue. Most of modern models only focus on a short range of information, which are fatal for problems such as time series forecasting which needs to capture long-term information characteristics. As a result, the main concern of this work is to further mine relationship between local and global information contained in time series to produce more precise predictions. In this paper, to satisfactorily realize the purpose, we make three main contributions that are experimentally verified to have performance advantages. Firstly, original time series is transformed into difference sequence which serves as input to the proposed model. And secondly, we introduce the global atrous sliding window into the forecasting model which references the concept of fuzzy time series to associate relevant global information with temporal data within a time period and utilizes central-bidirectional atrous algorithm to capture underlying-related features to ensure validity and consistency of captured data. Thirdly, a variation of widely-used asymmetric convolution which is called semi-asymmetric convolution is devised to more flexibly extract relationships in adjacent elements and corresponding associated global features with adjustable ranges of convolution on vertical and horizontal directions. The proposed model in this paper achieves state-of-the-art on most of time series datasets provided compared with competitive modern models.
From Semi-supervised to Omni-supervised Room Layout Estimation Using Point Clouds

Room layout estimation is a long-existing robotic vision task that benefits both environment sensing and motion planning. However, layout estimation using point clouds (PCs) still suffers from data scarcity due to annotation difficulty. As such, we address the semi-supervised setting of this task based upon the idea of model exponential moving averaging. But adapting this scheme to the state-of-the-art (SOTA) solution for PC-based layout estimation is not straightforward. To this end, we define a quad set matching strategy and several consistency losses based upon metrics tailored for layout quads. Besides, we propose a new online pseudo-label harvesting algorithm that decomposes the distribution of a hybrid distance measure between quads and PC into two components. This technique does not need manual threshold selection and intuitively encourages quads to align with reliable layout points. Surprisingly, this framework also works for the fully-supervised setting, achieving a new SOTA on the ScanNet benchmark. Last but not least, we also push the semi-supervised setting to the realistic omni-supervised setting, demonstrating significantly promoted performance on a newly annotated ARKitScenes testing set. Our codes, data and models are made publicly available**Code: https://github.com/AIR-DISCOVER/Omni-PQ.
Learning Roles with Emergent Social Value Orientations

Social dilemmas can be considered situations where individual rationality leads to collective irrationality. The multi-agent reinforcement learning community has leveraged ideas from social science, such as social value orientations (SVO), to solve social dilemmas in complex cooperative tasks. In this paper, by first introducing the typical"division of labor or roles"mechanism in human society, we provide a promising solution for intertemporal social dilemmas (ISD) with SVOs. A novel learning framework, called Learning Roles with Emergent SVOs (RESVO), is proposed to transform the learning of roles into the social value orientation emergence, which is symmetrically solved by endowing agents with altruism to share rewards with other agents. An SVO-based role embedding space is then constructed by individual conditioning policies on roles with a novel rank regularizer and mutual information maximizer. Experiments show that RESVO achieves a stable division of labor and cooperation in ISDs with different complexity.
Wave function network description and Kolmogorov complexity of quantum many-body systems

Programmable quantum devices are now able to probe wave functions at unprecedented levels. This is based on the ability to project the many-body state of atom and qubit arrays onto a measurement basis which produces snapshots of the system wave function. Extracting and processing information from such observations remains, however, an open quest. One often resorts to analyzing low-order correlation functions - i.e., discarding most of the available information content. Here, we introduce wave function networks - a mathematical framework to describe wave function snapshots based on network theory. For many-body systems, these networks can become scale free - a mathematical structure that has found tremendous success in a broad set of fields, ranging from biology to epidemics to internet science. We demonstrate the potential of applying these techniques to quantum science by introducing protocols to extract the Kolmogorov complexity corresponding to the output of a quantum simulator, and implementing tools for fully scalable cross-platform certification based on similarity tests between networks. We demonstrate the emergence of scale-free networks analyzing data from Rydberg quantum simulators manipulating up to 100 atoms. We illustrate how, upon crossing a phase transition, the system complexity decreases while correlation length increases - a direct signature of build up of universal behavior in data space. Comparing experiments with numerical simulations, we achieve cross-certification at the wave-function level up to timescales of 4 $\mu$ s with a confidence level of 90%, and determine experimental calibration intervals with unprecedented accuracy. Our framework is generically applicable to the output of quantum computers and simulators with in situ access to the system wave function, and requires probing accuracy and repetition rates accessible to most currently available platforms.
Strong Reverse Saturation and Fast-Light in Ruby

