Miracle-dz/UniFAR-FTUs huggingface dataset:
Thermal Performance of a Blend System Based on Poly(l-lactic acid) and an Aliphatic Multiamide Derivative of 1H-Benzotriazole

ABSTRACT An aliphatic multiamide derivative derived from 1H-benzotriazole, N, N'-bis(1H-benzotriazole) sebacic acid acethydrazide (SA), was synthesized to evaluate its effect on the thermal performance, including non-isothermal crystallization and melting behavior as well as thermal stability, of poly(l-lactic acid) (PLLA). The comparative study, by means of DSC measurements, showed that the incorporation of SA caused a non-isothermal crystallization peak to appear and become sharp, showing its advanced crystallization promoting effect for PLLA. The non-isothermal crystallization results further indicated that 2 wt% SA was the saturation concentration for PLLA crystallization, and that the cooling rate was also a crucial determinant for PLLA crystallization. Considering the melting behavior, the difference between the virgin PLLA and PLLA/2%SA further confirmed the crystallization accelerative effect of SA for PLLA, with the increase of crystallization temperature in the temperature zone from 90 to 130°C being beneficial to the crystallization of PLLA during processing. Compared to the virgin PLLA, the trends of thermal decomposition curves were similar, suggesting that the introduction of SA of 0.5–3 wt% did not significantly change the thermal decomposition behavior of PLLA.
The Supersonic Project: SIGOs, A Proposed Progenitor to Globular Clusters, and Their Connections to Gravitational-wave Anisotropies

Supersonically induced gas objects (SIGOs), are structures with little to no dark-matter component predicted to exist in regions of the universe with large relative velocities between baryons and dark matter at the time of recombination. They have been suggested to be the progenitors of present-day globular clusters. Using simulations, SIGOs have been studied on small scales (around 2 Mpc) where these relative velocities are coherent. However, it is challenging to study SIGOs using simulations on large scales due to the varying relative velocities at scales larger than a few Mpc. Here, we study SIGO abundances semi-analytically: using perturbation theory, we predict the number density of SIGOs analytically, and compare these results to small-box numerical simulations. We use the agreement between the numerical and analytic calculations to extrapolate the large-scale variation of SIGO abundances over different stream velocities. As a result, we predict similar large-scale variations of objects with high gas densities before reionization that could possibly be observed by JWST. If indeed SIGOs are progenitors of globular clusters, then we expect a similar variation of globular cluster abundances over large scales. Significantly, we find that the expected number density of SIGOs is consistent with observed globular cluster number densities. As a proof-of-concept, and because globular clusters were proposed to be natural formation sites for gravitational wave sources from binary black-hole mergers, we show that SIGOs should imprint an anisotropy on the gravitational wave signal on the sky, consistent with their distribution.
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text

We present a framework for learning multimodal representations from unlabeled data using convolution-free Transformer architectures. Specifically, our Video-Audio-Text Transformer (VATT) takes raw signals as inputs and extracts multimodal representations that are rich enough to benefit a variety of downstream tasks. We train VATT end-to-end from scratch using multimodal contrastive losses and evaluate its performance by the downstream tasks of video action recognition, audio event classification, image classification, and text-to-video retrieval. Furthermore, we study a modality-agnostic, single-backbone Transformer by sharing weights among the three modalities. We show that the convolution-free VATT outperforms state-of-the-art ConvNet-based architectures in the downstream tasks. Especially, VATT's vision Transformer achieves the top-1 accuracy of 82.1% on Kinetics-400, 83.6% on Kinetics-600, 72.7% on Kinetics-700, and 41.1% on Moments in Time, new records while avoiding supervised pre-training. Transferring to image classification leads to 78.7% top-1 accuracy on ImageNet compared to 64.7% by training the same Transformer from scratch, showing the generalizability of our model despite the domain gap between videos and images. VATT's audio Transformer also sets a new record on waveform-based audio event recognition by achieving the mAP of 39.4% on AudioSet without any supervised pre-training. VATT's source code is publicly available.
Color Transparency and the Proton Form Factor- Feynman Wins

