Miracle-dz/UniFAR-FTUs huggingface dataset:
Tight Distributed Listing of Cliques

Much progress has recently been made in understanding the complexity landscape of subgraph finding problems in the CONGEST model of distributed computing. However, so far, very few tight bounds are known in this area. For triangle (i.e., 3-clique) listing, an optimal $\tilde{O}(n^{1/3})$-round distributed algorithm has been constructed by Chang et al.~[SODA 2019, PODC 2019]. Recent works of Eden et al.~[DISC 2019] and of Censor-Hillel et al.~[PODC 2020] have shown sublinear algorithms for $K_p$-listing, for each $p \geq 4$, but still leaving a significant gap between the upper bounds and the known lower bounds of the problem. In this paper, we completely close this gap. We show that for each $p \geq 4$, there is an $\tilde{O}(n^{1 - 2/p})$-round distributed algorithm that lists all $p$-cliques $K_p$ in the communication network. Our algorithm is \emph{optimal} up to a polylogarithmic factor, due to the $\tilde{\Omega}(n^{1 - 2/p})$-round lower bound of Fischer et al.~[SPAA 2018], which holds even in the CONGESTED CLIQUE model. Together with the triangle-listing algorithm by Chang et al.~[SODA 2019, PODC 2019], our result thus shows that the round complexity of $K_p$-listing, for all $p$, is the same in both the CONGEST and CONGESTED CLIQUE models, at $\tilde{\Theta}(n^{1 - 2/p})$ rounds. For $p=4$, our result additionally matches the $\tilde{\Omega}(n^{1/2})$ lower bound for $K_4$-\emph{detection} by Czumaj and Konrad [DISC 2018], implying that the round complexities for detection and listing of $K_4$ are equivalent in the CONGEST model.
Management of Complex Cryptoglandular Anal Fistula: Challenges and Solutions

Abstract Anal fistulae can be a very difficult disease to manage. The management of complex fistulae is even more challenging. The risk to the fecal continence mechanism due to damage to the anal sphincters and refractoriness to the treatment (high recurrence rate) pose the two biggest challenges in the management of this disease. Apart from these, there are several other challenges in the treatment of complex fistulae. The intriguing and uphill task is that satisfactory solutions to most of these challenges are still not known, and there is hardly any consensus on whatever treatment solutions are available. To summarize, there is no gold-standard treatment available for treating complex anal fistulae, and the search for a satisfactory treatment option is still on. In this review, the endeavor has been to discuss and highlight recent path-breaking updates in the management of complex anal fistulae.
Good proctor or "Big Brother"? AI Ethics and Online Exam Supervision Technologies

This article philosophically analyzes online exam supervision technologies, which have been thrust into the public spotlight due to campus lockdowns during the COVID-19 pandemic and the growing demand for online courses. Online exam proctoring technologies purport to provide effective oversight of students sitting online exams, using artificial intelligence (AI) systems and human invigilators to supplement and review those systems. Such technologies have alarmed some students who see them as `Big Brother-like', yet some universities defend their judicious use. Critical ethical appraisal of online proctoring technologies is overdue. This article philosophically analyzes these technologies, focusing on the ethical concepts of academic integrity, fairness, non-maleficence, transparency, privacy, respect for autonomy, liberty, and trust. Most of these concepts are prominent in the new field of AI ethics and all are relevant to the education context. The essay provides ethical considerations that educational institutions will need to carefully review before electing to deploy and govern specific online proctoring technologies.
s-processing in AGB Stars Revisited. III. Neutron Captures from MHD Mixing at Different Metallicities and Observational Constraints

We present post-process neutron-capture computations for Asymptotic Giant Branch (AGB) stars of 1.5–3 M⊙ and metallicities −1.3 ≤ [Fe/H] ≤ 0.1. The reference stellar models are computed with the FRANEC code, using the Schwarzschild's criterion for convection; our motivations for this choice are outlined. We assume that MHD processes induce the penetration of protons below the convective boundary, when the Third Dredge Up occurs. There, the 13C n-source can subsequently operate, merging its effects with those of the 22Ne(α, n)25Mg reaction, activated at the temperature peaks characterizing AGB stages. This work has three main scopes. (i) We provide a grid of abundance yields, as produced through our MHD mixing scheme, which are uniformly sampled in mass and metallicity. From this, we deduce that the solar s-process distribution, as well as the abundances in recent stellar populations, can be accounted for, without the need of the extra primary-like contributions suggested in the past. (ii) We formulate analytic expressions for the mass of the 13C-pockets generated to allow easy verification of our findings. (iii) We compare our results with observations of evolved stars and with isotopic ratios in presolar SiC grains, also noticing how some flux tubes should survive turbulent disruption, carrying C-rich materials into the winds even when the envelope is O-rich. This wind phase is approximated through the G-component of AGB s-processing. We conclude that MHD-induced mixing is adequate to drive slow n-capture phenomena accounting for observations; our prescriptions should permit its inclusion into current stellar evolutionary codes.
Sparse Representations of Positive Functions via Projected Pseudo-Mirror Descent

