Miracle-dz/UniFAR-FTUs huggingface dataset:
A Field Test of Bandit Algorithms for Recommendations: Understanding the Validity of Assumptions on Human Preferences in Multi-armed Bandits

Personalized recommender systems suffuse modern life, shaping what media we read and what products we consume. Algorithms powering such systems tend to consist of supervised-learning-based heuristics, such as latent factor models with a variety of heuristically chosen prediction targets. Meanwhile, theoretical treatments of recommendation frequently address the decision-theoretic nature of the problem, including the need to balance exploration and exploitation, via the multi-armed bandits (MABs) framework. However, MAB-based approaches rely heavily on assumptions about human preferences. These preference assumptions are seldom tested using human subject studies, partly due to the lack of publicly available toolkits to conduct such studies. In this work, we conduct a study with crowdworkers in a comics recommendation MABs setting. Each arm represents a comic category, and users provide feedback after each recommendation. We check the validity of core MABs assumptions—that human preferences (reward distributions) are fixed over time—and find that they do not hold. This finding suggests that any MAB algorithm used for recommender systems should account for human preference dynamics. While answering these questions, we provide a flexible experimental framework for understanding human preference dynamics and testing MABs algorithms with human users. The code for our experimental framework and the collected data can be found at https://github.com/HumainLab/human-bandit-evaluation.
Neural Lumped Parameter Differential Equations with Application in Friction-Stir Processing

Lumped parameter methods aim to simplify the evolution of spatially-extended or continuous physical systems to that of a"lumped"element representative of the physical scales of the modeled system. For systems where the definition of a lumped element or its associated physics may be unknown, modeling tasks may be restricted to full-fidelity simulations of the physics of a system. In this work, we consider data-driven modeling tasks with limited point-wise measurements of otherwise continuous systems. We build upon the notion of the Universal Differential Equation (UDE) to construct data-driven models for reducing dynamics to that of a lumped parameter and inferring its properties. The flexibility of UDEs allow for composing various known physical priors suitable for application-specific modeling tasks, including lumped parameter methods. The motivating example for this work is the plunge and dwell stages for friction-stir welding; specifically, (i) mapping power input into the tool to a point-measurement of temperature and (ii) using this learned mapping for process control.
Stability of Baroclinic Vortices on the β Plane and Implications for Transport

AbstractThis paper explores stability of and transport by baroclinic vortices on the β plane using a two-layer, quasigeostrophic model. The study adapts a wave–mean flow formalism and examines interactions between the axisymmetric flow (“the vortex”) and residuals (“the waves”). Unlike baroclinically unstable vortices on the f plane, such vortices on the β plane can be also vulnerable to barotropic instability as revealed by the globally integrated energy balance analysis. The spatial structure of energy fluxes shows the energy leakage inside the vortex core when its breakdown occurs. Mixing by stable and unstable vortical flows is quantified through the computation of finite-time Lyapunov exponent (FTLE) maps. Depending on the strength of wave radiation, the upper-layer FTLE maps of stable vortices show either an annulus or volute ring of vigorous mixing inside the vortex interior. This ring region is disrupted when the vortex becomes unstable. Both stable and unstable vortices show the wavy patterns of .
UN ESTRATO DE LAS MATRICES NORMALES A STRATA OF NORMAL MATRICES

A strata for normal matrices is presented in this article. This requires aspects from three branches of mathematics: Differential geometry, differential topology and matrix theory. The first one studies geometrical aspects of mathematical analysis. The second one studies topology aspects of the first one, and the third branch faces the study of matrices from diverse mathematical contexts.
Independence testing for inhomogeneous random graphs

Testing for independence between graphs is a problem that arises naturally in social network analysis and neuroscience. In this paper, we address independence testing for inhomogeneous Erd\H{o}s-R\'{e}nyi random graphs on the same vertex set. We first formulate a notion of pairwise correlations between the edges of these graphs and derive a necessary condition for their detectability. We next show that the problem can exhibit a statistical vs. computational tradeoff, i.e., there are regimes for which the correlations are statistically detectable but may require algorithms whose running time is exponential in n, the number of vertices. Finally, we consider a special case of correlation testing when the graphs are sampled from a latent space model (graphon) and propose an asymptotically valid and consistent test procedure that also runs in time polynomial in n.
Robust Control Barrier Functions with Uncertainty Estimation

