Miracle-dz/UniFAR-FTUs huggingface dataset:
Camera Based mmWave Beam Prediction: Towards Multi-Candidate Real-World Scenarios

Leveraging sensory information to aid the millimeter-wave (mmWave) and sub-terahertz (sub-THz) beam selection process is attracting increasing interest. This sensory data, captured for example by cameras at the basestations, has the potential of significantly reducing the beam sweeping overhead and enabling highly-mobile applications. The solutions developed so far, however, have mainly considered single-candidate scenarios, i.e., scenarios with a single candidate user in the visual scene, and were evaluated using synthetic datasets. To address these limitations, this paper extensively investigates the sensing-aided beam prediction problem in a real-world multi-object vehicle-to-infrastructure (V2I) scenario and presents a comprehensive machine learning-based framework. In particular, this paper proposes to utilize visual and positional data to predict the optimal beam indices as an alternative to the conventional beam sweeping approaches. For this, a novel user (transmitter) identification solution has been developed, a key step in realizing sensing-aided multi-candidate and multi-user beam prediction solutions. The proposed solutions are evaluated on the large-scale real-world DeepSense $6$G dataset. Experimental results in realistic V2I communication scenarios indicate that the proposed solutions achieve close to $100\%$ top-5 beam prediction accuracy for the scenarios with single-user and close to $95\%$ top-5 beam prediction accuracy for multi-candidate scenarios. Furthermore, the proposed approach can identify the probable transmitting candidate with more than $93\%$ accuracy across the different scenarios. This highlights a promising approach for nearly eliminating the beam training overhead in mmWave/THz communication systems.
Revisiting Vision Transformer from the View of Path Ensemble

Vision Transformers (ViTs) are normally regarded as a stack of transformer layers. In this work, we propose a novel view of ViTs showing that they can be seen as ensemble networks containing multiple parallel paths with different lengths. Specifically, we equivalently transform the traditional cascade of multi-head self-attention (MSA) and feed-forward network (FFN) into three parallel paths in each transformer layer. Then, we utilize the identity connection in our new transformer form and further transform the ViT into an explicit multi-path ensemble network. From the new perspective, these paths perform two functions: the first is to provide the feature for the classifier directly, and the second is to provide the lower-level feature representation for subsequent longer paths. We investigate the influence of each path for the final prediction and discover that some paths even pull down the performance. Therefore, we propose the path pruning and EnsembleScale skills for improvement, which cut out the underperforming paths and reweight the ensemble components, respectively, to optimize the path combination and make the short paths focus on providing high-quality representation for subsequent paths. We also demonstrate that our path combination strategies can help ViTs go deeper and act as high-pass filters to filter out partial low-frequency signals. To further enhance the representation of paths served for subsequent paths, self-distillation is applied to transfer knowledge from the long paths to the short paths. This work calls for more future research to explain and design ViTs from new perspectives.
The ecology, exploitation, and conservation of river turtles

Table of Contents Preface 1. Introduction 2. River Turtle Diversity, Adaptations, and Roles in the River 3. Communities and Habitats 4. Traditional Exploitation Methods, Efficiency, and Consequences for 5. River Turtle Exploitation: Past and Present 6. Indirect Factors Contributing to Extinction 7. Conservation, Management, and Rehabilitation Epilogue Literature Cited Index
The unphysicality of Hilbert spaces

We show that Hilbert spaces should not be considered the ``correct'' spaces to represent quantum states mathematically. We first prove that the requirements posited by complex inner product spaces are physically justified. We then show that completeness in the infinite-dimensional case requires the inclusion of states with infinite expectations, coordinate transformations that take finite expectations to infinite ones and vice-versa, and time evolutions that transform finite expectations to infinite ones in finite time. This makes Hilbert spaces physically unsound as they model a potential infinity as an actual infinity. We suspect that at least some problems in quantum theory related to infinities may be ultimately caused by the wrong space being used. We strongly believe a better solution can be found, and we look at Schwartz spaces for inspiration, as, among other things, they guarantee that the expectation of all polynomials of position and momentum are finite, their elements are uniquely identified by these expectations, and they are the only space closed under Fourier transform.
A discretization scheme for path-dependent FBSDEs

This paper studies a discretization scheme for solutions to forward-backward stochastic differential equations (FBSDEs) with path-dependent coefficients. We show the convergence of the Picard-type iteration to the FBDSE solution and provide its convergence rate. To the best of our knowledge, this is the first result of discretization scheme for path-dependent FBSDEs. Using this result, we establish a numerical method for solutions to second-order parabolic path-dependent partial differential equations. To achieve this, weak approximation of martingale representation theorem (Cont, Rama, and Yi Lu. ``Weak approximation of martingale representations."Stochastic Processes and their Applications 2016) is employed. Our results generalize the scheme for Markovian cases in (Bender, Christian, and Robert Denk. ``A forward scheme for backward SDEs."Stochastic processes and their applications, 2007)
MT4CrossOIE: Multi-stage Tuning for Cross-lingual Open Information Extraction

