Miracle-dz/UniFAR-FTUs huggingface dataset:
Batch Codes for Asynchronous Recovery of Data

We propose a new model of asynchronous batch codes that allow for parallel recovery of information symbols from a coded database in an asynchronous manner, i.e. when requests arrive at random times and they take varying time to process. We show that the graph-based batch codes studied by Rawat <italic>et al.</italic> are asynchronous. Further, we demonstrate that hypergraphs of Berge girth larger or equal to 4, respectively larger or equal to 3, yield graph-based asynchronous batch codes, respectively private information retrieval (PIR) codes. We prove a hypergraph-theoretic proposition that the maximum number of hyperedges in a hypergraph of a fixed Berge girth equals the quantity in a certain generalization of the hypergraph-theoretic (6,3)-problem, first posed by Brown, Erdős and Sós. We then apply the constructions and bounds by Erdős, Frankl and Rödl about this generalization of the (6,3)-problem, known as the (<inline-formula> <tex-math notation="LaTeX">$3\varrho $ </tex-math></inline-formula>-3,<inline-formula> <tex-math notation="LaTeX">$\varrho $ </tex-math></inline-formula>)-problem, to obtain batch code constructions and bounds on the redundancy of the graph-based asynchronous batch and PIR codes. We derive bounds on the optimal redundancy of several families of asynchronous batch codes with the query size <inline-formula> <tex-math notation="LaTeX">$t=2$ </tex-math></inline-formula>. In particular, we show that the optimal redundancy <inline-formula> <tex-math notation="LaTeX">$\rho (k)$ </tex-math></inline-formula> of graph-based asynchronous batch codes of dimension <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> for <inline-formula> <tex-math notation="LaTeX">$t=2$ </tex-math></inline-formula> is <inline-formula> <tex-math notation="LaTeX">$2\sqrt {k}$ </tex-math></inline-formula>. Moreover, for graph-based asynchronous batch codes with <inline-formula> <tex-math notation="LaTeX">$t \ge 3$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\rho (k) = O\left ({{k}^{1/(2-\epsilon)}}\right)$ </tex-math></inline-formula> for any small <inline-formula> <tex-math notation="LaTeX">$\epsilon >0$ </tex-math></inline-formula>.
Survey,Indentification and Virulence Test of Pathogens of the Pine Sawyer Beetle,Monochamus alternatus,at Forest Farm of Maanshan,Anhui Province

A survey of pathogens of Monochamus alternatus,a vector of Bursaphelenchus xylophilus,was conducted at Forest Farm of Maanshan,Anhui Province.Eight entomogenous fungi and one bacterium were identified from Monochamus alternatus in pine forests.Of these species,Beauveria bassiana was the most common species.There was substantial variability in virulence among the strains of the same species B.bassian and the different species.The strains of B.bassiana were tested on virulence to M.alternatus,colony growth and sporogenous ability.These results suggested strain 226 might be a effective agent for controling of M.alternatus.
CosmoBit: a GAMBIT module for computing cosmological observables and likelihoods

We introduce CosmoBit, a module within the open-source GAMBIT software framework for exploring connections between cosmology and particle physics with joint global fits. CosmoBit provides a flexible framework for studying various scenarios beyond ΛCDM, such as models of inflation, modifications of the effective number of relativistic degrees of freedom, exotic energy injection from annihilating or decaying dark matter, and variations of the properties of elementary particles such as neutrino masses and the lifetime of the neutron. Many observables and likelihoods in CosmoBit are computed via interfaces to AlterBBN, CLASS, DarkAges, MontePython, MultiModeCode, and plc. This makes it possible to apply a wide range of constraints from large-scale structure, Type Ia supernovae, Big Bang Nucleosynthesis and the cosmic microwave background. Parameter scans can be performed using the many different statistical sampling algorithms available within the GAMBIT framework, and results can be combined with calculations from other GAMBIT modules focused on particle physics and dark matter. We include extensive validation plots and a first application to scenarios with non-standard relativistic degrees of freedom and neutrino temperature, showing that the corresponding constraint on the sum of neutrino masses is much weaker than in the standard scenario.
Galactic seismology: the evolving ‘phase spiral’ after the Sagittarius dwarf impact