This paper proposes a safety controller for control-affine nonlinear systems with unmodelled dynamics and disturbances to improve closed-loop robustness. Uncertainty estimation-based control barrier functions (CBFs) are utilized to ensure robust safety in the presence of model uncertainties, which may depend on control input and states. We present a new uncertainty/disturbance estimator with theoretical upper bounds on estimation error and estimated outputs, which are used to ensure robust safety by formulating a convex optimization problem using a high-order CBF. The possibly unsafe nominal feedback controller is augmented with the proposed estimator in two frameworks (1) an uncertainty compensator and (2) a robustifying reformulation of CBF constraint with respect to the estimator outputs. The former scheme ensures safety with performance improvement by adaptively rejecting the matched uncertainty. The second method uses uncertainty estimation to robustify higher-order CBFs for safety-critical control. The proposed methods are demonstrated in simulations of an uncertain adaptive cruise control problem and a multirotor obstacle avoidance situation.
MER 2023: Multi-label Learning, Modality Robustness, and Semi-Supervised Learning

The first Multimodal Emotion Recognition Challenge (MER 2023)1 was successfully held at ACM Multimedia. The challenge focuses on system robustness and consists of three distinct tracks: (1) MER-MULTI, where participants are required to recognize both discrete and dimensional emotions; (2) MER-NOISE, in which noise is added to test videos for modality robustness evaluation; (3) MER-SEMI, which provides a large amount of unlabeled samples for semi-supervised learning. In this paper, we introduce the motivation behind this challenge, describe the benchmark dataset, and provide some statistics about participants. To continue using this dataset after MER 2023, please sign a new End User License Agreement2 and send it to our official email address3. We believe this high-quality dataset can become a new benchmark in multimodal emotion recognition, especially for the Chinese research community.
Bridging Discrete and Backpropagation: Straight-Through and Beyond

Backpropagation, the cornerstone of deep learning, is limited to computing gradients for continuous variables. This limitation poses challenges for problems involving discrete latent variables. To address this issue, we propose a novel approach to approximate the gradient of parameters involved in generating discrete latent variables. First, we examine the widely used Straight-Through (ST) heuristic and demonstrate that it works as a first-order approximation of the gradient. Guided by our findings, we propose ReinMax, which achieves second-order accuracy by integrating Heun's method, a second-order numerical method for solving ODEs. ReinMax does not require Hessian or other second-order derivatives, thus having negligible computation overheads. Extensive experimental results on various tasks demonstrate the superiority of ReinMax over the state of the art. Implementations are released at https://github.com/microsoft/ReinMax.
On the Interdependence of Reliance Behavior and Accuracy in AI-Assisted Decision-Making

In AI-assisted decision-making, a central promise of putting a human in the loop is that they should be able to complement the AI system by adhering to its correct and overriding its mistaken recommendations. In practice, however, we often see that humans tend to over- or under-rely on AI recommendations, meaning that they either adhere to wrong or override correct recommendations. Such reliance behavior is detrimental to decision-making accuracy. In this work, we articulate and analyze the interdependence between reliance behavior and accuracy in AI-assisted decision-making, which has been largely neglected in prior work. We also propose a visual framework to make this interdependence more tangible. This framework helps us interpret and compare empirical findings, as well as obtain a nuanced understanding of the effects of interventions (e.g., explanations) in AI-assisted decision-making. Finally, we infer several interesting properties from the framework: (i) when humans under-rely on AI recommendations, there may be no possibility for them to complement the AI in terms of decision-making accuracy; (ii) when humans cannot discern correct and wrong AI recommendations, no such improvement can be expected either; (iii) interventions may lead to an increase in decision-making accuracy that is solely driven by an increase in humans' adherence to AI recommendations, without any ability to discern correct and wrong. Our work emphasizes the importance of measuring and reporting both effects on accuracy and reliance behavior when empirically assessing interventions.
Sliced optimal transport on the sphere

Sliced optimal transport reduces optimal transport on multi-dimensional domains to transport on the line. More precisely, sliced optimal transport is the concatenation of the well-known Radon transform and the cumulative density transform, which analytically yields the solutions of the reduced transport problems. Inspired by this concept, we propose two adaptions for optimal transport on the 2-sphere. Firstly, as counterpart to the Radon transform, we introduce the vertical slice transform, which integrates along all circles orthogonal to a given direction. Secondly, we introduce a semicircle transform, which integrates along all half great circles with an appropriate weight function. Both transforms are generalized to arbitrary measures on the sphere. While the vertical slice transform can be combined with optimal transport on the interval and leads to a sliced Wasserstein distance restricted to even probability measures, the semicircle transform is related to optimal transport on the circle and results in a different sliced Wasserstein distance for arbitrary probability measures. The applicability of both novel sliced optimal transport concepts on the sphere is demonstrated by proof-of-concept examples dealing with the interpolation and classification of spherical probability measures. The numerical implementation relies on the singular value decompositions of both transforms and fast Fourier techniques. For the inversion with respect to probability measures, we propose the minimization of an entropy-regularized Kullback–Leibler divergence, which can be numerically realized using a primal-dual proximal splitting algorithm.
Align Your Latents: High-Resolution Video Synthesis with Latent Diffusion Models

