Miracle-dz/UniFAR-FTUs huggingface dataset:
Stopper vs. singular-controller games with degenerate diffusions

We study zero-sum stochastic games between a singular controller and a stopper when the (state-dependent) diffusion matrix of the underlying controlled diffusion process is degenerate. In particular, we show the existence of a value for the game and determine an optimal strategy for the stopper. The degeneracy of the dynamics prevents the use of analytical methods based on solution in Sobolev spaces of suitable variational problems. Therefore we adopt a probabilistic approach based on a perturbation of the underlying diffusion modulated by a parameter $\gamma>0$. For each $\gamma>0$ the approximating game is non-degenerate and admits a value $u^\gamma$ and an optimal strategy $\tau^\gamma_*$ for the stopper. Letting $\gamma\to 0$ we prove convergence of $u^\gamma$ to a function $v$, which identifies the value of the original game. We also construct explicitly optimal stopping times $\theta^\gamma_*$ for $u^\gamma$, related but not equal to $\tau^\gamma_*$, which converge almost surely to an optimal stopping time $\theta_*$ for the game with degenerate dynamics.
Cascaded Channel Decoupling Based Solution for RIS Regulation Matrix

This article presents a novel solution for reconfigurable intelligent surfaces (RISs) based on cascaded channel decoupling. The proposed mechanism simplifies the RIS regulation matrix, by decomposing the electromagnetic wave regulation process into two sub-processes: virtual receiving response and virtual regular transmission, which leads to the decoupling of the RIS cascaded channel. This article further discusses the concrete implementation of the proposed channel decoupling mechanism in two scenarios of single-user access and multi-user access, and gives the corresponding detailed scheme. The numerical simulation results demonstrate that the proposed channel decoupling scheme is a low-complexity and effective solution for resolving the RIS regulation matrix.
Using cross country panel regression analysis to relook at the relationship between armed conflict and HIV prevalence in Africa

This paper explores the relationship between armed conflict and HIV prevalence in Africa. We review the literature suggesting that conflict can exacerbate the HIV epidemic (through sex with infected soldiers, war-related rape, povertyrelated unsafe sex, transactional sex, etc) and the literature arguing the contrary (such as that militaries do not always have higher prevalence rates than the surrounding populations and that war can disrupt sexual networks). Building on past econometric contributions on the debate, our parsimonious cross-country panel data regression analysis of HIV prevalence casts doubt on the argument that conflict worsens the epidemic, particularly at a country level. We do, however, find a negative, albeit statistically weak association between armed conflict, militarization, and HIV prevalence. Fully acknowledging the limitations of our analysis, we stress the importance of looking at the association between HIV prevalence and conflict on a case by case basis, taking lessons from the experiences of other conflict afflicted countries.
Insulator to Metal Transition, Spin-Phonon Coupling, and Potential Magnetic Transition Observed in Quantum Spin Liquid Candidate LiYbSe$_2$ under High Pressure

Metallization of quantum spin liquid (QSL) materials has long been considered as a potential route to achieve unconventional superconductivity. Here we report our endeavor in this direction by pressurizing a three-dimensional QSL candidate, LiYbSe$_2$, with a previously unreported pyrochlore structure. High-pressure X-ray diffraction and Raman studies up to 50 GPa reveal no appreciable changes of structural symmetry or distortion in this pressure range. This compound is so insulating that its resistance decreases below 10$^5$ ${\Omega}$ only at pressures above 25 GPa in the corresponding temperature range accompanying the gradual reduction of band gap upon compression. Interestingly, an insulator-to-metal transition takes place in LiYbSe$_2$ at about 68 GPa and the metallic behavior remains up to 123.5 GPa, the highest pressure reached in the present study. A possible sign of magnetic or other phase transition was observed in LiYbSe$_2$. The insulator-to-metal transition in LiYbSe$_2$ under high pressure makes it an ideal system to study the pressure effects on QSL candidates of spin-1/2 Yb$^{3+}$ system in different lattice patterns.
Experiment on Gender and Racial/Ethnic Bias Against Video Game Streamers: Comparing Perceived Gameplay Skill and Viewer Engagement

Research suggests there is a perception that females and underrepresented racial/ethnic minorities have worse gameplay skills and produce less engaging video game streaming content. This bias might impact streamers' audience size, viewers' financial patronage of a streamer, streamers' sponsorship offers, etc. However, few studies on this topic use experimental methods. To fill this gap, we conducted a between-subjects survey experiment to examine if viewers are biased against video game streamers based on the streamer's gender or race/ethnicity. 200 survey participants rated the gameplay skill and viewer engagement of an identical gameplay recording. The only change between experimental conditions was the streamer's name who purportedly created the recording. The Dunnett's test found no statistically significant differences in viewer engagement ratings when comparing White male streamers to either White female (p = 0.37), Latino male (p = 0.66), or Asian male (p = 0.09) streamers. Similarly, there were no statistically significant differences in gameplay skill ratings when comparing White male streamers to either White female (p = 0.10), Latino male (p = 1.00), or Asian male (p = 0.59) streamers. Potential contributors to statistically non-significant results and counter-intuitive results (i.e., White females received non-significantly higher ratings than White males) are discussed.
Twist-3 generalized parton distribution for the proton from basis light-front quantization

