Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantum traces for $\mathrm{SL}_n(\mathbb{C})$: the case $n=3$.

We generalize Bonahon and Wong's $\mathrm{SL}_2(\mathbb{C})$-quantum trace map to the setting of $\mathrm{SL}_3(\mathbb{C})$. More precisely, for each non-zero complex number $q$, we associate to every isotopy class of framed oriented links $K$ in a thickened punctured surface $\mathfrak{S} \times (0, 1)$ a Laurent polynomial $\mathrm{Tr}_\lambda^q(K) = \mathrm{Tr}_\lambda^q(K)(X_i^q)$ in $q$-deformations $X_i^q$ of the Fock-Goncharov coordinates $X_i$ for a higher Teichm\"{u}ller space, depending on the choice of an ideal triangulation $\lambda$ of the surface $\mathfrak{S}$. Along the way, we propose a definition for a $\mathrm{SL}_n(\mathbb{C})$-version of this invariant.
Resolution Limits of Non-Adaptive 20 Questions Search for Multiple Targets

We study the problem of simultaneous search for multiple targets over a multidimensional unit cube and derive fundamental resolution limits of non-adaptive querying procedures using the 20 questions estimation framework. The performance criterion that we consider is the achievable resolution, which is defined as the maximal $L_\infty $ norm between the location vector and its estimated version where the maximization is over all target location vectors. The fundamental resolution limit is defined as the minimal achievable resolution of any non-adaptive query procedure, where each query has binary yes/no answers. We drive non-asymptotic and second-order asymptotic bounds on the minimal achievable resolution, using tools from finite blocklength information theory. Specifically, in the achievability part, we relate the 20 questions problem to data transmission over a multiple access channel, use the information spectrum method by Han and borrow results from finite blocklength analysis for random access channel coding. In the converse part, we relate the 20 questions problem to data transmission over a point-to-point channel and adapt finite blocklength converse results for channel coding. Our results extend the purely first-order asymptotic analyses of Kaspi et al. (ISIT 2015) for the one-dimensional case: we consider channels beyond the binary symmetric channel and derive non-asymptotic and second-order asymptotic bounds on the performance of optimal non-adaptive query procedures.
Acceleration of Tropical Cyclones As a Proxy For Extratropical
Interactions: Synoptic-Scale Patterns and Long-Term Trends

Abstract. It is well known that rapid changes in tropical cyclone motion occur during interaction with extratropical waves. While the translation speed has received much attention in the published literature, acceleration has not. Using a large data sample of Atlantic tropical cyclones, we formally examine the composite synoptic-scale patterns associated with tangential and curvature components of their acceleration. During periods of rapid tangential acceleration, the composite tropical cyclone moves poleward between an upstream trough and downstream ridge of a developing extratropical wavepacket. The two systems subsequently merge in a manner that is consistent with extratropical transition. During rapid curvature acceleration, a prominent downstream ridge promotes recurvature of the tropical cyclone. In contrast, during rapid tangential or curvature deceleration, a ridge is located directly poleward of the tropical cyclone. Locally, this arrangement takes the form of a cyclone-anticyclone vortex pair somewhat akin to a dipole block. On average, the tangential acceleration peaks 18 hours prior to extratropical transition while the curvature acceleration peaks at recurvature. These findings confirm that rapid acceleration of tropical cyclones is mediated by interaction with extratropical baroclinic waves. Furthermore, The tails of the distribution of acceleration and translation speed show a robust reduction over the past 5 decades. We speculate that these trends may reflect the poleward shift and weakening of extratropical Rossby waves.
Interactions of SARS-CoV-2 spike protein and transient receptor potential (TRP) cation channels could explain smell, taste, and/or chemesthesis disorders

