Miracle-dz/UniFAR-FTUs huggingface dataset:
Who can help me? Reconstructing users' psychological journeys in depression-related social media interactions

Social media are increasingly being used as self-help boards, where individuals can disclose personal experiences and feelings and look for support from peers or experts. Here we investigate several popular mental health-related Reddit boards about depression while proposing a novel psycho-social framework. We reconstruct users' psychological/linguistic profiles together with their social interactions. We cover a total of 303,016 users, engaging in 378,483 posts and 1,475,044 comments from 01/05/2018 to 01/05/2020. After identifying a network of users' interactions, e.g., who replied to whom, we open an unprecedented window over psycholinguistic, cognitive, and affective digital traces with relevance for mental health research. Through user-generated content, we identify four categories or archetypes of users in agreement with the Patient Health Engagement model: the emotionally turbulent/under blackout, the aroused, the adherent-yet-conflicted, and the eudaimonically hopeful. Analyzing users' transitions over time through conditional Markov processes, we show how these four archetypes are not consecutive stages. We do not find a linear progression or sequential patient journey, where users evolve from struggling to serenity through feelings of conflict. Instead, we find online users to follow spirals towards both negative and positive archetypal stages. Through psychological/linguistic and social network modelling, we can provide compelling quantitative pieces of evidence on how such a complex path unfolds through positive, negative, and conflicting online contexts. Our approach opens the way to data-informed understandings of psychological coping with mental health issues through social media.
Design of variable acceptance sampling plan for exponential distribution under uncertainty

In an acceptance monitoring system, acceptance sampling techniques are used to increase production, enhance control, and deliver higher-quality products at a lesser cost. It might not always be possible to define the acceptance sampling plan parameters as exact values, especially, when data has uncertainty. In this work, acceptance sampling plans for a large number of identical units with exponential lifetimes are obtained by treating acceptable quality life, rejectable quality life, consumer's risk, and producer's risk as fuzzy parameters. To obtain plan parameters of sequential sampling plans and repetitive group sampling plans, fuzzy hypothesis test is considered. To validate the sampling plans obtained in this work, some examples are presented. Our results are compared with existing results in the literature. Finally, to demonstrate the application of the resulting sampling plans, a real-life case study is presented.
Topological quantum criticality from multiplicative topological phases

Symmetry-protected topological phases (SPTs) characterized by short-range entanglement include many states essential to understanding of topological condensed matter physics, and the extension to gapless SPTs provides essential understanding of their consequences. In this work, we identify a fundamental connection between gapless SPTs and recently-introduced multiplicative topological phases, demonstrating that multiplicative topological phases are an intuitive and general approach to realizing concrete models for gapless SPTs. In particular, they are naturally well-suited to realizing higher-dimensional, stable, and intrinsic gapless SPTs through combination of canonical topological insulator and semimetal models with critical gapless models in symmetry-protected tensor product constructions, opening avenues to far broader and deeper investigation of topology via short-range entanglement.
SpeechAct: Towards Generating Whole-body Motion from Speech

This paper addresses the problem of generating whole-body motion from speech. Despite great successes, prior methods still struggle to produce reasonable and diverse whole-body motions from speech. This is due to their reliance on suboptimal representations and a lack of strategies for generating diverse results. To address these challenges, we present a novel hybrid point representation to achieve accurate and continuous motion generation, e.g., avoiding foot skating, and this representation can be transformed into an easy-to-use representation, i.e., SMPL-X body mesh, for many applications. To generate whole-body motion from speech, for facial motion, closely tied to the audio signal, we introduce an encoder-decoder architecture to achieve deterministic outcomes. However, for the body and hands, which have weaker connections to the audio signal, we aim to generate diverse yet reasonable motions. To boost diversity in motion generation, we propose a contrastive motion learning method to encourage the model to produce more distinctive representations. Specifically, we design a robust VQ-VAE to learn a quantized motion codebook using our hybrid representation. Then, we regress the motion representation from the audio signal by a translation model employing our contrastive motion learning method. Experimental results validate the superior performance and the correctness of our model. The project page is available for research purposes at http://cic.tju. edu.cn/faculty/likun/projects/SpeechAct.
Melting curve of black phosphorus: evidence for a solid-liquid-liquid triple point

