Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal and Adaptive Non-Stationary Dueling Bandits Under a Generalized Borda Criterion

In dueling bandits, the learner receives preference feedback between arms, and the regret of an arm is defined in terms of its suboptimality to a winner arm. The more challenging and practically motivated non-stationary variant of dueling bandits, where preferences change over time, has been the focus of several recent works (Saha and Gupta, 2022; Buening and Saha, 2023; Suk and Agarwal, 2023). The goal is to design algorithms without foreknowledge of the amount of change. The bulk of known results here studies the Condorcet winner setting, where an arm preferred over any other exists at all times. Yet, such a winner may not exist and, to contrast, the Borda version of this problem (which is always well-defined) has received little attention. In this work, we establish the first optimal and adaptive Borda dynamic regret upper bound, which highlights fundamental differences in the learnability of severe non-stationarity between Condorcet vs. Borda regret objectives in dueling bandits. Surprisingly, our techniques for non-stationary Borda dueling bandits also yield improved rates within the Condorcet winner setting, and reveal new preference models where tighter notions of non-stationarity are adaptively learnable. This is accomplished through a novel generalized Borda score framework which unites the Borda and Condorcet problems, thus allowing reduction of Condorcet regret to a Borda-like task. Such a generalization was not previously known and is likely to be of independent interest.
Epidemiology of thrombocytopenia in HIV inlection

Abstract: Thrombocytopenia is a known complication of human immunodeficiency virus Type‐I (HIV‐1) infection, and more data need to be collected on its frequency, severity, and clinical sequelae. We determined the frequency of thrombocytopenia and its relationship to other HIV infection characteristics from a review of records of 1004 HIV‐infected patients attending two outpatient clinics in Washington, D.C. The self‐reported sources of HIV‐1 exposure were male homosexual activity (68j, bisexual activity (10%), heterosexual activity (6%), and intravenous drug use (15%). Fifty‐nine percent of the individuals were white, 37% were black and 94% were male. Fifteen percent had AIDS. Thrombocytopenia occurred more frequently in subjects with AIDS (21.2%) than in HIV‐infected individuals who did not fit clinical criteria for AIDS (9.2%) (p<0.001). Patients with few CD4‐positive cells and an advanced stage of disease were more likely to have low platelet counts: 30% with an absolute CD4 cell count lower than 200/mm3vs 8 % with CD4 counts between 200 and 500 (p<0.00001), and 18.5yo with Stage IV disease compared to 7.6% in Stage I1 (p< 0.001) had platelet counts less than 150000/mm3. Thrombocytopenia was more frequent in white males and older subjects. Although subjects infected by heterosexual exposure had a lower frequency of thrombocytopenia, intravenous drug users and homosexual men exhibited similar frequencies of thrombocytopenia. Of all subjects with platelet counts less than 50000/mm3, 40% reported bleeding and 1 died of an intracranial hemorrhage. Thrombocytopenia occurs frequently in HIV‐infected people, primarily in those with AIDS, low CD4 cell numbers, and advanced stages of diseases
Evaluating Text to Image Synthesis: Survey and Taxonomy of Image Quality Metrics

Recent advances in text-to-image synthesis have been enabled by exploiting a combination of language and vision through foundation models. These models are pre-trained on tremendous amounts of text-image pairs sourced from the World Wide Web or other large-scale databases. As the demand for high-quality image generation shifts towards ensuring content alignment between text and image, novel evaluation metrics have been developed with the aim of mimicking human judgments. Thus, researchers have started to collect datasets with increasingly complex annotations to study the compositionality of vision-language models and their incorporation as a quality measure of compositional alignment between text and image contents. In this work, we provide a comprehensive overview of existing text-to-image evaluation metrics and propose a new taxonomy for categorizing these metrics. We also review frequently adopted text-image benchmark datasets before discussing techniques to optimize text-to-image synthesis models towards quality and human preferences. Ultimately, we derive guidelines for improving text-to-image evaluation and discuss the open challenges and current limitations.
FutureDepth: Learning to Predict the Future Improves Video Depth Estimation