A significant subset of patients infected by SARS-CoV-2 presents olfactory, taste, and/or chemesthesis (OTC) disorders (OTCD). These patients recover rapidly, eliminating damage of sensory nerves. Discovering that S protein contains two ankyrin repeat binding motifs (S-ARBMs) and some TRP cation channels, implicated in OTC, have ankyrin repeat domains (TRPs-ARDs), I hypothesized that interaction of S-ARBMs and TRPs-ARDs could dysregulate the function of the latter and thus explains OTCD. Of note, some TRPs-ARDs are expressed in the olfactory epithelium, taste buds, trigeminal neurons in the oronasal cavity and vagal neurons in the trachea/lungs. Furthermore, this hypothesis is supported by studies that have shown: (i) respiratory viruses interact with TRPA1 and TRPV1 on sensory nerves and epithelial cells in the airways, (ii) the respiratory pathophysiology in COVID-19 patients is similar to lungs injuries produced by the sensitization of TRPV1 and TRPV4, and (iii) resolvin D1 and D2 shown to reduce SARS-CoV-2-induced inflammation, directly inhibit TRPA1, TRPV1, TRPV3 and TRPV4. Herein, results of blind dockings of S-ARBMs, 408-RQIAPG-413 (in RBD but distal from the ACE-2 binding region) and 905-RFNGIG-910 (in HR1), into TRPA1, TRPV1 and TRPV4 suggest that S-ARBMs interact with ankyrin repeat 6 of TRPA1 near an active site, and ankyrin repeat 3-4 of TRPV1 near cysteine 258 supposed to be implicated in the formation of inter-subunits disulfide bond. These findings suggest that S-ARBMs affect TRPA1, TRPV1 and TRPV4 function by interfering with channel assembly and trafficking. After an experimental confirmation of these interactions, among possible preventive treatments against COVID-19, the use of pharmacological manipulation (probably inhibition) of TRPs-ARDs to control or mitigate sustained pro-inflammatory response.
On properties of graded rings and graded modules

Let R be a G-graded ring. In this article, we introduce two new concepts on graded rings, namely, weakly graded rings and invertible graded rings, and we discuss the relations between these concepts and several properties of graded rings. Also, we study the concept of weakly crossed products and study some properties defined on weakly crossed product and give the relationship between this new concept and several properties of graded rings. Moreover, in this article, we give a generalization for the concept of graded essential submodules, and introduce the concept of graded semi-uniform modules which is a generalization for the concept of graded uniform modules.
SceneGen: Learning to Generate Realistic Traffic Scenes

We consider the problem of generating realistic traffic scenes automatically. Existing methods typically insert actors into the scene according to a set of hand-crafted heuristics and are limited in their ability to model the true complexity and diversity of real traffic scenes, thus inducing a content gap between synthesized traffic scenes versus real ones. As a result, existing simulators lack the fidelity necessary to train and test self-driving vehicles. To address this limitation, we present SceneGen—a neural autoregressive model of traffic scenes that eschews the need for rules and heuristics. In particular, given the ego-vehicle state and a high definition map of surrounding area, SceneGen inserts actors of various classes into the scene and synthesizes their sizes, orientations, and velocities. We demonstrate on two large-scale datasets SceneGen’s ability to faithfully model distributions of real traffic scenes. Moreover, we show that SceneGen coupled with sensor simulation can be used to train perception models that generalize to the real world.
Detection of Insider Attacks in Distributed Projected Subgradient Algorithms

The gossip-based distributed projected subgradient algorithms (DPS) are widely used to solve decentralized optimization problems in various multi-agent applications, while they are generally vulnerable to data injection attacks by internal malicious agents as each agent locally estimates its descent direction without an authorized supervision. In this work, we explore the application of artificial intelligence (AI) technologies to detect internal attacks. We show that a general neural network is particularly suitable for detecting and localizing the malicious agents, as they can effectively explore nonlinear relationship underlying the collected data. Moreover, we propose to adopt one of the state-of-the-art approaches in decentralized federated learning, i.e., a gossip-based collaborative learning protocol, to facilitate training the neural network models by gossip exchanges. This advanced approach is expected to make our model more robust to challenges with insufficient training data, or mismatched test data. In our simulations, a least-squared problem is considered to verify the feasibility and effectiveness of AI-based methods. Simulation results demonstrate that the proposed AI-based methods are beneficial to improve performance of detecting and localizing malicious agents over score-based methods, and the peer-to-peer neural network model is indeed robust to target issues.
Modeling the in-orbit Background of PolarLight