One of the most important discoveries to emerge from the ESA Gaia astrometric survey is the "phase spiral" pattern detected in the $z-V_z$ plane throughout the solar neighbourhood. In Galactic cylindrical coordinates ($R,\phi,z$),individual stars have velocities ($V_R,V_\phi,V_z$) and oscillation frequencies ($\Omega_R,\Omega_\phi,\Omega_z$)=($\kappa,\Omega,\nu$). The phase spiral is most evident when each point of the $z-V_z$ phase plane is represented by either $\langle V_R\rangle$ or $\langle V_\phi\rangle$, averaged over the volume, indicating that the disturbance has locked the vertical oscillations with the in-plane epicyclic motions. We revisit Binney & Schoenrich (2018) who use an analytic model of a disc-crossing satellite ($2\times 10^{10}$ M$_\odot$) to explain the phase spiral and its coupled behaviour in $\langle V_R\rangle$ and $\langle V_\phi\rangle$. With the aid of the AGAMA and RAMSES codes, we explore a high-resolution N-body model that is true to the analytic calculation. We find that the impulse triggers a superposition of two distinct bisymmetric (m=2) modes -- a density wave and a bending wave -- that wrap up at different rates. Stars in the faster density wave wrap up with time $T$ according to $\phi_D(R,T)=(\Omega_D(R)+\Omega_o)T$ where $\phi_D$ describes the spiral pattern and $\Omega_D=\Omega(R)-\kappa(R)/2$. While the pattern speed $\Omega_o$ is small, it is non-zero indicating that this is a dynamic density wave. The slower bending wave wraps up according to $\Omega_B\approx\Omega_D/2$ producing a corrugated wave, a phenomenon now well established in the Galaxy. The bunching effect of the density wave triggers the "phase spiral" as it rolls up and down on the bending wave ("rollercoaster" model). The phase spiral emerges slowly about 380 Myr after impact, and symbiotically in $\langle V_R\rangle$ and $\langle V_\phi\rangle$.
Matching Bounds: How Choice of Matching AlgorithmImpacts Treatment Effects Estimates and What to Do about It

Different matches on the same data may produce different treatment effect estimates, even when they achieve similar balance or minimize the same loss function. We discuss reasons and consequences of this problem. We present evidence of this problem by replicating ten papers that use matching. We introduce Matching Bounds: a finite-sample, non-stochastic method that allows analysts to know whether a matched sample that produces different results with the same levels of balance and overall match quality could be obtained from their data. We apply Matching Bounds to a replication of a matching study of the effects of foreign aid on civil conflict and find that results could change based on the matched sample chosen.
Respect for Human Autonomy in Recommender Systems

Recommender systems can influence human behavior in significant ways, in some cases making people more machine-like. In this sense, recommender systems may be deleterious to notions of human autonomy. Many ethical systems point to respect for human autonomy as a key principle arising from human rights considerations, and several emerging frameworks for AI include this principle. Yet, no specific formalization has been defined. Separately, self-determination theory shows that autonomy is an innate psychological need for people, and moreover has a significant body of experimental work that formalizes and measures level of human autonomy. In this position paper, we argue that there is a need to specifically operationalize respect for human autonomy in the context of recommender systems. Moreover, that such an operational definition can be developed based on well-established approaches from experimental psychology, which can then be used to design future recommender systems that respect human autonomy.
Mapping Poincaré gauge cosmology to Horndeski theory for emergent dark energy

The ten-parameter, quadratic Poincare gauge theory of gravity is a plausible alternative to general relativity (GR). We show that the rich background cosmology of the gauge theory is described by a non-canonical bi-scalar-tensor theory in the Jordan frame: the metrical analogue. This provides a unified framework for future investigation by the broader community. The analogues of novel unitary and power-counting-renormalisable cases of the gauge theory contain a Cuscuton field: these theories match GR up to a dark radiation component, and produce their own dark energy.
Bluff: Interactively Deciphering Adversarial Attacks on Deep Neural Networks