Latent Diffusion Models (LDMs) enable high-quality image synthesis while avoiding excessive compute demands by training a diffusion model in a compressed lower-dimensional latent space. Here, we apply the LDM paradigm to high-resolution video generation, a particularly resource-intensive task. We first pre-train an LDM on images only; then, we turn the image generator into a video generator by introducing a temporal dimension to the latent space diffusion model and finetuning on encoded image sequences, i.e., videos. Similarly, we temporally align diffusion model upsamplers, turning them into temporally consistent video super resolution models. We focus on two relevant real-world applications: Simulation of in-the-wild driving data and creative content creation with text-to-video modeling. In particular, we validate our Video LDM on real driving videos of resolution $512 \times 1024$, achieving state-of-the-art performance. Furthermore, our approach can easily leverage off-the-shelf pretrained image LDMs, as we only need to train a temporal alignment model in that case. Doing so, we turn the publicly available, state-of-the-art text-to-image LDM Stable Diffusion into an efficient and expressive text-to-video model with resolution up to $1280 \times 2048$. We show that the temporal layers trained in this way generalize to different finetuned text-to-image LDMs. Utilizing this property, we show the first results for personalized text-to-video generation, opening exciting directions for future content creation. Project page: https://nv-tlabs.github. io/VideoLDM/
Understand Data Preprocessing for Effective End-to-End Training of Deep Neural Networks

In this paper, we primarily focus on understanding the data preprocessing pipeline for DNN Training in the public cloud. First, we run experiments to test the performance implications of the two major data preprocessing methods using either raw data or record files. The preliminary results show that data preprocessing is a clear bottleneck, even with the most efficient software and hardware configuration enabled by NVIDIA DALI, a high-optimized data preprocessing library. Second, we identify the potential causes, exercise a variety of optimization methods, and present their pros and cons. We hope this work will shed light on the new co-design of ``data storage, loading pipeline'' and ``training framework'' and flexible resource configurations between them so that the resources can be fully exploited and performance can be maximized.
Collective Dynamics, Diversification and Optimal Portfolio Construction for Cryptocurrencies

Since its conception, the cryptocurrency market has been frequently described as an immature market, characterized by significant swings in volatility and occasionally described as lacking rhyme or reason. There has been great speculation as to what role it plays in a diversified portfolio. For instance, is cryptocurrency exposure an inflationary hedge or a speculative investment that follows broad market sentiment with amplified beta? We have recently explored similar questions with a clear focus on the equity market. There, our research revealed several noteworthy dynamics such as an increase in the market’s collective strength and uniformity during crises, greater diversification benefits across equity sectors (rather than within them), and the existence of a “best value” portfolio of equities. In essence, we can now contrast any potential signatures of maturity we identify in the cryptocurrency market and contrast these with the substantially larger, older and better-established equity market. This paper aims to investigate whether the cryptocurrency market has recently exhibited similar mathematical properties as the equity market. Instead of relying on traditional portfolio theory, which is grounded in the financial dynamics of equity securities, we adjust our experimental focus to capture the presumed behavioral purchasing patterns of retail cryptocurrency investors. Our focus is on collective dynamics and portfolio diversification in the cryptocurrency market, and examining whether previously established results in the equity market hold in the cryptocurrency market and to what extent. The results reveal nuanced signatures of maturity related to the equity market, including the fact that correlations collectively spike around exchange collapses, and identify an ideal portfolio size and spread across different groups of cryptocurrencies.
Sequential mutations in exponentially growing populations

Stochastic models of sequential mutation acquisition are widely used to quantify cancer and bacterial evolution. Across manifold scenarios, recurrent research questions are: how many cells are there with n alterations, and how long will it take for these cells to appear. For exponentially growing populations, these questions have been tackled only in special cases so far. Here, within a multitype branching process framework, we consider a general mutational path where mutations may be advantageous, neutral or deleterious. In the biologically relevant limiting regimes of large times and small mutation rates, we derive probability distributions for the number, and arrival time, of cells with n mutations. Surprisingly, the two quantities respectively follow Mittag-Leffler and logistic distributions regardless of n or the mutations’ selective effects. Our results provide a rapid method to assess how altering the fundamental division, death, and mutation rates impacts the arrival time, and number, of mutant cells. We highlight consequences for mutation rate inference in fluctuation assays.
Breather and double-pole solutions for the Benjamin-Ono equation in a stratified fluid