We observe a strong reverse saturation of absorption in ruby at a wavelength of 473 nm. With an intensity-modulated laser, we observe that the peaks of the pulses appear more than a hundred microseconds earlier than the reference signal. A theoretical model based on coherent population oscillation would suggest a fast-light effect with an extremely large and negative group index of $-(1.7\pm0.1)\times 10^6$. We propose that this pulse advancement can also be described by time-dependent absorption of ruby. Our study helps to understand the nature of the fast- and slow-light effects in transition-metal-doped crystals such as ruby and alexandrite.
Autobidders with Budget and ROI Constraints: Efficiency, Regret, and Pacing Dynamics

We study a game between autobidding algorithms that compete in an online advertising platform. Each autobidder is tasked with maximizing its advertiser's total value over multiple rounds of a repeated auction, subject to budget and/or return-on-investment constraints. We propose a gradient-based learning algorithm that is guaranteed to satisfy all constraints and achieves vanishing individual regret. Our algorithm uses only bandit feedback and can be used with the first- or second-price auction, as well as with any"intermediate"auction format. Our main result is that when these autobidders play against each other, the resulting expected liquid welfare over all rounds is at least half of the expected optimal liquid welfare achieved by any allocation. This holds whether or not the bidding dynamics converges to an equilibrium and regardless of the correlation structure between advertiser valuations.
Multi-modal Large Language Model Enhanced Pseudo 3D Perception Framework for Visual Commonsense Reasoning

The visual commonsense reasoning (VCR) task is to choose an answer and provide a justifying rationale based on the given image and textural question. Representative works first recognize objects in images and then associate them with key words in texts. However, existing approaches do not consider exact positions of objects in a human-like three-dimensional (3D) manner, making them incompetent to accurately distinguish objects and understand visual relation. Recently, multi-modal large language models (MLLMs) have been used as powerful tools for several multi-modal tasks but not for VCR yet, which requires elaborate reasoning on specific visual objects referred by texts. In light of the above, an MLLM enhanced pseudo 3D perception framework is designed for VCR. Specifically, we first demonstrate that the relation between objects is relevant to object depths in images, and hence introduce object depth into VCR frameworks to infer 3D positions of objects in images. Then, a depth-aware Transformer is proposed to encode depth differences between objects into the attention mechanism of Transformer to discriminatively associate objects with visual scenes guided by depth. To further associate the answer with the depth of visual scene, each word in the answer is tagged with a pseudo depth to realize depth-aware association between answer words and objects. On the other hand, BLIP-2 as an MLLM is employed to process images and texts, and the referring expressions in texts involving specific visual objects are modified with linguistic object labels to serve as comprehensible MLLM inputs. Finally, a parameter optimization technique is devised to fully consider the quality of data batches based on multi-level reasoning confidence. Experiments on the VCR dataset demonstrate the superiority of the proposed framework over state-of-the-art approaches.
A High-precision Survey of the D/H Ratio in the Nearby Interstellar Medium

We present high signal-to-noise ratio measurements of the H i Lyα absorption line toward 16 Galactic targets that are at distances between approximately 190 and 2200 pc, all beyond the wall of the Local Bubble. We describe the models used to remove stellar emission and absorption features and the methods used to account for all known sources of error in order to compute high-precision values of the H i column density with robust determinations of the uncertainties. When combined with H2 column densities from other sources, we find total H column densities ranging from 1020.01 to 1021.25 cm−2. Using deuterium column densities from Far Ultraviolet Spectroscopic Explorer observations we determine the D/H ratio along the sight lines. We confirm and strengthen the conclusion that D/H is spatially variable over these H i column density and target distance regimes, which predominantly probe the interstellar medium outside the Local Bubble. We discuss how these results affect models of Galactic chemical evolution. We also present an analysis of metal lines along the five sight lines for which we have high-resolution spectra and, along with results reported in the literature, discuss the corresponding column densities in the context of a generalized depletion analysis. We find that D/H is only weakly correlated with metal depletion and conclude that the spatial D/H variability is not solely due to dust depletion. A bifurcation of D/Htot as a function of depletion at high depletion levels provides modest support that deuterium-rich gas is infalling onto the Galactic plane.
Grain Boundary Migration and Other Processes in the Formation of Ice Sheets on Water

Direct observations of the growth of ice on, and perpendicular to, a water‐air interface are described, in an experiment reproducing the conditions of formation of natural lake ice. The final, preferred crystal orientation is affected by the initial orientations and by selective wedging cut of grains of certain orientations, but often the most important factor is grain boundary migration. Boundary migration is driven in part by interfacial energy and probably in part by stress. The origin of the stress is not known. Preferred intergrain orientations are found.
Zero-shot-Learning Cross-Modality Data Translation Through Mutual Information Guided Stochastic Diffusion