A recent experiment [Phys. Rev. Lett. 126,082301 (2021)] used the (e, e′p) reaction on C to search for the effects of color transparency (the absence of final state interactions). Color transparency was said to be ruled out. The ability to observe the effects of color transparency depends on the ability of a putative point-like-configuration (PLC), formed in a high-momentum transfer coherent reaction, to escape the nucleus without expanding its size. We study the expansion aspect of color transparency using superconformal baryon-meson symmetry and light-front holographic QCD. A new formalism is obtained and used to analyze the recent experiment. The resulting conclusion is that effects of expansion would not be sufficiently significant in causing final state interactions to occur. Therefore we conclude that a PLC was not formed. This means that the Feynman mechanism involving virtual photon absorption on a single high momentum quark is responsible for high momentum electromagnetic form factor of the proton.
ImageNet-21K Pretraining for the Masses

ImageNet-1K serves as the primary dataset for pretraining deep learning models for computer vision tasks. ImageNet-21K dataset, which is bigger and more diverse, is used less frequently for pretraining, mainly due to its complexity, low accessibility, and underestimation of its added value. This paper aims to close this gap, and make high-quality efficient pretraining on ImageNet-21K available for everyone. Via a dedicated preprocessing stage, utilization of WordNet hierarchical structure, and a novel training scheme called semantic softmax, we show that various models significantly benefit from ImageNet-21K pretraining on numerous datasets and tasks, including small mobile-oriented models. We also show that we outperform previous ImageNet-21K pretraining schemes for prominent new models like ViT and Mixer. Our proposed pretraining pipeline is efficient, accessible, and leads to SoTA reproducible results, from a publicly available dataset. The training code and pretrained models are available at: https://github.com/Alibaba-MIIL/ImageNet21K
Barriers and Opportunities to Accessible Social Media Content Authoring

User-generated content plays a key role in social networking, allowing a more active participation, socialisation, and collaboration among users. In particular, media content has been gaining a lot of ground, allowing users to express themselves through different types of formats such as images, GIFs and videos. The majority of this growing type of online content remains inaccessible to a part of the population, despite available tools to mitigate this source of exclusion. We sought to understand how people are perceiving these online contents in their networks and how to support tools are being used. To do so, we performed an online survey of 258 social network users and a follow-up interview conducted with 20 of them - 7 of them self-reporting blind and 13 sighted users without a disability. Results show how the different approaches being employed by major platforms are still not sufficient to properly address this issue. Our findings reveal that mainstream users are not aware of the possibility and the benefits of adopting accessible practices. From the general perspectives of end-users experiencing accessible practices, concerning barriers encountered, and motivational factors, we also discuss further approaches to create more user engagement and awareness.
Near-optimal intense and powerful terahertz source by optical rectification in lithium niobate crystal

Using an affordable ytterbium laser with sub-mJ of energy combined with a novel pulse compression technique, we demonstrate an extremely competitive state-of-the-art terahertz (THz) source with 53 mW of average power and 310 kV/cm at focus from the tilted-pulse front pumping scheme in lithium niobate at room temperature. Key points of this demonstration include the use of a pump pulse duration of 280 fs in combination with an echelon mirror. Our results present unmatched combined characteristics and are highly competitive with the existing THz sources pumped at the mJ range. This demonstration is a step towards the democratization of access to intense and powerful THz pulses.
Scaling of Neural‐Network Quantum States for Time Evolution

Simulating quantum many‐body dynamics on classical computers is a challenging problem due to the exponential growth of the Hilbert space. Artificial neural networks have recently been introduced as a new tool to approximate quantum many‐body states. The variational power of the restricted Boltzmann machine quantum states and different shallow and deep neural autoregressive quantum states to simulate the global quench dynamics of a non‐integrable quantum Ising chain is benchmarked. It is found that the number of parameters required to represent the quantum state at a given accuracy increases exponentially in time. The growth rate is only slightly affected by the network architecture over a wide range of different design choices: shallow and deep networks, small and large filter sizes, dilated and normal convolutions, and with and without shortcut connections.
Misinformation, believability, and vaccine acceptance over 40 countries: Takeaways from the initial phase of the COVID-19 infodemic