PolarLight is a gas pixel X-ray polarimeter mounted on a CubeSat, which was launched into a Sun-synchronous orbit in 2018 October. We build a mass model of the whole CubeSat with the Geant4 toolkit to simulate the background induced by the cosmic X-ray background (CXB) and high-energy charged particles in the orbit. The simulated energy spectra and morphologies of event images both suggest that the background measured with PolarLight is dominated by high-energy electrons, with a minor contribution from protons and the CXB. The simulation reveals that, in the energy range 2–8 keV, roughly 28% of background events are caused by energy deposited by a secondary electron with an energy of a few keV, in a physical process identical to the detection of X-rays. Thus, this fraction of the background cannot be discriminated from X-ray events. The background distribution is uneven on the detector plane, with an enhancement near the edges. The edge effect occurs because high-energy electrons tend to produce long tracks, which are discarded by the readout electronics unless energy is partially deposited near the edges. The internal background rate is expected to be around 6 × 10−3 counts s−1 cm−2 at 2–8 keV if an effective particle discrimination algorithm can be applied. This indicates that the internal background should be negligible for future focusing X-ray polarimeters with a focal size of the order of millimeters.
Some cases of Kudla’s modularity conjecture for unitary Shimura varieties

Abstract We use the method of Bruinier–Raum to show that symmetric formal Fourier–Jacobi series, in the cases of norm-Euclidean imaginary quadratic fields, are Hermitian modular forms. Consequently, combining a theorem of Yifeng Liu, we deduce Kudla’s conjecture on the modularity of generating series of special cycles of arbitrary codimension for unitary Shimura varieties defined in these cases.
Minimal Gaussian Curvature Surface

This paper deals with finding surfaces in R which are as close as possible to being flat and span a given contour such that the contour is a geodesic on the sought surface. We look for a surface which minimizes the total Gaussian curvature squared. We show that by a change of coordinates the curvature of the optimal surface is controlled by a PDE which can be reduced to the biharmonic equation with an easy-to-define Dirichlet boundary condition and Neumann boundary condition zero. We then state a system of PDEs for the function whose graph is the optimal surface.
Understanding in Artificial Intelligence

Current Artificial Intelligence (AI) methods, most based on deep learning, have facilitated progress in several fields, including computer vision and natural language understanding. The progress of these AI methods is measured using benchmarks designed to solve challenging tasks, such as visual question answering. A question remains of how much understanding is leveraged by these methods and how appropriate are the current benchmarks to measure understanding capabilities. To answer these questions, we have analysed existing benchmarks and their understanding capabilities, defined by a set of understanding capabilities, and current research streams. We show how progress has been made in benchmark development to measure understanding capabilities of AI methods and we review as well how current methods develop understanding capabilities.
Profiling Software Developers with Process Mining and N-Gram Language Models

Context: Profiling developers is challenging since many factors, such as their skills, experience, development environment and behaviors, may influence a detailed analysis and the delivery of coherent interpretations. Objective: We aim at profiling software developers by mining their software development process. To do so, we performed a controlled experiment where, in the realm of a Python programming contest, a group of developers had the same well-defined set of requirements specifications and a well-defined sprint schedule. Events were collected from the PyCharm IDE, and from the Mooshak automatic jury where subjects checked-in their code. Method: We used n-gram language models and text mining to characterize developers' profiles, and process mining algorithms to discover their overall workflows and extract the correspondent metrics for further evaluation. Results: Findings show that we can clearly characterize with a coherent rationale most developers, and distinguish the top performers from the ones with more challenging behaviors. This approach may lead ultimately to the creation of a catalog of software development process smells. Conclusions: The profile of a developer provides a software project manager a clue for the selection of appropriate tasks he/she should be assigned. With the increasing usage of low and no-code platforms, where coding is automatically generated from an upper abstraction layer, mining developer's actions in the development platforms is a promising approach to early detect not only behaviors but also assess project complexity and model effort.
What Makes Good In-Context Examples for GPT-3?