Cross-lingual open information extraction aims to extract structured information from raw text across multiple languages. Previous work uses a shared cross-lingual pre-trained model to handle the different languages but underuses the potential of the language-specific representation. In this paper, we propose an effective multi-stage tuning framework called MT4CrossIE, designed for enhancing cross-lingual open information extraction by injecting language-specific knowledge into the shared model. Specifically, the cross-lingual pre-trained model is first tuned in a shared semantic space (e.g., embedding matrix) in the fixed encoder and then other components are optimized in the second stage. After enough training, we freeze the pre-trained model and tune the multiple extra low-rank language-specific modules using mixture-of-LoRAs for model-based cross-lingual transfer. In addition, we leverage two-stage prompting to encourage the large language model (LLM) to annotate the multi-lingual raw data for data-based cross-lingual transfer. The model is trained with multi-lingual objectives on our proposed dataset OpenIE4++ by combing the model-based and data-based transfer techniques. Experimental results on various benchmarks emphasize the importance of aggregating multiple plug-in-and-play language-specific modules and demonstrate the effectiveness of MT4CrossIE in cross-lingual OIE\footnote{\url{https://github.com/CSJianYang/Multilingual-Multimodal-NLP}}.
Fourier neural operator for learning solutions to macroscopic traffic flow models: Application to the forward and inverse problems

Deep learning methods are emerging as popular computational tools for solving forward and inverse problems in traffic flow. In this paper, we study a neural operator framework for learning solutions to nonlinear hyperbolic partial differential equations with applications in macroscopic traffic flow models. In this framework, an operator is trained to map heterogeneous and sparse traffic input data to the complete macroscopic traffic state in a supervised learning setting. We chose a physics-informed Fourier neural operator ($\pi$-FNO) as the operator, where an additional physics loss based on a discrete conservation law regularizes the problem during training to improve the shock predictions. We also propose to use training data generated from random piecewise constant input data to systematically capture the shock and rarefied solutions. From experiments using the LWR traffic flow model, we found superior accuracy in predicting the density dynamics of a ring-road network and urban signalized road. We also found that the operator can be trained using simple traffic density dynamics, e.g., consisting of $2-3$ vehicle queues and $1-2$ traffic signal cycles, and it can predict density dynamics for heterogeneous vehicle queue distributions and multiple traffic signal cycles $(\geq 2)$ with an acceptable error. The extrapolation error grew sub-linearly with input complexity for a proper choice of the model architecture and training data. Adding a physics regularizer aided in learning long-term traffic density dynamics, especially for problems with periodic boundary data.
Cross-Attribute Matrix Factorization Model with Shared User Embedding

Over the past few years, deep learning has firmly established its prowess across various domains, including computer vision, speech recognition, and natural language processing. Motivated by its outstanding success, researchers have been directing their efforts towards applying deep learning techniques to recommender systems. Neural collaborative filtering (NCF) and Neural Matrix Factorization (NeuMF) refreshes the traditional inner product in matrix factorization with a neural architecture capable of learning complex and data-driven functions. While these models effectively capture user-item interactions, they overlook the specific attributes of both users and items. This can lead to robustness issues, especially for items and users that belong to the"long tail". Such challenges are commonly recognized in recommender systems as a part of the cold-start problem. A direct and intuitive approach to address this issue is by leveraging the features and attributes of the items and users themselves. In this paper, we introduce a refined NeuMF model that considers not only the interaction between users and items, but also acrossing associated attributes. Moreover, our proposed architecture features a shared user embedding, seamlessly integrating with user embeddings to imporve the robustness and effectively address the cold-start problem. Rigorous experiments on both the Movielens and Pinterest datasets demonstrate the superiority of our Cross-Attribute Matrix Factorization model, particularly in scenarios characterized by higher dataset sparsity.
Topology of Fatou Components for Endomorphisms of Cp 2 : Linking with the Green's Current

Little is known about the global topology of Fatou components for holomor-phic endomorphisms f : CP 2 → CP 2. We develop a type of linking number between closed loops in the Fatou set of f with the Green's current T , which forms the complement of the Fatou set. Using these linking numbers we establish that many classes of endomorphisms have Fatou components with infinitely generated first homology. For example, we prove that the Fatou set has infinitely generated first homology for any polynomial endomorphism of CP 2 for which the restriction to the line at infinity is hyperbolic and has disconnected Julia set. In addition we show a polynomial skew product of CP 2 has Fatou set with infinitely generated first homology if the vertical Julia set in an appropriate slice is disconnected. We then conclude with a section of concrete examples and questions for further study.
SpeechX: Neural Codec Language Model as a Versatile Speech Transformer