In this paper, we propose a novel video depth estimation approach, FutureDepth, which enables the model to implicitly leverage multi-frame and motion cues to improve depth estimation by making it learn to predict the future at training. More specifically, we propose a future prediction network, F-Net, which takes the features of multiple consecutive frames and is trained to predict multi-frame features one time step ahead iteratively. In this way, F-Net learns the underlying motion and correspondence information, and we incorporate its features into the depth decoding process. Additionally, to enrich the learning of multiframe correspondence cues, we further leverage a reconstruction network, R-Net, which is trained via adaptively masked auto-encoding of multiframe feature volumes. At inference time, both F-Net and R-Net are used to produce queries to work with the depth decoder, as well as a final refinement network. Through extensive experiments on several benchmarks, i.e., NYUDv2, KITTI, DDAD, and Sintel, which cover indoor, driving, and open-domain scenarios, we show that FutureDepth significantly improves upon baseline models, outperforms existing video depth estimation methods, and sets new state-of-the-art (SOTA) accuracy. Furthermore, FutureDepth is more efficient than existing SOTA video depth estimation models and has similar latencies when comparing to monocular models
FLRW Cosmology in Myrzakulov $F(R,Q)$ Gravity

In the present work, we investigate some exact cosmological models in the Myrzakulov $F(R,Q)$ gravity or the Myrzakulov gravity-II (MG-II) which was proposed in [arXiv:1205.5266]. Here $R$ and $Q$ are the curvature and nonmetricity scalars using non-special connection, respectively. We have solved field equations in two different contexts using a flat FLRW metric. We have obtained two exact solutions in the form of scale factor $a(t)$, and using this scale factor, we have derived other cosmological parameters. After that using recent observational datasets $H(z)$ and Pantheon SNe Ia, we have obtained best fit constrained values of model parameters by applying the MCMC analysis. Using these best fit values of model parameters, we have discussed the results behaviour of the derived models. We have found that both models are transit phase model and approaches to $\Lambda$CDM model at late-time universe. We have found that the geometrical sector dark equation of state $\omega_{(geom)}$ behaves just like dark energy candidate. Also, we have estimated the transition redshift $z_{t}$ and present age of the universe $t_{0}$ which are consistent with recent observations.
State-Separated SARSA: A Practical Sequential Decision-Making Algorithm with Recovering Rewards

While many multi-armed bandit algorithms assume that rewards for all arms are constant across rounds, this assumption does not hold in many real-world scenarios. This paper considers the setting of recovering bandits (Pike-Burke&Grunewalder, 2019), where the reward depends on the number of rounds elapsed since the last time an arm was pulled. We propose a new reinforcement learning (RL) algorithm tailored to this setting, named the State-Separate SARSA (SS-SARSA) algorithm, which treats rounds as states. The SS-SARSA algorithm achieves efficient learning by reducing the number of state combinations required for Q-learning/SARSA, which often suffers from combinatorial issues for large-scale RL problems. Additionally, it makes minimal assumptions about the reward structure and offers lower computational complexity. Furthermore, we prove asymptotic convergence to an optimal policy under mild assumptions. Simulation studies demonstrate the superior performance of our algorithm across various settings.
High-Fidelity SLAM Using Gaussian Splatting with Rendering-Guided Densification and Regularized Optimization

We propose a dense RGBD SLAM system based on 3D Gaussian Splatting that provides metrically accurate pose tracking and visually realistic reconstruction. To this end, we first propose a Gaussian densification strategy based on the rendering loss to map unobserved areas and refine reobserved areas. Second, we introduce extra regularization parameters to alleviate the forgetting problem in the continuous mapping problem, where parameters tend to overfit the latest frame and result in decreasing rendering quality for previous frames. Both mapping and tracking are performed with Gaussian parameters by minimizing re-rendering loss in a differentiable way. Compared to recent neural and concurrently developed gaussian splatting RGBD SLAM baselines, our method achieves state-of-the-art results on the synthetic dataset Replica and competitive results on the real-world dataset TUM.
Quantification of the variation due to laboratory and physiologic sources in CD4 lymphocyte counts of clinically stable HIV-infected individuals.