GPT-3 has attracted lots of attention due to its superior performance across a wide range of NLP tasks, especially with its in-context learning abilities. Despite its success, we found that the empirical results of GPT-3 depend heavily on the choice of in-context examples. In this work, we investigate whether there are more effective strategies for judiciously selecting in-context examples (relative to random sampling) that better leverage GPT-3’s in-context learning capabilities.Inspired by the recent success of leveraging a retrieval module to augment neural networks, we propose to retrieve examples that are semantically-similar to a test query sample to formulate its corresponding prompt. Intuitively, the examples selected with such a strategy may serve as more informative inputs to unleash GPT-3’s power of text generation. We evaluate the proposed approach on several natural language understanding and generation benchmarks, where the retrieval-based prompt selection approach consistently outperforms the random selection baseline. Moreover, it is observed that the sentence encoders fine-tuned on task-related datasets yield even more helpful retrieval results. Notably, significant gains are observed on tasks such as table-to-text generation (44.3% on the ToTTo dataset) and open-domain question answering (45.5% on the NQ dataset).
Low star formation efficiency due to turbulent adiabatic compression in the Taffy bridge

The Taffy system (UGC 12914/15) consists of two massive spiral galaxies that had a head-on collision about 20 Myr ago. It represents an ideal laboratory for studying the reaction of the interstellar medium (ISM) to a high-speed (∼1000 km s−1) gas-gas collision. New sensitive, high-resolution (2.7″ or ∼800 pc) CO(1−0) observations of the Taffy system with the IRAM Plateau de Bure Interferometer (PdBI) are presented. The total CO luminosity of the Taffy system detected with the PdBI is LCO, tot = 4.8 × 109 K km s−1 pc2, 60% of the CO luminosity found with the IRAM 30 m telescope. About 25% of the total interferometric CO luminosity stems from the bridge region. Assuming a Galactic N(H2)/ICO conversion factor for the galactic disks and a third of this value for the bridge gas, about 10% of the molecular gas mass is located in the bridge region. The giant H II region close to UGC 12915 is located at the northern edge of the high-surface-brightness giant molecular cloud association (GMA), which has the highest velocity dispersion among the bridge GMAs. The bridge GMAs are clearly not virialized because of their high velocity dispersion. Three dynamical models are presented and while no single model reproduces all of the observed features, they are all present in at least one of the models. Most of the bridge gas detected in CO does not form stars. We suggest that turbulent adiabatic compression is responsible for the exceptionally high velocity dispersion of the molecular ISM and the suppression of star formation in the Taffy bridge. In this scenario the turbulent velocity dispersion of the largest eddies and turbulent substructures or clouds increase such that giant molecular clouds are no longer in global virial equilibrium. The increase in the virial parameter leads to a decrease in the star formation efficiency. The suppression of star formation caused by turbulent adiabatic compression was implemented in the dynamical simulations and decreased the star formation rate in the bridge region by ∼90%. Most of the low-surface-density, CO-emitting gas will disperse without forming stars but some of the high-density gas will probably collapse and form dense star clusters, such as the luminous H II region close to UGC 12915. We suggest that globular clusters and super star clusters formed and still form through the gravitational collapse of gas previously compressed by turbulent adiabatic compression during galaxy interactions.
Whistler wave occurrence and the interaction with strahl electrons during the first encounter of Parker Solar Probe