Recent advancements in generative speech models based on audio-text prompts have enabled remarkable innovations like high-quality zero-shot text-to-speech. However, existing models still face limitations in handling diverse audio-text speech generation tasks involving transforming input speech and processing audio captured in adverse acoustic conditions. This paper introduces SpeechX, a versatile speech generation model capable of zero-shot TTS and various speech transformation tasks, dealing with both clean and noisy signals. SpeechX combines neural codec language modeling with multi-task learning using task-dependent prompting, enabling unified and extensible modeling and providing a consistent way for leveraging textual input in speech enhancement and transformation tasks. Experimental results show SpeechX's efficacy in various tasks, including zero-shot TTS, noise suppression, target speaker extraction, speech removal, and speech editing with or without background noise, achieving comparable or superior performance to specialized models across tasks. See https://aka.ms/speechx for demo samples.
Particles and $p-$adic integrals of Spin$\left(\frac{1}{2}\right)$: spin Lie group, $\mathcal{R}(\rho,q)-$gamma and $\mathcal{R}(\rho,q)-$ beta functions, ghost and applications

In this work, we address the $p$-adic analogues of the fermion spin Lie algebras and Lie groups. We consider the extension of the fermion spin Lie groups and Lie algebras to the $p-$adic Lie groups and investigate the way to extend their integral to the zeta function as well. We show that their groups are ghost friendly. In addition, we develop the $\mathcal{R}(p,q)-$deformed calculus for the Bernoulli, Volkenborn, Euler and Genocchi polynomials, and establish related definitions. Finally, we perform a $p-$adic generalization of beta and gamma functions and exhibit some physical applications.
Checklist to Transparently Define Test Oracles for TP, FP, and FN Objects in Automated Driving

Popular test oracles for the perception subsystem of driving automation systems identify true-positive (TP), false-positive (FP), and false-negative (FN) objects. Oracle transparency is needed for comparing test results and for safety cases. To date, there exists a common notion of TPs, FPs, and FNs in the field, but apparently no published way to comprehensively define their oracles. Therefore, this paper provides a checklist of functional aspects and implementation details that affect the oracle behavior. Besides labeling policies of the test set, we cover fields of view, occlusion handling, safety-relevant areas, matching criteria, temporal and probabilistic issues, and further aspects. Even though our checklist can hardly be formalized, it can help practitioners maximize the transparency of their oracles, which, in turn, makes statements on object perception more reliable and comparable.
ON A CONJECTURE ABOUT HIGGS BUNDLES FOR RANK 2 AND SOME INEQUALITIES

We brieﬂy review an open conjecture about Higgs bundles that are semistable with after pulling back to any curve, and prove it in the rank 2 case. We also prove a set of inequalities holding for H-nef Higgs bundles that generalize some of the Fulton-Lazarsfeld inequalities for numerically eﬀective vector bundles.
Distributionally Robust Optimization and Invariant Representation Learning for Addressing Subgroup Underrepresentation: Mechanisms and Limitations

Spurious correlation caused by subgroup underrepresentation has received increasing attention as a source of bias that can be perpetuated by deep neural networks (DNNs). Distributionally robust optimization has shown success in addressing this bias, although the underlying working mechanism mostly relies on upweighting under-performing samples as surrogates for those underrepresented in data. At the same time, while invariant representation learning has been a powerful choice for removing nuisance-sensitive features, it has been little considered in settings where spurious correlations are caused by significant underrepresentation of subgroups. In this paper, we take the first step to better understand and improve the mechanisms for debiasing spurious correlation due to subgroup underrepresentation in medical image classification. Through a comprehensive evaluation study, we first show that 1) generalized reweighting of under-performing samples can be problematic when bias is not the only cause for poor performance, while 2) naive invariant representation learning suffers from spurious correlations itself. We then present a novel approach that leverages robust optimization to facilitate the learning of invariant representations at the presence of spurious correlations. Finetuned classifiers utilizing such representation demonstrated improved abilities to reduce subgroup performance disparity, while maintaining high average and worst-group performance.
The Toxicology, Environmental Fate, and Human Risk of Herbicide Orange and Its Associated Dioxin