We investigate the twist-3 generalized parton distributions (GPDs) for the valence quarks of the proton within the basis light-front quantization (BLFQ) framework. We first solve for the mass spectra and light-front waved functions (LFWFs) in the leading Fock sector using an effective Hamiltonian. Using the LFWFs we then calculate the twist-3 GPDs via the overlap representation. By taking the forward limit, we also get the twist-3 parton distribution functions (PDFs), and discuss their properties. Our prediction for the twist-3 scalar PDF agrees well with the CLAS experimental extractions.
DeepDR: Deep Structure-Aware RGB-D Inpainting for Diminished Reality

Diminished reality (DR) refers to the removal of real objects from the environment by virtually replacing them with their background. Modern DR frameworks use inpainting to hallucinate unobserved regions. While recent deep learning-based inpainting is promising, the DR use case is complicated by the need to generate coherent structure and 3D geometry (i.e., depth), in particular for advanced applications, such as 3D scene editing. In this paper, we propose DeepDR, a first RGB-D inpainting framework fulfilling all requirements of DR: Plausible image and geometry inpainting with coherent structure, running at real-time frame rates, with minimal temporal artifacts. Our structure-aware generative network allows us to explicitly condition color and depth outputs on the scene semantics, overcoming the difficulty of reconstructing sharp and consistent boundaries in regions with complex backgrounds. Experimental results show that the proposed framework can outperform related work qualitatively and quantitatively.
FedEmb: A Vertical and Hybrid Federated Learning Algorithm using Network And Feature Embedding Aggregation

Federated learning (FL) is an emerging paradigm for decentralized training of machine learning models on distributed clients, without revealing the data to the central server. The learning scheme may be horizontal, vertical or hybrid (both vertical and horizontal). Most existing research work with deep neural network (DNN) modelling is focused on horizontal data distributions, while vertical and hybrid schemes are much less studied. In this paper, we propose a generalized algorithm FedEmb, for modelling vertical and hybrid DNN-based learning. The idea of our algorithm is characterised by higher inference accuracy, stronger privacy-preserving properties, and lower client-server communication bandwidth demands as compared with existing work. The experimental results show that FedEmb is an effective method to tackle both split feature&subject space decentralized problems, shows 0.3% to 4.2% inference accuracy improvement with limited privacy revealing for datasets stored in local clients, and reduces 88.9 % time complexity over vertical baseline method.
Text Attribute Control via Closed-Loop Disentanglement

Abstract Changing an attribute of a text without changing the content usually requires first disentangling the text into irrelevant attributes and content representations. After that, in the inference phase, the representation of one attribute is tuned to a different value, expecting that the corresponding attribute of the text can also be changed accordingly. The usual way of disentanglement is to add some constraints on the latent space of an encoder-decoder architecture, including adversarial-based constraints and mutual-information-based constraints. However, previous semi-supervised processes of attribute change are usually not enough to guarantee the success of attribute change and content preservation. In this paper, we propose a novel approach to achieve a robust control of attributes while enhancing content preservation. In this approach, we use a semi-supervised contrastive learning method to encourage the disentanglement of attributes in latent spaces. Differently from previous works, we re-disentangle the reconstructed sentence and compare the re-disentangled latent space with the original latent space, which makes a closed-loop disentanglement process. This also helps content preservation. In addition, the contrastive learning method is also able to replace the role of minimizing mutual information and adversarial training in the disentanglement process, which alleviates the computation cost. We conducted experiments on three text datasets, including the Yelp Service review dataset, the Amazon Product review dataset, and the GoEmotions dataset. The experimental results show the effectiveness of our model.
Directions of Curvature as an Explanation for Loss of Plasticity

Loss of plasticity is a phenomenon in which neural networks lose their ability to learn from new experience. Despite being empirically observed in several problem settings, little is understood about the mechanisms that lead to loss of plasticity. In this paper, we offer a consistent explanation for loss of plasticity: Neural networks lose directions of curvature during training and that loss of plasticity can be attributed to this reduction in curvature. To support such a claim, we provide a systematic investigation of loss of plasticity across continual learning tasks using MNIST, CIFAR-10 and ImageNet. Our findings illustrate that loss of curvature directions coincides with loss of plasticity, while also showing that previous explanations are insufficient to explain loss of plasticity in all settings. Lastly, we show that regularizers which mitigate loss of plasticity also preserve curvature, motivating a simple distributional regularizer that proves to be effective across the problem settings we considered.
Multi-Axis and Multi-Vector Gradient Estimations: Using Multi-Sampled Complex Unit Vectors to Estimate Gradients of Real Functions