Cross-modality data translation has attracted great interest in image computing. Deep generative models (\textit{e.g.}, GANs) show performance improvement in tackling those problems. Nevertheless, as a fundamental challenge in image translation, the problem of Zero-shot-Learning Cross-Modality Data Translation with fidelity remains unanswered. This paper proposes a new unsupervised zero-shot-learning method named Mutual Information guided Diffusion cross-modality data translation Model (MIDiffusion), which learns to translate the unseen source data to the target domain. The MIDiffusion leverages a score-matching-based generative model, which learns the prior knowledge in the target domain. We propose a differentiable local-wise-MI-Layer ($LMI$) for conditioning the iterative denoising sampling. The $LMI$ captures the identical cross-modality features in the statistical domain for the diffusion guidance; thus, our method does not require retraining when the source domain is changed, as it does not rely on any direct mapping between the source and target domains. This advantage is critical for applying cross-modality data translation methods in practice, as a reasonable amount of source domain dataset is not always available for supervised training. We empirically show the advanced performance of MIDiffusion in comparison with an influential group of generative models, including adversarial-based and other score-matching-based models.
Experimental paradigm for examining the formation of complex auditory categories

Phonetic perception is at its essence a categorization task involving multiple imperfectly valid cues to category membership. To examine the general cognitive processes underlying the formation and utilization of complex auditory categories such as speech, we have created a set of stimuli crafted from 300‐m bursts of white noise. These stimuli vary in onset duration (or attack) and the center frequencies of two spectral gaps. Categories of varying complexity can be created from this stimulus set and input distributions can be strictly controlled. In addition, category formation can be observed ‘‘online’’ in this microgenetic design as subjects make responses (identification and discrimination) while learning the categories. In one such task, subjects were trained with categories defined by boundary values across three attributes. After 10 h of training, the subjects demonstrated extensive learning despite the fact that the categories suffered from ‘‘lack of invariance;’’ that is, no cue could be relied on.
GaitSADA: Self-Aligned Domain Adaptation for mmWave Gait Recognition

mmWave radar-based gait recognition is a novel user identification method that captures human gait biometrics from mmWave radar return signals. This technology offers privacy protection and is resilient to weather and lighting conditions. However, its generalization performance is yet unknown and limits its practical deployment. To address this problem, in this paper, a non-synthetic dataset is collected and analyzed to reveal the presence of spatial and temporal domain shifts in mmWave gait biometric data, which significantly impacts identification accuracy. To mitigate this issue, a novel self-aligned domain adaptation method called GaitSADA is proposed. GaitSADA improves system generalization performance by using a two-stage semi-supervised model training approach. The first stage employs semi-supervised contrastive learning to learn a compact gait representation from both source and target domain data, aligning source-target domain distributions implicitly. The second stage uses semi-supervised consistency training with centroid alignment to further close source-target domain gap by pseudo-labelling the target-domain samples, clustering together the samples belonging to the same class but from different domains, and pushing the class centroid close to the weight vector of each class. Experiments show that GaitSADA outperforms representative domain adaptation methods with an improvement ranging from 15.41% to 26.32% on average accuracy in low data regimes. Code and dataset will be available at https://exitudio.github. io/GaitSADA
Evolution in polarimetric signatures of thin saline ice under constant growth

An experiment is carried out to measure polarimetric backscatter signatures at C band together with physical characteristics of thin saline ice grown at a constant rate under quiescent conditions. The objectives are to investigate the electromagnetic scattering mechanism in saline ice, to relate the polarimetric backscatter to ice physical characteristics, and to assess the inversion of ice thickness from backscatter data. Controlled laboratory conditions are utilized to avoid complicated variations in interrelated characteristics of saline ice and the environment. The ice sheet was grown in a refrigerated facility at the U.S. Army Cold Regions Research and Engineering Laboratory. Growth conditions, thickness and growth rate, temperatures and salinities, and internal and interfacial structures of the ice sheet were monitored. Measurements indicate that the laboratory saline ice has characteristics similar to thin sea ice in the Arctic. A strong increase of 6–10 dB is observed in backscatter as the ice grows from 3 to 11.2 cm in thickness. Ice characteristics and processes suggest that the large enhancement in backscatter relates to the interconnection and increase in the size of brine inclusions during the desalination process. Polarimetric signatures calculated with a physically based sea ice model agree with backscatter data at incident angles from 20° to 35° over the thickness range of the ice growth. Furthermore, backscattering coefficients of the saline ice sheet are shown to be similar to airborne radar measurements of thin sea ice growing in a newly opened lead in the Beaufort Sea. For the inversion the large increase in backscatter indicates that the ice thickness is retrievable for thin ice grown under the conditions in this experiment. More complicated conditions should be considered in future experiments to study their effects on the retrieval of sea ice parameters.
Conversational Automated Program Repair