Aims. We studied the properties and occurrence of narrowband whistler waves and their interaction with strahl electrons observed between 0.17 and 0.26 au during the first encounter of Parker Solar Probe. Methods. We used Digital Fields Board band-pass filtered (BPF) data from FIELDS to detect the signatures of whistler waves. Additionally parameters derived from the particle distribution functions measured by the Solar Wind Electrons Alphas and Protons (SWEAP) instrument suite were used to investigate the plasma properties, and FIELDS suite measurements were used to investigate the electromagnetic (EM) fields properties corresponding to the observed whistler signatures. Results. We observe that the occurrence of whistler waves is low, nearly ~1.5% and less than 0.5% in the analyzed peak and average BPF data, respectively. Whistlers occur highly intermittently and 80% of the whistlers appear continuously for less than 3 s. The spacecraft frequencies of the analyzed waves are less than 0.2 electron cyclotron frequency (fce). The occurrence rate of whistler waves was found to be anticorrelated with the solar wind bulk velocity. The study of the duration of the whistler intervals revealed an anticorrelation between the duration and the solar wind velocity, as well as between the duration and the normalized amplitude of magnetic field variations. The pitch-angle widths (PAWs) of the field-aligned electron population referred to as the strahl are broader by at least 12 degrees during the presence of large amplitude narrowband whistler waves. This observation points toward an EM wave electron interaction, resulting in pitch-angle scattering. PAWs of strahl electrons corresponding to the short duration whistlers are higher compared to the long duration whistlers, indicating short duration whistlers scatter the strahl electrons better than the long duration ones. Parallel cuts through the strahl electron velocity distribution function (VDF) observed during the whistler intervals appear to depart from the Maxwellian shape typically found in the near-Sun strahl VDFs. The relative decrease in the parallel electron temperature and the increase in PAW for the electrons in the strahl energy range suggests that the interaction with whistler waves results in a transfer of electron momentum from the parallel to the perpendicular direction.
Regularized Policies are Reward Robust

Entropic regularization of policies in Reinforcement Learning (RL) is a commonly used heuristic to ensure that the learned policy explores the state-space sufficiently before overfitting to a local optimal policy. The primary motivation for using entropy is for exploration and disambiguating optimal policies; however, the theoretical effects are not entirely understood. In this work, we study the more general regularized RL objective and using Fenchel duality; we derive the dual problem which takes the form of an adversarial reward problem. In particular, we find that the optimal policy found by a regularized objective is precisely an optimal policy of a reinforcement learning problem under a worst-case adversarial reward. Our result allows us to reinterpret the popular entropic regularization scheme as a form of robustification. Furthermore, due to the generality of our results, we apply to other existing regularization schemes. Our results thus give insights into the effects of regularization of policies and deepen our understanding of exploration through robust rewards at large.
Dual-Level Collaborative Transformer for Image Captioning

Descriptive region features extracted by object detection networks have played an important role in the recent advancements of image captioning. However, they are still criticized for the lack of contextual information and fine-grained details, which in contrast are the merits of traditional grid features. In this paper, we introduce a novel Dual-Level Collaborative Transformer (DLCT) network to realize the complementary advantages of the two features. Concretely, in DLCT, these two features are first processed by a novel Dual-way Self Attenion (DWSA) to mine their intrinsic properties, where a Comprehensive Relation Attention component is also introduced to embed the geometric information. In addition, we propose a Locality-Constrained Cross Attention module to address the semantic noises caused by the direct fusion of these two features, where a geometric alignment graph is constructed to accurately align and reinforce region and grid features. To validate our model, we conduct extensive experiments on the highly competitive MS-COCO dataset, and achieve new state-of-the-art performance on both local and online test sets, i.e., 133.8% CIDEr on Karpathy split and 135.4% CIDEr on the official split.
A Differential Scanning Calorimetric (DSC) Study on Heavy Ozonized C60 Fullerene

The DSC (Differential Scanning Calorimetry) analysis of heavy ozonized C60 fullerene (known also as “fullerene ozopolymer”), shows the exothermal decomposition of this compound with a peak at 158°C with a decomposition enthalpy ΔHdec ≈ −322 kJ/mol and an activation energy for the decomposition E# = 71 kJ/mol. These experimental facts confirm the secondary ozonide/peroxidic and polymeric nature of fullerene ozopolymer originally suggested by the infrared spectroscopy analysis and solid state 13C-NMR spectroscopy. It is also shown that the secondary ozonide group of fullerene ozopolymer can be reduced selectively by a treatment with hydrogen iodide.