Black phosphorus (bP) is a crystalline material that can be seen as ordered stackings of two-dimensional layers, which lead to outstanding anisotropic physical properties. The knowledge of its pressure-temperature (P-T) phase diagram, and in particular, the slope and location of its melting curve is fundamental for better understanding the synthesis and stability conditions of this important material. Despite several experimental studies, important uncertainties remain in the determination of this melting curve. Here we report accurate melting points measurements, using in situ high-temperature and high-pressure high-resolution synchrotron x-ray diffraction. In particular, we have employed an original and accurate pressure and temperature metrology based on the unique anisotropic P-T response of bP, that we used as sensor for the simultaneous determination of pressure and temperature up to 5 GPa and 1700 K. We confirmed the existence of and located a solid-liquid-liquid triple point at the intersection of the low- and high-pressure melting curves. Finally, we have characterized the irreversibility of the transformation in the low-pressure regime below 1 GPa, as the low-density liquid does not crystallize back to bP but into red phosphorus on temperature quenching.
Space-like asymptotics of the thermal two-point functions of the XXZ spin-1/2 chain

This work proposes a closed formula for the leading term of the large-distance and long-time asymptotics in a cone of the space-like regime for the transverse dynamical two-point functions of the XXZ spin 1/2 chain at finite temperatures. The result follows from a simple analysis of the thermal form factor series for dynamical correlation functions. The obtained leading asymptotics are driven by the Bethe Ansatz data associated with the first sub-leading eigenvalue of the quantum transfer matrix.
Pragmatic Radiology Report Generation

When pneumonia is not found on a chest X-ray, should the report describe this negative observation or omit it? We argue that this question cannot be answered from the X-ray alone and requires a pragmatic perspective, which captures the communicative goal that radiology reports serve between radiologists and patients. However, the standard image-to-text formulation for radiology report generation fails to incorporate such pragmatic intents. Following this pragmatic perspective, we demonstrate that the indication, which describes why a patient comes for an X-ray, drives the mentions of negative observations and introduce indications as additional input to report generation. With respect to the output, we develop a framework to identify uninferable information from the image as a source of model hallucinations, and limit them by cleaning groundtruth reports. Finally, we use indications and cleaned groundtruth reports to develop pragmatic models, and show that they outperform existing methods not only in new pragmatics-inspired metrics (+4.3 Negative F1) but also in standard metrics (+6.3 Positive F1 and +11.0 BLEU-2).
The boundary of the geomagnetic field

A three-component magnetometer was carried on Explorer 12, launched on August 16, 1961. The boundary of the geomagnetic field on the sunlit side of the earth was observed. During magnetically quiet periods the boundary, near the earth-sun line, was at approximately 10 earth radii distance. During the period of observation a field of magnitude 30 to 40 gammas existed outside the boundary; it was usually directed downward, or southward, from the ecliptic plane. The boundary was stable, and magnetic fluctuations inside the geomagnetic field were relatively small although often large outside.
Vortex pair dynamics in three-dimensional homogeneous dipolar superfluids

The static and dynamic properties of vortices in dipolar Bose-Einstein condensates (dBECs) can be considerably modified relative to their nondipolar counterparts by the anisotropic and long-ranged nature of the dipole-dipole interaction. Working in a uniform dBEC, we analyze the structure of single vortices and the dynamics of vortex pairs, investigating the deviations from the nondipolar paradigm. For a straight vortex line, we find that the induced dipolar interaction potential is axially anisotropic when the dipole moments have a nonzero projection orthogonal to the vortex line. This results in a corresponding elongation of the vortex core along this projection as well as an anisotropic superfluid phase and enhanced compressibility in the vicinity of the vortex core. Consequently, the trajectories of like-signed vortex pairs are described by a family of elliptical and oval-like curves rather than the familiar circular orbits. Similarly for opposite-signed vortex pairs their translation speeds along the binormal are found to be dipole interaction-dependent. We expect that these findings will shed light on the underlying mechanisms of many-vortex phenomena in dBECs such as quantum turbulence, vortex reconnections, and vortex lattices.
Neural Texture Puppeteer: A Framework for Neural Geometry and Texture Rendering of Articulated Shapes, Enabling Re-Identification at Interactive Speed

In this paper, we present a neural rendering pipeline for textured articulated shapes that we call Neural Texture Puppeteer. Our method separates geometry and texture encoding. The geometry pipeline learns to capture spatial relationships on the surface of the articulated shape from ground truth data that provides this geometric information. A texture auto-encoder makes use of this information to encode textured images into a global latent code. This global texture embedding can be efficiently trained separately from the geometry, and used in a downstream task to identify individuals. The neural texture rendering and the identification of individuals run at interactive speeds. To the best of our knowledge, we are the first to offer a promising alternative to CNN- or transformer-based approaches for re-identification of articulated individuals based on neural rendering. Realistic looking novel view and pose synthesis for different synthetic cow textures further demonstrate the quality of our method. Restricted by the availability of ground truth data for the articulated shape's geometry, the quality for real-world data synthesis is reduced. We further demonstrate the flexibility of our model for real-world data by applying a synthetic to real-world texture domain shift where we reconstruct the texture from a real-world 2D RGB image. Thus, our method can be applied to endangered species where data is limited. Our novel synthetic texture dataset NePuMoo is publicly available to inspire further development in the field of neural rendering-based re-identification.
SODA: Bottleneck Diffusion Models for Representation Learning