In this preliminary study, we provide two methods for estimating the gradients of functions of real value. Both methods are built on derivative estimations that are calculated using the standard method or the Squire-Trapp method for any given direction. Gradients are computed as the average of derivatives in uniformly sampled directions. The first method uses a uniformly distributed set of axes that consists of orthogonal unit vectors that span the space. The second method only uses a uniformly distributed set of unit vectors. Both methods essentially minimize the error through an average of estimations to cancel error terms. Both methods are essentially a conceptual generalization of the method used to estimate normal fractal surfaces.
Binary perceptrons capacity via fully lifted random duality theory

We study the statistical capacity of the classical binary perceptrons with general thresholds $\kappa$. After recognizing the connection between the capacity and the bilinearly indexed (bli) random processes, we utilize a recent progress in studying such processes to characterize the capacity. In particular, we rely on \emph{fully lifted} random duality theory (fl RDT) established in \cite{Stojnicflrdt23} to create a general framework for studying the perceptrons' capacities. Successful underlying numerical evaluations are required for the framework (and ultimately the entire fl RDT machinery) to become fully practically operational. We present results obtained in that directions and uncover that the capacity characterizations are achieved on the second (first non-trivial) level of \emph{stationarized} full lifting. The obtained results \emph{exactly} match the replica symmetry breaking predictions obtained through statistical physics replica methods in \cite{KraMez89}. Most notably, for the famous zero-threshold scenario, $\kappa=0$, we uncover the well known $\alpha\approx0.8330786$ scaled capacity.
DREAM: Diffusion Rectification and Estimation-Adaptive Models

We present DREAM, a novel training framework representing Diffusion Rectification and Estimation Adaptive Models, requiring minimal code changes (just three lines) yet significantly enhancing the alignment of training with sampling in diffusion models. DREAM features two components: diffusion rectification, which adjusts training to reflect the sampling process, and estimation adaptation, which balances perception against distortion. When applied to image super-resolution (SR), DREAM adeptly navigates the tradeoff between minimizing distortion and preserving high image quality. Experiments demonstrate DREAM's superiority over standard diffusion-based SR methods, showing a $2$ to $3\times $ faster training convergence and a $10$ to $20\times$ reduction in sampling steps to achieve comparable results. We hope DREAM will inspire a rethinking of diffusion model training paradigms.
Enhancing Explainability in Mobility Data Science through a combination of methods

In the domain of Mobility Data Science, the intricate task of interpreting models trained on trajectory data, and elucidating the spatio-temporal movement of entities, has persistently posed significant challenges. Conventional XAI techniques, although brimming with potential, frequently overlook the distinct structure and nuances inherent within trajectory data. Observing this deficiency, we introduced a comprehensive framework that harmonizes pivotal XAI techniques: LIME (Local Interpretable Model-agnostic Explanations), SHAP (SHapley Additive exPlanations), Saliency maps, attention mechanisms, direct trajectory visualization, and Permutation Feature Importance (PFI). Unlike conventional strategies that deploy these methods singularly, our unified approach capitalizes on the collective efficacy of these techniques, yielding deeper and more granular insights for models reliant on trajectory data. In crafting this synthesis, we effectively address the multifaceted essence of trajectories, achieving not only amplified interpretability but also a nuanced, contextually rich comprehension of model decisions. To validate and enhance our framework, we undertook a survey to gauge preferences and reception among various user demographics. Our findings underscored a dichotomy: professionals with academic orientations, particularly those in roles like Data Scientist, IT Expert, and ML Engineer, showcased a profound, technical understanding and often exhibited a predilection for amalgamated methods for interpretability. Conversely, end-users or individuals less acquainted with AI and Data Science showcased simpler inclinations, such as bar plots indicating timestep significance or visual depictions pinpointing pivotal segments of a vessel's trajectory.
HeTriNet: Heterogeneous Graph Triplet Attention Network for Drug-Target-Disease Interaction