The COVID-19 pandemic has been damaging to the lives of people all around the world. Accompanied by the pandemic is an infodemic, an abundant and uncontrolled spread of potentially harmful misinformation. The infodemic may severely change the pandemic’s course by interfering with public health interventions such as wearing masks, social distancing, and vaccination. In particular, the impact of the infodemic on vaccination is critical because it holds the key to reverting to pre-pandemic normalcy. This paper presents findings from a global survey on the extent of worldwide exposure to the COVID-19 infodemic, assesses different populations’ susceptibility to false claims, and analyzes its association with vaccine acceptance. Based on responses gathered from over 18,400 individuals from 40 countries, we find a strong association between perceived believability of COVID-19 misinformation and vaccination hesitancy. Our study shows that only half of the online users exposed to rumors might have seen corresponding fact-checked information. Moreover, depending on the country, between 6% and 37% of individuals considered these rumors believable. A key finding of this research is that poorer regions were more susceptible to encountering and believing COVID-19 misinformation; countries with lower gross domestic product (GDP) per capita showed a substantially higher prevalence of misinformation. We discuss implications of our findings to public campaigns that proactively spread accurate information to countries that are more susceptible to the infodemic. We also defend that fact-checking platforms should prioritize claims that not only have wide exposure but are also perceived to be believable. Our findings give insights into how to successfully handle risk communication during the initial phase of a future pandemic.
Uniqueness and stability for the solution of a nonlinear least squares problem

In this paper, we focus on the nonlinear least squares: $\mbox{min}_{\mathbf{x} \in \mathbb{H}^d}\| |A\mathbf{x}|-\mathbf{b}\|$ where $A\in \mathbb{H}^{m\times d}$, $\mathbf{b} \in \mathbb{R}^m$ with $\mathbb{H} \in \{\mathbb{R},\mathbb{C} \}$ and consider the uniqueness and stability of solutions. Such problem arises, for instance, in phase retrieval and absolute value rectification neural networks. For the case where $\mathbf{b}=|A\mathbf{x}_0|$ for some $\mathbf{x}_0\in \mathbb{H}^d$, many results have been developed to characterize the uniqueness and stability of solutions. However, for the case where $\mathbf{b} \neq |A\mathbf{x}_0| $ for any $\mathbf{x}_0\in \mathbb{H}^d$, there is no existing result for it to the best of our knowledge. In this paper, we first focus on the uniqueness of solutions and show for any matrix $A\in \mathbb{H}^{m \times d}$ there always exists a vector $\mathbf{b} \in \mathbb{R}^m$ such that the solution is not unique. But, in real case, such ``bad'' vectors $\mathbf{b}$ are negligible, namely, if $\mathbf{b} \in \mathbb{R}_{+}^m$ does not lie in some measure zero set, then the solution is unique. We also present some conditions under which the solution is unique. For the stability of solutions, we prove that the solution is never uniformly stable. But if we restrict the vectors $\mathbf{b}$ to any convex set then it is stable.
An Accurate and Efficient Large-Scale Regression Method Through Best Friend Clustering

As the data size in Machine Learning fields grows exponentially, it is inevitable to accelerate the computation by utilizing the ever-growing large number of available cores provided by high-performance computing hardware. However, existing parallel methods for clustering or regression often suffer from problems of low accuracy, slow convergence, and complex hyperparameter-tuning. Furthermore, the parallel efficiency is usually difficult to improve while striking a balance between preserving model properties and partitioning computing workloads on distributed systems. In this article, we propose a novel and simple data structure capturing the most important information among data samples. It has several advantageous properties supporting a hierarchical clustering strategy that contains well-defined metrics for determining optimal hierarchy, balanced partition for maintaining the clustering property, and efficient parallelization for accelerating computation phases. Then we combine the clustering with regression techniques as a parallel library and utilize a hybrid structure of data and model parallelism to make predictions. Experiments illustrate that our library obtains remarkable performance on convergence, accuracy, and scalability.
Predictive Mutation Analysis via the Natural Language Channel in Source Code