We consider the problem of expected risk minimization when the population loss is strongly convex and the target domain of the decision variable is required to be nonnegative, motivated by the settings of maximum likelihood estimation (MLE) and trajectory optimization. We restrict focus to the case that the decision variable belongs to a nonparametric Reproducing Kernel Hilbert Space (RKHS). To solve it, we consider stochastic mirror descent that employs (i) pseudo-gradients and (ii) projections. Compressive projections are executed via kernel orthogonal matching pursuit (KOMP), and overcome the fact that the vanilla RKHS parameterization grows unbounded with time. Moreover, pseudo-gradients are needed, e.g., when stochastic gradients themselves define integrals over unknown quantities that must be evaluated numerically, as in estimating the intensity parameter of an inhomogeneous Poisson Process, and multi-class kernel logistic regression with latent multi-kernels. We establish tradeoffs between accuracy of convergence in mean and the projection budget parameter under constant step-size and compression budget, as well as non-asymptotic bounds on the model complexity. Experiments demonstrate that we achieve state-of-the-art accuracy and complexity tradeoffs for inhomogeneous Poisson Process intensity estimation and multi-class kernel logistic regression.
K-stability of birationally superrigid Fano 3-fold weighted hypersurfaces

Abstract We prove that the alpha invariant of a quasi-smooth Fano 3-fold weighted hypersurface of index $1$ is greater than or equal to $1/2$ . Combining this with the result of Stibitz and Zhuang [SZ19] on a relation between birational superrigidity and K-stability, we prove the K-stability of a birationally superrigid quasi-smooth Fano 3-fold weighted hypersurfaces of index $1$ .
Quantum Analysis of BTZ Black Hole Formation Due to the Collapse of a Dust Shell

We perform Hamiltonian reduction of a model in which 2 + 1 dimensional gravity with negative cosmological constant is coupled to a cylindrically symmetric dust shell. The resulting action contains only a finite number of degrees of freedom. The phase space consists of two copies of ADS2—both coordinate and momentum space are curved. Different regions in the Penrose diagram can be identified with different patches of ADS2 momentum space. Quantization in the momentum representation becomes particularly simple in the vicinity of the horizon, where one can neglect momentum non-commutativity. In this region, we calculate the spectrum of the shell radius. This spectrum turns out to be continuous outside the horizon and becomes discrete inside the horizon with eigenvalue spacing proportional to the square root of the black hole mass. We also calculate numerically quantum transition amplitudes between different regions of the Penrose diagram in the vicinity of the horizon. This calculation shows a possibility of quantum tunneling of the shell into classically forbidden regions of the Penrose diagram, although with an exponentially damped rate away from the horizon.
Corrupted Contextual Bandits with Action Order Constraints

We consider a variant of the novel contextual bandit problem with corrupted context, which we call the contextual bandit problem with corrupted context and action correlation, where actions exhibit a relationship structure that can be exploited to guide the exploration of viable next decisions. Our setting is primarily motivated by adaptive mobile health interventions and related applications, where users might transitions through different stages requiring more targeted action selection approaches. In such settings, keeping user engagement is paramount for the success of interventions and therefore it is vital to provide relevant recommendations in a timely manner. The context provided by users might not always be informative at every decision point and standard contextual approaches to action selection will incur high regret. We propose a meta-algorithm using a referee that dynamically combines the policies of a contextual bandit and multi-armed bandit, similar to previous work, as wells as a simple correlation mechanism that captures action to action transition probabilities allowing for more efficient exploration of time-correlated actions. We evaluate empirically the performance of said algorithm on a simulation where the sequence of best actions is determined by a hidden state that evolves in a Markovian manner. We show that the proposed meta-algorithm improves upon regret in situations where the performance of both policies varies such that one is strictly superior to the other for a given time period. To demonstrate that our setting has relevant practical applicability, we evaluate our method on several real world data sets, clearly showing better empirical performance compared to a set of simple algorithms.
Joint Power Allocation and Passive Beamforming Design for IRS-Assisted Physical-Layer Service Integration