We introduce SODA, a self-supervised diffusion model, designed for representation learning. The model incorporates an image encoder, which distills a source view into a compact representation, that, in turn, guides the generation of related novel views. We show that by imposing a tight bottleneck between the encoder and a denoising decoder, and leveraging novel view synthesis as a self-supervised objective, we can turn diffusion models into strong representation learners, capable of capturing visual semantics in an unsupervised manner. To the best of our knowledge, SODA is the first diffusion model to succeed at ImageNet linear-probe classification, and, at the same time, it accomplishes reconstruction, editing and synthesis tasks across a wide range of datasets. Further investigation reveals the disentangled nature of its emergent latent space, that serves as an effective interface to control and manipulate the model's produced images. All in all, we aim to shed light on the exciting and promising potential of diffusion models, not only for image generation, but also for learning rich and robust representations.
Giant amplification of Berezinskii-Kosterlitz-Thouless transition temperature in superconducting systems characterized by cooperative interplay of small-gapped valence and conduction bands

Two-dimensional superconductors and electron-hole superfluids in van der Waals heterostructures having tunable valence and conduction bands in the electronic spectrum are emerging as rich platforms to investigate novel quantum phases and topological phase transitions. In this work, by adopting a mean-field approach considering multiple-channel pairings and the Kosterlitz-Nelson criterion, we demonstrate giant amplifications of the Berezinskii-Kosterlitz-Thouless (BKT) transition temperature and a shrinking of the pseudogap for small energy separations between the conduction and valence bands and small density of carriers in the conduction band. The presence of the holes in the valence band, generated by intra-band and pair-exchange couplings, contributes constructively to the phase stiffness of the total system, adding up to the phase stiffness of the conduction band electrons that is boosted as well, due to the presence of the valence band electrons. This strong cooperative effect avoids the suppression of the BKT transition temperature for low density of carriers, that occurs in single-band superconductors where only the conduction band is present. Thus, we predict that in this regime, multi-band superconducting and superfluid systems with valence and conduction bands can exhibit much larger BKT critical temperatures with respect to single-band and single-condensate systems.
Using Ornstein-Uhlenbeck Process to understand Denoising Diffusion Probabilistic Model and its Noise Schedules

The aim of this short note is to show that Denoising Diffusion Probabilistic Model DDPM, a non-homogeneous discrete-time Markov process, can be represented by a time-homogeneous continuous-time Markov process observed at non-uniformly sampled discrete times. Surprisingly, this continuous-time Markov process is the well-known and well-studied Ornstein-Ohlenbeck (OU) process, which was developed in 1930's for studying Brownian particles in Harmonic potentials. We establish the formal equivalence between DDPM and the OU process using its analytical solution. We further demonstrate that the design problem of the noise scheduler for non-homogeneous DDPM is equivalent to designing observation times for the OU process. We present several heuristic designs for observation times based on principled quantities such as auto-variance and Fisher Information and connect them to ad hoc noise schedules for DDPM. Interestingly, we show that the Fisher-Information-motivated schedule corresponds exactly the cosine schedule, which was developed without any theoretical foundation but is the current state-of-the-art noise schedule.
Robust Correlated Equilibrium: Definition and Computation

We study N-player finite games with costs perturbed due to time-varying disturbances in the underlying system and to that end we propose the concept of Robust Correlated Equilibrium that generalizes the definition of Correlated Equilibrium. Conditions under which the Robust Correlated Equilibrium exists are specified and a decentralized algorithm for learning strategies that are optimal in the sense of Robust Correlated Equilibrium is proposed. The primary contribution of the paper is the convergence analysis of the algorithm and to that end, we propose an extension of the celebrated Blackwell's Approachability theorem to games with costs that are not just time-average as in the original Blackwell's Approachability Theorem but also include time-average of previous algorithm iterates. The designed algorithm is applied to a practical water distribution network with pumps being the controllers and their costs being perturbed by uncertain consumption by consumers. Simulation results show that each controller achieves no regret and empirical distributions converge to the Robust Correlated Equilibrium.
Calendar variation model based on ARIMAX for forecasting sales data with Ramadhan effect