We have conducted a study to quantify the amount of variation in the CD4 lymphocyte counts of HIV-infected individuals due to laboratory and physiological factors. Thirty HIV-infected male volunteers had blood drawn on six occasions: three times in each of 2 weeks, 4 weeks apart. Two tubes of blood were drawn at each visit, and duplicate measurements were obtained from one of the tubes of blood. Differences between duplicate measurements from a single tube of blood and between CD4 counts obtained from two tubes of blood drawn on the same day were attributed to laboratory factors. Differences between CD4 counts obtained on different days were attributed to a combination of laboratory factors and physiologic factors, which included the effects of exercise, tobacco, and the consumption of alcohol and caffeine. The mean absolute CD4 count at the first visit was 450 (range 86-1,081). The short-term coefficient of variation of CD4 count was 13.7 (95% CI: 12.9, 14.6). Physiologic and laboratory factors accounted for 85% and 15% of the variation in CD4 counts, respectively. Variation in the absolute white blood cell count, lymphocyte percentage, and CD4 percentage accounted fo 52%, 29%, and 19% of the physiologic variation in CD4 counts, respectively. Our results confirm a high degree of short-term variability of CD4 counts among HIV-infected individuals, which can be largely attributed to physiological factors. This variability can be minimized more effectively by repeating CD4 counts over time than by repeating measurements at a single visit. (ABSTRACT TRUNCATED AT 250 WORDS)
Treatment of allograft vasculopathy in heart transplantation

Cardiac allograft vasculopathy remains one of the main causes of morbidity and mortality after heart transplantation, although its impact is becoming somewhat smaller as prophylactic measures are implemented. Advances in the understanding of the molecular and cellular mechanisms involved in the genesis and development of cardiac allograft vasculopathy are opening ways for new diagnostic and therapeutic strategies. Successful prophylaxis of the early stages of the disease has been demonstrated with the use of newer immunosuppressive agents, such as sirolimus and everolimus, that will probably be included in future protocols. For most patients with established cardiac allograft vasculopathy, currently available revascularisation methods and retransplantation are not appropriate options. Antiproliferative agents could provide significant improvement in terms of symptom relief and prognosis, but their definite value must be proven in well-designed trials.
FairSIN: Achieving Fairness in Graph Neural Networks through Sensitive Information Neutralization

Despite the remarkable success of graph neural networks (GNNs) in modeling graph-structured data, like other machine learning models, GNNs are also susceptible to making biased predictions based on sensitive attributes, such as race and gender. For fairness consideration, recent state-of-the-art (SOTA) methods propose to filter out sensitive information from inputs or representations, e.g., edge dropping or feature masking. However, we argue that such filtering-based strategies may also filter out some non-sensitive feature information, leading to a sub-optimal trade-off between predictive performance and fairness. To address this issue, we unveil an innovative neutralization-based paradigm, where additional Fairness-facilitating Features (F3) are incorporated into node features or representations before message passing. The F3 are expected to statistically neutralize the sensitive bias in node representations and provide additional nonsensitive information. We also provide theoretical explanations for our rationale, concluding that F3 can be realized by emphasizing the features of each node’s heterogeneous neighbors (neighbors with different sensitive attributes). We name our method as FairSIN, and present three implementation variants from both data-centric and model-centric perspectives. Experimental results on five benchmark datasets with three different GNN backbones show that FairSIN significantly improves fairness metrics while maintaining high prediction accuracies. Codes and appendix can be found at https://github.com/BUPT-GAMMA/FariSIN.
Fast algorithms for slew constrained minimum cost buffering