Intelligent reflecting surface (IRS) has emerged as an appealing solution to enhance wireless communication performance by reconfiguring the wireless propagation environment. In this paper, we propose to apply IRS to the physical-layer service integration (PHY-SI) system, where a single-antenna access point (AP) integrates two sorts of service messages, i.e., multicast message and confidential message, via superposition coding to serve multiple single-antenna users. Our goal is to optimize the power allocation (for transmitting different messages) at the AP and the passive beamforming at the IRS to maximize the achievable secrecy rate region. To this end, we formulate this problem as a bi-objective optimization problem, which is shown equivalent to a secrecy rate maximization problem subject to the constraints on the quality of multicast service. Due to the non-convexity of this problem, we propose two customized algorithms to obtain its high-quality suboptimal solutions, thereby approximately characterizing the secrecy rate region. The resulting performance gap with the globally optimal solution is analyzed. Furthermore, we provide theoretical analysis to unveil the impact of IRS beamforming on the performance of PHY-SI. Numerical results demonstrate the advantages of leveraging IRS in improving the performance of PHY-SI and also validate our theoretical analysis.
Isomorphism of Skew-Holomorphic Harmonic Maass-Jacobi Forms and Certain Weak Harmonic Maass Forms

Recently Bringmann, Raum and Richter generalised the definition of Jacobi forms and Skoruppa's skew-holomorphic Jacobi forms by intertwining with harmonic Maass forms. We prove the isomorphism of the Kohnen's plus space analogue of harmonic Maass forms of weight $k-1/2$ for $\Gamma_0(4m)$ and the space of these skew-holomorphic harmonic Maass-Jacobi forms of weight $k$ and index $m$ for $k$ odd and $m=1$ or a prime.
Regularized Mutual Information Neural Estimation

With the variational lower bound of mutual information (MI), the estimation of MI can be understood as an optimization task via stochastic gradient descent. In this work, we start by showing how Mutual Information Neural Estimator (MINE) searches for the optimal function $T$ that maximizes the Donsker-Varadhan representation. With our synthetic dataset, we directly observe the neural network outputs during the optimization to investigate why MINE succeeds or fails: We discover the drifting phenomenon, where the constant term of $T$ is shifting through the optimization process, and analyze the instability caused by the interaction between the $logsumexp$ and the insufficient batch size. Next, through theoretical and experimental evidence, we propose a novel lower bound that effectively regularizes the neural network to alleviate the problems of MINE. We also introduce an averaging strategy that produces an unbiased estimate by utilizing multiple batches to mitigate the batch size limitation. Finally, we show that $L^2$ regularization achieves significant improvements in both discrete and continuous settings.
Wide-field Decodable Orthogonal Fingerprints of Single Nanoparticles Unlock Multiplexed Digital Assays

The control in optical uniformity of single nanoparticles and tuning their diversity in orthogonal dimensions, dot to dot, holds the key to unlock nanoscience and applications. Here we report that the time-domain emissive profile from single upconversion nanoparticle, including the rising, decay and peak moment of the excited state population (T2 profile), can be arbitrarily tuned by upconversion schemes, including interfacial energy migration, concentration dependency, energy transfer, and isolation of surface quenchers. This allows us to significantly increase the coding capacity at the nanoscale. We further implement both time-resolved wide-field imaging and deep-learning techniques to decode these fingerprints, showing high accuracies at high throughput. These high-dimensional optical fingerprints provide a new horizon for applications spanning from sub-diffraction-limit data storage, security inks, to high-throughput single-molecule digital assays and super-resolution imaging.
Viscous Heating and Boundary Layer Accretion in the Disk of Outbursting Star FU Orionis

Context. FU Orionis is the archetypal FUor star, a subclass of young stellar object (YSO) that undergo rapid brightening events, often gaining 4-6 magnitudes on timescales of days. This brightening is often associated with a massive increase in accretion; one of the most ubiquitous processes in astrophysics from planets and stars to super-massive black holes. We present multi-band interferometric observations of the FU Ori circumstellar environment, including the first J-band interferometric observations of a YSO. Aims. We investigate the morphology and temperature gradient of the inner-most regions of the accretion disk around FU Orionis. We aim to characterise the heating mechanisms of the disk and comment on potential outburst triggering processes. Methods. Recent upgrades to the MIRC-X instrument at the CHARA array allowed the first dual-band J and H observations of YSOs.Using baselines up to 331 m, we present high angular resolution data of a YSO covering the near-infrared bands J, H, and K. The unprecedented spectral range of the data allows us to apply temperature gradient models to the innermost regions of FU Ori. Results. We spatially resolve the innermost astronomical unit of the disk and determine the exponent of the temperature gradient of the inner disk to $T=r^{-0.74\pm0.02}$. This agrees with theoretical work that predicts $T = r^{-0.75}$ for actively accreting, steady state disks, a value only obtainable through viscous heating within the disk. We find a disk which extends down to the stellar surface at $0.015\pm0.007$ au where the temperature is found to be $5800\pm700$ K indicating boundary layer accretion. We find a disk inclined at $32\pm4^\circ$ with a minor-axis position angle of $34\pm11^\circ$.
Speech Prediction in Silent Videos Using Variational Autoencoders