The Benjamin–Ono equation is hereby investigated, which arises in the context of long internal gravity waves in a stratified fluid. With the Hirota method and symbolic computation, breather solutions are derived. Propagation of the breather and elastic collisions between the breather and soliton are graphically analyzed. The collision period and the bunch number in a wave packet are relevant to the ratio of the real part to the imaginary of the wavenumber. Through the coalescence of wavenumbers in the two-soliton solutions, we obtain the double-pole solutions.
Ca2+-Permeable TRPV1 Receptor Mediates Neuroprotective Effects in a Mouse Model of Alzheimer’s Disease via BDNF/CREB Signaling Pathway

Transient receptor potential vanilloid 1 (TRPV1) protein is a Ca2+-permeable non-selective cation channel known for its pain modulation pathway. In a previous study, it was discovered that a triple-transgenic Alzheimer’s disease (AD) mouse model (3xTg-AD+/+) has anti-AD effects. The expression of proteins in the brain-derived neurotrophic factor (BDNF)/cAMP response element binding protein (CREB) pathway in a 3xTg-AD/TRPV1 transgenic mice model was investigated to better understand the AD regulatory effect of TRPV1 deficiency. The results show that TRPV1 deficiency leads to CREB activation by increasing BDNF levels and promoting phosphorylation of tyrosine receptor kinase B (TrkB), extracellular signal-regulated kinase (ERK), protein kinase B (Akt), and CREB in the hippocampus. Additionally, TRPV1 deficiency-induced CREB activation increases the antiapoptotic factor B-cell lymphoma 2 (Bcl-2) gene, which consequently downregulates Bcl-2-associated X (Bax) expression and decreases cleaved caspase-3 and cleaved poly (ADP-ribose) polymerase (PARP), which leads to the prevention of hippocampal apoptosis. In conclusion, TRPV1 deficiency exhibits neuroprotective effects by preventing apoptosis through the BDNF/CREB signal transduction pathway in the hippocampus of 3xTg-AD mice.
The Effect of Adding Curcumin to Sertraline in the Treatment of Severe Major Depressive Disorder: A Randomized, Double-Blind Clinical Trial

Objectives Major depressive disorder (MDD) is a chronic and debilitating disease influenced by inflammatory processes in the brain. Some evidence has represented the adding curcumin as a complementary regime to the standard medication in treating depressive symptoms. However, limited clinical trials have been conducted on the antidepressants effects of curcumin in MDD patients. Therefore, this study aimed to investigate the effectiveness of curcumin in the treatment of MDD. Methods In a randomized, double-blind clinical trial, 45 severe MDD patients referred to the psychiatric clinic of Ibn-e-Sina Hospital, Mashhad, Iran, during 2016 were selected. Patients were randomly divided into 2 groups who received sertraline plus curcumin or placebo at a dose of 40 mg/d for 8 weeks. The patients were evaluated using Beck Anxiety and Depression Surveys at the beginning of the study, fourth, and eighth weeks by a psychiatry resident. The data analyzed aiding SPSS software. Results While depression and anxiety significantly decreased during the 8 weeks of the study, there was no significant difference between the 2 groups (P > 0.05). However, the anxiety score was lower in the intervention group. Moreover, no severe adverse events were observed in all patients. Conclusions Adding 40 mg/d of SinaCurcumin to sertraline as a routine medical regimen did not improve the depression and anxiety levels in severe MDD patients. However, the anxiety score was lower in the intervention group than in the placebo receiver, which suggests curcumin may have a more effect on anxiety.
Evaluating the effect of sleep quality and chronotype differences on erectile dysfunction.

OBJECTIVE Erectile dysfunction (ED) refers to an inability to achieve or maintain a firm penile erection sufficient for satisfactory sexual intercourse. Insufficient, irregular sleep and sleep disorders adversely affect human health, including sexual function. Significant differences between biological rhythms (chronotypes) have been reported. In the present study we examine the effect of sleep quality and chronotype differences on ED patients and a control group. PATIENTS AND METHODS The study included 69 patients who presented with ED and 64 healthy controls. The respondents completed a sociodemographic data form, and disease severity in the ED group was measured using the International Index of Erectile Function (IIEF). The participants were further administered the Hospital Anxiety and Depression Scale (HADS), Insomnia Severity Index (ISI), Pittsburgh Sleep Quality Index (PSQI) and Morningness-Eveningness Questionnaire (MEQ), and the scale scores were compared statistically between the patient and control groups. RESULTS There was no difference in the age, body mass index (BMI), alcohol use and smoking of the ED and healthy control groups, while the IIEF score was significantly lower in the ED group than in the control group. The PSQI subscale scores other than for sleep duration subscale, the PSQI global score and the HADS score were higher in the ED group than in the control group, while there was no difference between the groups in the MEQ and ISI scores. The IIEF score was correlated with the PSQI and HADS scores, and the PSQI score with the ISI and HADS scores. CONCLUSIONS It would be useful to evaluate sleep quality in addition to anxiety and depression while evaluating patients with ED. Our study found no relationship between chronotype differences and ED.