As a prevalent constraint, sharp slew rate is often required in circuit design which causes a huge demand for buffering resources. This problem requires ultra-fast buffering techniques to handle large volume of nets, while also minimizing buffering cost. This problem is intensively studied in this paper. First, a highly efficient algorithm based on dynamic programming is proposed to optimally solve slew buffering with discrete buffer locations. Second, a new algorithm is developed to handle the difficult cases in which no assumption is made on buffer input slew. Third, an adaptive buffer selection approach is proposed to efficiently handle slew buffering with continuous buffer locations. Experiments on industrial netlists demonstrate that our algorithms are very effective and highly efficient: we achieve > 100times speed up and save up to 40% buffer area over the commonly-used van Ginneken style buffering
Expansion of Polyfunctional HIV-Specific T Cells upon Stimulation with mRNA Electroporated Dendritic Cells in the Presence of Immunomodulatory Drugs

ABSTRACT Recently, it has been demonstrated that disease progression during HIV infection is not determined merely by the number of HIV-specific T cells but also by their quality (J. R. Almeida, et al., J. Exp. Med. 204:2473–2485, 2007; C. T. Berger, et al., J. Virol. 85:9334–9345, 2011; M. R. Betts, et al., Blood 107:4781–4789, 2006; V. V. Ganusov, et al., J. Virol. 85:10518–10528, 2011; P. Kiepiela, et al., Nat. Med. 13:46–53, 2007; and F. Pereyra, et al., J. Infect. Dis. 197:563–571, 2008). Therefore, strategies to specifically enhance or induce high-quality, HIV-specific T-cell responses are necessary to develop effective immune therapies. Thalidomide, lenalidomide, and pomalidomide have a strong capacity to boost immune responses and are therefore referred to as immunomodulatory drugs (IMiDs). We evaluated the effects of lenalidomide and pomalidomide on HIV-specific T cells. We found that the presence of IMiDs during in vitro T-cell stimulation with dendritic cells electroporated with Gag- or Nef-encoding mRNA resulted in higher numbers of cytokine-secreting HIV-specific CD8+ T cells, particularly inducing polyfunctional HIV-specific CD8+ T cells with an enhanced lytic capacity. Furthermore, CD8+ T-cell responses were detected upon stimulation with lower antigenic peptide concentrations, and a higher number of Gag epitopes was recognized upon addition of IMiDs. Finally, IMiDs reduced the proliferation of the HIV-specific CD4+ T cells while increasing the number of polyfunctional CD4+ T cells. These results provide new information about the effects of IMiDs on antigen-specific T cells and suggest that these drugs increase the efficacy of immune therapies for infectious diseases and cancer.
One family of dark-bright solitons with striking width differences

Most of previously reported dark-bright solitons admit identical width for the two components in both theoretical and experimental studies. We report dark-bright solitons can admit strikingly different widths, and derive a family of analytical solutions for them by Lagrangian variational method. The existence regimes for these solitons become much more widespread in the space of nonlinear parameters, than the ones for the previously known dark-bright solitons with identical width. Our analysis indicates that the effective quantum wells are quite different in the two components, in sharp contrast to the ones for all previously known vector solitons. Especially, the particle number of bright soliton can be used to control the generation of dark-bright solitons with varied ratios of solitons' widths. Based on the current experimental technologies, we propose an experimental scheme for observing these novel dark-bright solitons. The results suggest that abundant vector solitons with difference widths exist in multi-components coupled systems, and would inspire experiments to observe them in nonlinear optical fibers, Bose-Einstein condensates, and other nonlinear coupled systems.
Water Aging Effects on Graphene Nanoplatelets and Multi-walled Carbon Nanotube Reinforced Epoxy Glass Fiber Nanocomposites