Mutation analysis can provide valuable insights into both the system under test and its test suite. However, it is not scalable due to the cost of building and testing a large number of mutants. Predictive Mutation Testing (PMT) has been proposed to reduce the cost of mutation testing, but it can only provide statistical inference about whether a mutant will be killed or not by the entire test suite. We propose Seshat, a Predictive Mutation Analysis (PMA) technique that can accurately predict the entire kill matrix, not just the Mutation Score (MS) of the given test suite. Seshat exploits the natural language channel in code, and learns the relationship between the syntactic and semantic concepts of each test case and the mutants it can kill, from a given kill matrix. The learnt model can later be used to predict the kill matrices for subsequent versions of the program, even after both the source and test code have changed significantly. Empirical evaluation using the programs in Defects4J shows that Seshat can predict kill matrices with an average F-score of 0.83 for versions that are up to years apart. This is an improvement in F-score by 0.14 and 0.45 points over the state-of-the-art PMT technique and a simple coverage-based heuristic, respectively. Seshat also performs as well as PMT for the prediction of the MS only. When applied to a mutant-based fault localisation technique, the predicted kill matrix by Seshat is successfully used to locate faults within the top 10 position, showing its usefulness beyond prediction of MS. Once Seshat trains its model using a concrete mutation analysis, the subsequent predictions made by Seshat are on average 39 times faster than actual test-based analysis. We also show that Seshat can be successfully applied to automatically generated test cases with an experiment using EvoSuite.
Spatial patterns of a transient photon condensate inside a microcavity

We show that a photon condensate inside a dye-filled microcavity forms long-lived spatial patterns when incoherently excited by a focused pump orbiting around the cavity axis. The discrete rotational symmetry of the patterns is controlled by the orbital frequency of the pump spot and despite the absence of any effective photon-photon interactions the condensate is phase-coherent over its full spatial extent.
Precision matrix estimation under the horseshoe-like prior–penalty dual

Precision matrix estimation in a multivariate Gaussian model is fundamental to network estimation. Although there exist both Bayesian and frequentist approaches to this, it is difficult to obtain good Bayesian and frequentist properties under the same prior--penalty dual. To bridge this gap, our contribution is a novel prior--penalty dual that closely approximates the graphical horseshoe prior and penalty, and performs well in both Bayesian and frequentist senses. A chief difficulty with the horseshoe prior is a lack of closed form expression of the density function, which we overcome in this article. In terms of theory, we establish posterior convergence rate of the precision matrix that matches the oracle rate, in addition to the frequentist consistency of the MAP estimator. In addition, our results also provide theoretical justifications for previously developed approaches that have been unexplored so far, e.g. for the graphical horseshoe prior. Computationally efficient EM and MCMC algorithms are developed respectively for the penalized likelihood and fully Bayesian estimation problems. In numerical experiments, the horseshoe-based approaches echo their superior theoretical properties by comprehensively outperforming the competing methods. A protein--protein interaction network estimation in B-cell lymphoma is considered to validate the proposed methodology.
Trajectory Optimization and Resource Allocation for OFDMA UAV Relay Networks

In this paper, we consider a single-cell multi-user orthogonal frequency division multiple access (OFDMA) network with one unmanned aerial vehicle (UAV), which works as an amplify-and-forward relay to improve the quality-of-service (QoS) of the user equipments (UEs) in the cell edge. Aiming to improve the throughput while guaranteeing the user fairness, we jointly optimize the communication mode, subchannel allocation, power allocation, and UAV trajectory, which is an NP-hard problem. To design the UAV trajectory and resource allocation efficiently, we first decompose the problem into three subproblems, i.e., mode selection and subchannel allocation, trajectory optimization, and power allocation, and then solve these subproblems iteratively. Simulation results show that the proposed algorithm outperforms the random algorithm and the cellular scheme.
Variational Bayesian Supertrees