Many economics and business data are compiled each month and are available as monthly time series. Such time series may be subjected to two kinds of calendar effects, namely trading day and holiday effects. The objective of this research is to develop a calendar variation model based on ARIMAX method for forecasting sales data with Ramadhan effect. In most Islamic countries, the trade activities frequently contain calendar variation pattern due to the consumption to certain products usually follows lunar calendar, instead of following common solar calendar. Firstly, this research focuses on the development of model building procedure to find the best calendar variation model. In this ARIMAX method, the deterministic and stochastic trends are examined. The procedure for fitting the ARIMAX model consists of only modeling calendar variation effect, its error, and adaptively combining the calendar variation effect and ARIMA model. This procedure is applied in modeling and forecasting of sales data, i.e. the monthly sales of Moslem boys’ clothes in Indonesia. The results show that the proposed model yields better forecast at out-sample data compared to the Decomposition method and Seasonal ARIMA, and Neural Networks.
Robust Diffusion GAN using Semi-Unbalanced Optimal Transport

Diffusion models, a type of generative model, have demonstrated great potential for synthesizing highly detailed images. By integrating with GAN, advanced diffusion models like DDGAN \citep{xiao2022DDGAN} could approach real-time performance for expansive practical applications. While DDGAN has effectively addressed the challenges of generative modeling, namely producing high-quality samples, covering different data modes, and achieving faster sampling, it remains susceptible to performance drops caused by datasets that are corrupted with outlier samples. This work introduces a robust training technique based on semi-unbalanced optimal transport to mitigate the impact of outliers effectively. Through comprehensive evaluations, we demonstrate that our robust diffusion GAN (RDGAN) outperforms vanilla DDGAN in terms of the aforementioned generative modeling criteria, i.e., image quality, mode coverage of distribution, and inference speed, and exhibits improved robustness when dealing with both clean and corrupted datasets.
Learning Free Terminal Time Optimal Closed-loop Control of Manipulators

This paper presents a novel approach to learning free terminal time closed-loop control for robotic manipulation tasks, enabling dynamic adjustment of task duration and control inputs to enhance performance. We extend the supervised learning approach, namely solving selected optimal open-loop problems and utilizing them as training data for a policy network, to the free terminal time scenario. Three main challenges are addressed in this extension. First, we introduce a marching scheme that enhances the solution quality and increases the success rate of the open-loop solver by gradually refining time discretization. Second, we extend the QRnet in Nakamura-Zimmerer et al. (2021b) to the free terminal time setting to address discontinuity and improve stability at the terminal state. Third, we present a more automated version of the initial value problem (IVP) enhanced sampling method from previous work (Zhang et al., 2022) to adaptively update the training dataset, significantly improving its quality. By integrating these techniques, we develop a closed-loop policy that operates effectively over a broad domain with varying optimal time durations, achieving near globally optimal total costs.
Visual Anagrams: Generating Multi-View Optical Illusions with Diffusion Models

We address the problem of synthesizing multi-view optical illusions: images that change appearance upon a transformation, such as a flip or rotation. We propose a simple, zero-shot method for obtaining these illusions from off-the-shelf text-to-image diffusion models. During the reverse diffusion process, we estimate the noise from different views of a noisy image. We then combine these noise estimates together and denoise the image. A theoretical analysis suggests that this method works precisely for views that can be written as orthogonal transformations, of which permutations are a subset. This leads to the idea of a visual anagram--an image that changes appearance under some rearrangement of pixels. This includes rotations and flips, but also more exotic pixel permutations such as a jigsaw rearrangement. Our approach also naturally extends to illusions with more than two views. We provide both qualitative and quantitative results demonstrating the effectiveness and flexibility of our method. Please see our project webpage for additional visualizations and results: https://dangeng.github. io/visual_anagrams/
Efficient and Scalable Architecture for Multiple-Chip Implementation of Simulated Bifurcation Machines

Ising machines are specialized computers for finding the lowest energy states of Ising spin models, onto which many practical combinatorial optimization problems can be mapped. Simulated bifurcation (SB) is a quantum-inspired parallelizable algorithm for Ising problems that enables scalable multi-chip implementations of Ising machines. However, the computational performance of a previously proposed multi-chip architecture tends to saturate as the number of chips increases for a given problem size because computation and communication are exclusive in the time domain. In this paper, we propose a streaming architecture for multi-chip implementations of SB-based Ising machines with full spin-to-spin connectivity. The data flow in in-chip computation is harmonized with the data flow in inter-chip communication, enabling the computation and communication to overlap and the communication time to be hidden. Systematic experiments demonstrate linear strong scaling of performance up to the vicinity of the ideal communication limit determined only by the latency of chip-to-chip communication. Our eight-FPGA (field-programmable gate array) cluster can compute a 32,768-spin problem with a high pipeline efficiency of 97.9%. The performance of a 79-FPGA cluster for a 100,000-spin problem, projected using a theoretical performance model validated on smaller experimental clusters, is comparable to that of a state-of-the-art 100,000-spin optical Ising machine.