Nanocomposites reinforced with hybrid fillers of multi-walled carbon nanotubes (MWCNTs) and graphene nanoplatelets (GNPs) were developed, aimed at improving electrical and morphological properties of the hybrid nanocomposites while reducing the cost of the final product. GNPs and MWCNTs nanofillers have shown improved electrical and morphological properties with most polymers. In this work, the effect of short-term water aging for 1440 h on MCWNTs and GNPs reinforced epoxy glass fiber nanocomposites was studied. Epoxy nanocomposites were prepared with varying combinations of MCWNTs and GNPs (2:1, 2:2, and 2:3wt. %) as conducting fillers and their electrical conductivity was evaluated after short-term water aging. It was shown that the addition of MCWNTs and GNPs enhanced the electrical conductivity of composites: A low percolation threshold was achieved with 2 wt. % MCWNTs and 3 wt. % GNPs. The addition of MCWNTs enhanced the electrical conductivity and dielectric constant, confirming the synergistic effect of CNTs as multifunctional filler. Microstructural investigations and morphology of nanocomposites were investigated using Fourier transform-infrared spectroscopy and X-ray diffraction. The novelty of this work arises from the combination of two conducting fillers with different geometry and aspect ratios as well as different dispersion characteristics. The results obtained from the electrical measurements after water aging on hybrid nanocomposites indicates slight increases in electrical conductivity.
Knowledge Based and Intelligent Information and Engineering Systems , KES 2016 , 5-7 September 2016 , York , United Kingdom Approximate matching in ACSM dissimilarity measure

The paper introduces a new patch-based dissimilarity measure for image comparison employing an approximation strategy. It extends the Average Common Sub-matrix measure computing the exact dissimilarity among images. In the exact method, dissimilarity between two images is obtained by considering the average area of the biggest square sub-matrices in common between the images, by exact match of the extracted sub-matrices pixel by pixel. As an extension, the proposed dissimilarity measure computes an approximate match between the sub-matrices, which is obtained by omitting a controlled number of pixels at a given column offset inside the sub-matrices. The proposed dissimilarity measure is extensively compared with other well-known approximate methods for image comparison in the state-of-the-art. Experiments demonstrate the superiority of the proposed approximate measure in terms of execution time with respect to the exact method, and in terms of retrieval precision with respect to the other state-of-the-art methods. c © 2016 The Authors. Published by Elsevier B.V. Peer-review under responsibility of KES International.
Multiple-binding-site mechanism explains concentration-dependent unbinding rates of DNA-binding proteins

Recent work has demonstrated concentration-dependent unbinding rates of proteins from DNA, using fluorescence visualization of the bacterial nucleoid protein Fis [Graham et al. (2011) (Concentration-dependent exchange accelerates turnover of proteins bound to double-stranded DNA. Nucleic Acids Res., 39:2249)]. The physical origin of this concentration-dependence is unexplained. We use a combination of coarse-grained simulation and theory to demonstrate that this behavior can be explained by taking into account the dimeric nature of the protein, which permits partial dissociation and exchange with other proteins in solution. Concentration-dependent unbinding is generated by this simple model, quantitatively explaining experimental data. This effect is likely to play a major role in determining binding lifetimes of proteins in vivo where there are very high concentrations of solvated molecules.
Small Scale Reflection for the Working Lean User

We present the design and implementation of the Small Scale Reflection proof methodology and tactic language (a.k.a. SSR) for the Lean 4 proof assistant. Like its Coq predecessor SSReflect, our Lean 4 implementation, dubbed LeanSSR, provides powerful rewriting principles and means for effective management of hypotheses in the proof context. Unlike SSReflect for Coq, LeanSSR does not require explicit switching between the logical and symbolic representation of a goal, allowing for even more concise proof scripts that seamlessly combine deduction steps with proofs by computation. In this paper, we first provide a gentle introduction to the principles of structuring mechanised proofs using LeanSSR. Next, we show how the native support for metaprogramming in Lean 4 makes it possible to develop LeanSSR entirely within the proof assistant, greatly improving the overall experience of both tactic implementers and proof engineers. Finally, we demonstrate the utility of LeanSSR by conducting two case studies: (a) porting a collection of Coq lemmas about sequences from the widely used Mathematical Components library and (b) reimplementing proofs in the finite set library of Lean's mathlib4. Both case studies show significant reduction in proof sizes.