Abstract : The use of herbicides in South Vietnam between 1962 and 1971 was reviewed, including the nature and quantities of herbicides used, their handling and application. Emphasis was placed on Herbicide Orange, a 50:50 mixture of 2, 4-dichlorophenoxyacetic acid (2,4-D) and 2,4,5-trichlorophenoxyacetic acid (2,4, 5-T), with its associated contaminant 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) . The at-risk US military population in South Vietnam was defined to establish the potential for exposure in handling and application of Herbicide Orange. The environmental fate of the phenoxy herbicides and TCDD was reviewed to evaluate the potential for human risk associated with exposure to areas previously treated with Herbicide Orange. The occupational and environmental aspects of the project to incinerate at sea 2.22 million gallons of Herbicide Orange during the summer of 1977 were summarized to assess the potential for human exposure in handling large quantities of the material. Scientific data were reviewed on incidents and episodes involving suspected poisoning of humans or animals by phenoxy herbicides or TCDD. Literature dealing with animal toxicology and the effects of human exposure to 2,4-D, 2,4,5-T and TCDD was reviewed to correlate exposures with symtomatology.
Mitigation of Greenhouse Emissions from Transportation Systems Through Modelling and Data

Greenhouse gas emissions from transportation systems have become a grave health and environmental concern as the rise in population and urban connectivity has caused the increase in traffic demand and congestion. Increasing the use of public transportation would become a key factor in mitigating the adverse results of vehicular emissions. In this work, we have constructed a traffic and fuel consumption forecasting framework to analyze the effects of increasing public transportation modal share on the reduction of the total average fuel consumption by daily traffic. We have successfully forecasted a fuel savings of 21% via the increase of public transportation modal share from 12% to 40%. Our results could serve as an effective guideline for policymakers in drafting sustainable transportation masterplans.
Universal pretreatment development for low-input proteomics using Lauryl Maltose Neopentyl Glycol

In recent years, the demand for low-input proteomics, most notably single-cell proteomics (SCP), has increased. In this study, we developed a Lauryl Maltose Neopentyl Glycol (LMNG)-assisted sample preparation (LASP) method that suppresses the loss of proteins and peptides in samples by adding LMNG, a surfactant, to the digested solution and removing the LMNG simply via reversed phase solid-phase extraction. The advantage of removing LMNG during sample preparation for general proteomic analysis is that it prevents mass spectrometry (MS) contamination. When the LASP method was applied to the low-input SP3 method and on-bead digestion in immunoprecipitation-MS, the recovery of the digested peptides was greatly improved. Furthermore, we established a simple and operationally easy sample preparation method for SCP based on the LASP method (scpLASP) and identified a median of 1,175 proteins from a single HEK239F cell using liquid chromatography (LC)-MS/MS with a throughput of 80 samples per day.
Investigation of the effects of different ink density values on color gamut in offset printing

One of the most important reasons of increased print quality is the use of inks with a high coloring feature (Leach & Pierce, 1993). The film thickness of the ink should be below 2μm during printing (Dalton et al, 2002). The critical point is the direct effect of ink film thickness on print quality and color gamut. Ink absorption of paper and drying are the other important factors (Zjakić, Bates & Milković, 2011). It is also very important to have suitable solid ink density values for the used paper surface to achieve the desired print quality and color gamut. When the optimum amount of ink that is transferred onto the paper is set, the print quality as well as the color gamut will increase.
Upper mantle seismic anisotropy of South Victoria Land/Ross Island, Antarctica from SKS and SKKS splitting analysis

We determine shear wave splitting parameters of teleseismic SKS/SKKS phases at stations in Antarctica as part of the TAMSEIS experiment. Our data indicate that the anisotropy is fairly uniform from the Transantarctic Mountains into the interior of East Antarctica with a slight change of fast direction near the Vostok Highlands. The data show fairly consistent fast direction of anisotropy oriented approximately N60E with splitting times of about 1 second for stations in the vicinity of the Transantarctic Mountains, along the coast, and into East Antarctica. However, stations in the vicinity of the Vostok Highlands show a small rotation of approximately 20 degrees of the fast direction of anisotropy as well as a decrease in magnitude of the splitting time of about 0.5 second.
Design and FPGA verification of All-digital Low Order Sigma Delta Modulator with Simple Structure

This paper describes the design and FPGA verification of low order sigma delta modulators applied in fully digital applications. The all-digital sigma delta modulators, which output single bit streams, adopt very simple structure, such as first-order or second-order, and occupying very few digital circuits. The design work is based on matlab/simulink simulation, including parameter setting, loop structure adjustment, alternating current (AC) analysis, direct current (DC) analysis and so on. The design of the digital circuit implementation includes the selection of the data format, the selection of the adder structure, the HDL cosimulation of the digital circuit, and the FPGA in the loop verification. Suggestions also given for the application of these modulators.