Understanding the relationship between the auditory and visual signals is crucial for many different applications ranging from computer-generated imagery (CGI) and video editing automation to assisting people with hearing or visual impairments. However, this is challenging since the distribution of both audio and visual modality is inherently multi-modal. Therefore, most of the existing methods ignore the multimodal aspect and assume that there only exists a deterministic one-to-one mapping between the two modalities. It can lead to low-quality predictions as the model collapses to optimizing the average behavior rather than learning the full data distributions. In this paper, we present a stochastic model for generating speech in a silent video. The proposed model combines recurrent neural networks and variational deep generative models to learn the auditory signal’s conditional distribution given the visual signal. We demonstrate the performance of our model on the GRID dataset based on standard benchmarks.
Centrality Measures in Complex Networks: A Survey

In complex networks, each node has some unique characteristics that define the importance of the node based on the given application-specific context. These characteristics can be identified using various centrality metrics defined in the literature. Some of these centrality measures can be computed using local information of the node, such as degree centrality and semi-local centrality measure. Others use global information of the network like closeness centrality, betweenness centrality, eigenvector centrality, Katz centrality, PageRank, and so on. In this survey, we discuss these centrality measures and the state of the art literature that includes the extension of centrality measures to different types of networks, methods to update centrality values in dynamic networks, methods to identify top-k nodes, approximation algorithms, open research problems related to the domain, and so on. The paper is concluded with a discussion on application specific centrality measures that will help to choose a centrality measure based on the network type and application requirements.
Analysis of a high-resolution hand-written digits data set with writer characteristics

The contributions in this article are two-fold. First, we introduce a new hand-written digit data set that we collected. It contains high-resolution images of hand-written digits together with various writer characteristics which are not available in the well-known MNIST database. The data set is publicly available and is designed to create new research opportunities. Second, we perform a first analysis of this new data set. We begin with simple supervised tasks. We assess the predictability of the writer characteristics gathered, the effect of using some of those characteristics as predictors in classification task and the effect of higher resolution images on classification accuracy. We also explore semi-supervised applications; we can leverage the high quantity of hand-written digits data sets already existing online to improve the accuracy of various classifications task with noticeable success. Finally, we also demonstrate the generative perspective offered by this new data set; we are able to generate images that mimics the writing style of specific writers. The data set provides new research opportunities and our analysis establishes benchmarks and showcases some of the new opportunities made possible with this new data set.
Neopterin Predicts Disease Severity in Hospitalized Patients With COVID-19

Abstract This study evaluates the predictive value of circulating inflammatory markers, especially neopterin, in patients with coronavirus disease 2019 (COVID-19). Within this retrospective analysis of 115 hospitalized COVID-19 patients, elevated neopterin levels upon admission were significantly associated with disease severity, risk for intensive care unit admission, need for mechanical ventilation, and death. Therefore, neopterin is a reliable predictive marker in patients with COVID-19 and may help to improve the clinical management of patients.
Laparoscopic Ovariectomy in a Domestic Yak

Owners of a juvenile domestic yak elected bilateral ovariectomy to prevent future reproduction. The yak was noted to be healthy at presentation. Both ovaries were removed using a laparoscopic approach as follows: after induction and maintenance of general inhalant anesthesia, 15 degrees Trendelenburg positioning was required to view the ovaries. Ovariectomy was conducted within a surgical time of 50 minutes. Due to the small ovarian size, portal enlargement was not necessary for removal. Mild hemorrhage from the left ovarian pedicle was controlled with application of a vessel-sealing device. Postoperative complications were not encountered during hospitalization. At 12 months following surgery, the yak was healthy, and the owner was highly satisfied with the procedure. The described approach was successful for performing laparoscopic ovariectomy in a juvenile yak. Positioning for surgery was similar to other small ruminant species. Further case enrollment is needed to optimize the surgical approach and better describe clinical outcomes.
Search of spin-dependent fifth forces with precision magnetometry

Spin-dependent fifth-forces are associated with particles beyond the standard model. In particular, light pseudo-scalar bosons mediate long-range forces, allowing mass to interact with spins. The search of these interactions can be performed by periodically varying the distance between a source mass and a spin ensemble, in order to modulate the force intensity and detect it with precision magnetometry techniques. In our setup the force arises from room temperature lead masses and is detected in a paramagnetic crystal at 4.2\,K, whose magnetisation is monitored by a SQUID-based magnetometer with the sensitivity of $53\,\mathrm{aT/\sqrt{Hz}}$. Our measurement places the most stringent constraints on a spin-mass interaction in the ranges 1\,cm to 10\,m and 10\,km to 300\,km, improving existing limits up to more than two orders of magnitude. We show that this experimental technique may be further leveraged to explore a vast region of the fifth force's parameter space, with an interaction range longer than a few centimetres.