Automated Program Repair (APR) can help developers automatically generate patches for bugs. Due to the impressive performance obtained using Large Pre-Trained Language Models (LLMs) on many code related tasks, researchers have started to directly use LLMs for APR. However, prior approaches simply repeatedly sample the LLM given the same constructed input/prompt created from the original buggy code, which not only leads to generating the same incorrect patches repeatedly but also miss the critical information in testcases. To address these limitations, we propose conversational APR, a new paradigm for program repair that alternates between patch generation and validation in a conversational manner. In conversational APR, we iteratively build the input to the model by combining previously generated patches with validation feedback. As such, we leverage the long-term context window of LLMs to not only avoid generating previously incorrect patches but also incorporate validation feedback to help the model understand the semantic meaning of the program under test. We evaluate 10 different LLM including the newly developed ChatGPT model to demonstrate the improvement of conversational APR over the prior LLM for APR approach.
Hierarchical Disentangled Representation for Invertible Image Denoising and Beyond

Image denoising is a typical ill-posed problem due to complex degradation. Leading methods based on normalizing flows have tried to solve this problem with an invertible transformation instead of a deterministic mapping. However, the implicit bijective mapping is not explored well. Inspired by a latent observation that noise tends to appear in the high-frequency part of the image, we propose a fully invertible denoising method that injects the idea of disentangled learning into a general invertible neural network to split noise from the high-frequency part. More specifically, we decompose the noisy image into clean low-frequency and hybrid high-frequency parts with an invertible transformation and then disentangle case-specific noise and high-frequency components in the latent space. In this way, denoising is made tractable by inversely merging noiseless low and high-frequency parts. Furthermore, we construct a flexible hierarchical disentangling framework, which aims to decompose most of the low-frequency image information while disentangling noise from the high-frequency part in a coarse-to-fine manner. Extensive experiments on real image denoising, JPEG compressed artifact removal, and medical low-dose CT image restoration have demonstrated that the proposed method achieves competing performance on both quantitative metrics and visual quality, with significantly less computational cost.
An Comparative Analysis of Different Pitch and Metrical Grid Encoding Methods in the Task of Sequential Music Generation

Pitch and meter are two fundamental music features for symbolic music generation tasks, where researchers usually choose different encoding methods depending on specific goals. However, the advantages and drawbacks of different encoding methods have not been frequently discussed. This paper presents a integrated analysis of the influence of two low-level feature, pitch and meter, on the performance of a token-based sequential music generation model. First, the commonly used MIDI number encoding and a less used class-octave encoding are compared. Second, an dense intra-bar metric grid is imposed to the encoded sequence as auxiliary features. Different complexity and resolutions of the metric grid are compared. For complexity, the single token approach and the multiple token approach are compared; for grid resolution, 0 (ablation), 1 (bar-level), 4 (downbeat-level) 12, (8th-triplet-level) up to 64 (64th-note-grid-level) are compared; for duration resolution, 4, 8, 12 and 16 subdivisions per beat are compared. All different encodings are tested on separately trained Transformer-XL models for a melody generation task. Regarding distribution similarity of several objective evaluation metrics to the test dataset, results suggest that the class-octave encoding significantly outperforms the taken-for-granted MIDI encoding on pitch-related metrics; finer grids and multiple-token grids improve the rhythmic quality, but also suffer from over-fitting at early training stage. Results display a general phenomenon of over-fitting from two aspects, the pitch embedding space and the test loss of the single-token grid encoding. From a practical perspective, we both demonstrate the feasibility and raise the concern of easy over-fitting problem of using smaller networks and lower embedding dimensions on the generation task. The findings can also contribute to futural models in terms of feature engineering.
Theory of Gamma-Ray loud AGNs

The last decade has seen tremendous developments in gamma-ray astronomy with the extragalactic sky becoming highly populated by Active Galactic Nuclei (AGNs). This brief review highlights some of the progress in AGN research achieved over the years, and discusses exemplary advances in the theory and physics of gamma-ray loud AGNs, including black-hole magnetospheric processes, the physics of pc-scales jets, as well as particle acceleration and high-energy emission in the large-scale jets of AGNs.