Given overlapping subsets of a set of taxa (e.g. species), and posterior distributions on phylogenetic tree topologies for each of these taxon sets, how can we infer a posterior distribution on phylogenetic tree topologies for the entire taxon set? Although the equivalent problem for in the nonBayesian case has attracted substantial research, the Bayesian case has not attracted the attention it deserves. In this paper we develop a variational Bayes approach to this problem and demonstrate its effectiveness.
Compact bilinear operators on asymmetric normed spaces

The paper is concerned with compact bilinear operators on asymmetric normed spaces. One proves a Schauder type theorem on the compactness of the conjugate of a compact bilinear operator and one studies the ideal properties of spaces of compact bilinear operators. These extend some results of Ramanujan and Schock, Linear and Multilinear Algebra (1985), and Ruch, ibid. (1989), on compact bilinear operators on Banach spaces. The study of multilinear operators on asymmetric normed spaces was initiated by Latreche and Dahia, Colloq. Math. (2020). MSC 2020 46G25, 47H60, 46B50, 54E35
Localization of Ice-Rink for Broadcast Hockey Videos

In this work, an automatic and simple framework for hockey ice-rink localization from broadcast videos is introduced. First, video is broken into video-shots by a hierarchical partitioning of the video frames, and thresholding based on their histograms. To localize the frames on the ice-rink model, a ResNet18-based regressor is implemented and trained, which regresses to four control points on the model in a frame-by-frame fashion. This leads to the projection jittering problem in the video. To overcome this, in the inference phase, the trajectory of the control points on the ice-rink model are smoothed, for all the consecutive frames of a given video-shot, by convolving a Hann window with the achieved coordinates. Finally, the smoothed homography matrix is computed by using the direct linear transform on the four pairs of corresponding points. A hockey dataset for training and testing the regressor is gathered. The results show success of this simple and comprehensive procedure for localizing the hockey ice-rink and addressing the problem of jittering without affecting the accuracy of homography estimation.
Bunching ∗

Recent years have seen a surge of applied work using bunching approaches, a development that is closely linked to the increased availability of administrative data. These approaches exploit the incentives for bunching created by discontinuities in the slope of choice sets (kinks) or in the level of choice sets (notches) to study the behavior of individuals and firms. While the bunching approach was originally developed in the context of taxation, it is beginning to find applications in many other areas such as social security, social insurance, welfare programs, education, regulation, private sector prices, and reference-dependent preferences. This review provides a guide to bunching estimation, discusses its strengths and weaknesses, surveys a range of applications across fields, and considers reasons for the ubiquity of kinks and notches. ∗Prepared for the Annual Review of Economics, Volume 8, 2016.
Magnetospheric chaos and dynamical complexity response during storm time disturbance

Abstract. In this study, we examine the magnetospheric chaos and dynamical complexity response to the disturbance storm time (Dst) and solar wind electric field (VBs) during different categories of geomagnetic storm (minor, moderate and major geomagnetic storm). The time series data of the Dst and VBs are analysed for a period of 9 years using non-linear dynamics tools (maximal Lyapunov exponent, MLE; approximate entropy, ApEn; and delay vector variance, DVV). We found a significant trend between each non-linear parameter and the categories of geomagnetic storm. The MLE and ApEn values of the Dst indicate that chaotic and dynamical complexity responses are high during minor geomagnetic storms, reduce at moderate geomagnetic storms and decline further during major geomagnetic storms. However, the MLE and ApEn values obtained from VBs indicate that chaotic and dynamical complexity responses are high with no significant difference between the periods that are associated with minor, moderate and major geomagnetic storms. The test for non-linearity in the Dst time series during major geomagnetic storm reveals the strongest non-linearity features. Based on these findings, the dynamical features obtained in the VBs as input and Dst as output of the magnetospheric system suggest that the magnetospheric dynamics are non-linear, and the solar wind dynamics are consistently stochastic in nature.