Modeling the interactions between drugs, targets, and diseases is paramount in drug discovery and has significant implications for precision medicine and personalized treatments. Current approaches frequently consider drug-target or drug-disease interactions individually, ignoring the interdependencies among all three entities. Within human metabolic systems, drugs interact with protein targets in cells, influencing target activities and subsequently impacting biological pathways to promote healthy functions and treat diseases. Moving beyond binary relationships and exploring tighter triple relationships is essential to understanding drugs' mechanism of action (MoAs). Moreover, identifying the heterogeneity of drugs, targets, and diseases, along with their distinct characteristics, is critical to model these complex interactions appropriately. To address these challenges, we effectively model the interconnectedness of all entities in a heterogeneous graph and develop a novel Heterogeneous Graph Triplet Attention Network (\texttt{HeTriNet}). \texttt{HeTriNet} introduces a novel triplet attention mechanism within this heterogeneous graph structure. Beyond pairwise attention as the importance of an entity for the other one, we define triplet attention to model the importance of pairs for entities in the drug-target-disease triplet prediction problem. Experimental results on real-world datasets show that \texttt{HeTriNet} outperforms several baselines, demonstrating its remarkable proficiency in uncovering novel drug-target-disease relationships.
Japanese Tort-case Dataset for Rationale-supported Legal Judgment Prediction

This paper presents the first dataset for Japanese Legal Judgment Prediction (LJP), the Japanese Tort-case Dataset (JTD), which features two tasks: tort prediction and its rationale extraction. The rationale extraction task identifies the court's accepting arguments from alleged arguments by plaintiffs and defendants, which is a novel task in the field. JTD is constructed based on annotated 3,477 Japanese Civil Code judgments by 41 legal experts, resulting in 7,978 instances with 59,697 of their alleged arguments from the involved parties. Our baseline experiments show the feasibility of the proposed two tasks, and our error analysis by legal experts identifies sources of errors and suggests future directions of the LJP research.
Tree-based Forecasting of Day-ahead Solar Power Generation from Granular Meteorological Features

Accurate forecasts for day-ahead photovoltaic (PV) power generation are crucial to support a high PV penetration rate in the local electricity grid and to assure stability in the grid. We use state-of-the-art tree-based machine learning methods to produce such forecasts and, unlike previous studies, we hereby account for (i) the effects various meteorological as well as astronomical features have on PV power production, and this (ii) at coarse as well as granular spatial locations. To this end, we use data from Belgium and forecast day-ahead PV power production at an hourly resolution. The insights from our study can assist utilities, decision-makers, and other stakeholders in optimizing grid operations, economic dispatch, and in facilitating the integration of distributed PV power into the electricity grid.
The Efficiency Spectrum of Large Language Models: An Algorithmic Survey

The rapid growth of Large Language Models (LLMs) has been a driving force in transforming various domains, reshaping the artificial general intelligence landscape. However, the increasing computational and memory demands of these models present substantial challenges, hindering both academic research and practical applications. To address these issues, a wide array of methods, including both algorithmic and hardware solutions, have been developed to enhance the efficiency of LLMs. This survey delivers a comprehensive review of algorithmic advancements aimed at improving LLM efficiency. Unlike other surveys that typically focus on specific areas such as training or model compression, this paper examines the multi-faceted dimensions of efficiency essential for the end-to-end algorithmic development of LLMs. Specifically, it covers various topics related to efficiency, including scaling laws, data utilization, architectural innovations, training and tuning strategies, and inference techniques. This paper aims to serve as a valuable resource for researchers and practitioners, laying the groundwork for future innovations in this critical research area. Our repository of relevant references is maintained at url{https://github.com/tding1/Efficient-LLM-Survey}.
On-demand single-electron source via single-cycle acoustic pulses

Surface acoustic waves (SAWs) are a reliable solution to transport single electrons with precision in piezoelectric semiconductor devices. Recently, highly efficient single-electron transport with a strongly compressed single-cycle acoustic pulse has been demonstrated. This approach, however, requires surface gates constituting the quantum dots, their wiring, and multiple gate movements to load and unload the electrons, which is very time-consuming. Here, on the contrary, we employ such a single-cycle acoustic pulse in a much simpler way - without any quantum dot at the entrance or exit of a transport channel - to perform single-electron transport between distant electron reservoirs. We observe the transport of a solitary electron in a single-cycle acoustic pulse via the appearance of the quantized acousto-electric current. The simplicity of our approach allows for on-demand electron emission with arbitrary delays on a ns time scale. We anticipate that enhanced synthesis of the SAWs will facilitate electron-quantum-optics experiments with multiple electron flying qubits.
Liouville theory and the Weil-Petersson geometry of moduli space: bordered, conic, and higher genus surfaces

Two-dimensional conformal field theory is a powerful tool to understand the geometry of surfaces. Here, we study Liouville conformal field theory in the classical (large central charge) limit, where it encodes the geometry of the moduli space of Riemann surfaces. Generalizing previous work, we employ this to study moduli spaces of higher genus surfaces, surfaces with boundaries, and surfaces with cone points. In each case, the knowledge of classical conformal blocks provides an extremely efficient approximation to the Weil-Petersson metric on moduli space. We find detailed agreement with analytic results for volumes and geodesic lengths on moduli space.