Deep neural networks (DNNs) are now commonly used in many domains. However, they are vulnerable to adversarial attacks: carefully-crafted perturbations on data inputs that can fool a model into making incorrect predictions. Despite significant research on developing DNN attack and defense techniques, people still lack an understanding of how such attacks penetrate a model’s internals. We present Bluff, an interactive system for visualizing, characterizing, and deciphering adversarial attacks on vision-based neural networks. Bluff allows people to flexibly visualize and compare the activation pathways for benign and attacked images, revealing mechanisms that adversarial attacks employ to inflict harm on a model. Bluff is open-sourced and runs in modern web browsers.
MetaSleepLearner: A Pilot Study on Fast Adaptation of Bio-Signals-Based Sleep Stage Classifier to New Individual Subject Using Meta-Learning

Identifying bio-signals based-sleep stages requires time-consuming and tedious labor of skilled clinicians. Deep learning approaches have been introduced in order to challenge the automatic sleep stage classification conundrum. However, the difficulties can be posed in replacing the clinicians with the automatic system due to the differences in many aspects found in individual bio-signals, causing the inconsistency in the performance of the model on every incoming individual. Thus, we aim to explore the feasibility of using a novel approach, capable of assisting the clinicians and lessening the workload. We propose the transfer learning framework, entitled MetaSleepLearner, based on Model Agnostic Meta-Learning (MAML), in order to transfer the acquired sleep staging knowledge from a large dataset to new individual subjects (source code is available at https://github.com/IoBT-VISTEC/MetaSleepLearner). The framework was demonstrated to require the labelling of only a few sleep epochs by the clinicians and allow the remainder to be handled by the system. Layer-wise Relevance Propagation (LRP) was also applied to understand the learning course of our approach. In all acquired datasets, in comparison to the conventional approach, MetaSleepLearner achieved a range of 5.4% to 17.7% improvement with statistical difference in the mean of both approaches. The illustration of the model interpretation after the adaptation to each subject also confirmed that the performance was directed towards reasonable learning. MetaSleepLearner outperformed the conventional approaches as a result from the fine-tuning using the recordings of both healthy subjects and patients. This is the first work that investigated a non-conventional pre-training method, MAML, resulting in a possibility for human-machine collaboration in sleep stage classification and easing the burden of the clinicians in labelling the sleep stages through only several epochs rather than an entire recording.
A spectral study of the black hole X-ray binary MAXI J1820+070 with AstroSat and NuSTAR

MAXI J1820+070 is a newly discovered transient black hole X-ray binary, which showed several spectral and temporal features. In this work, we analyse the broadband X-ray spectra from all three simultaneously observing X-ray instruments onboard AstroSat, as well as contemporaneous X-ray spectra from NuSTAR, observed during the hard state of MAXI J1820+070 in March 2018. Implementing a combination of multi-colour disc model, relativistic blurred reflection model relxilllpCp and a distant reflection in the form of xillverCp, we achieve reasonable and consistent fits for AstroSat and NuSTAR spectra. The best-fit model suggests a low temperature disc ($kT_{\rm in} \sim 0.3$ keV), iron overabundance ($A_{\rm Fe} \sim 4-5$ solar), a short lamp-post corona height ($h \lesssim 8 R_{\rm g}$), and a high corona temperature ($kT_{\rm e} \sim 115-150$ keV). Addition of a second Comptonisation component leads to a significantly better fit, with the $kT_{\rm e}$ of the second Comptonisation component being $\sim 14-18$ keV. Our results from independent observations with two different satellites in a similar source state indicate an inhomogeneous corona, with decreasing temperature attributed to increasing height. Besides, utilising the broader energy coverage of AstroSat, we estimate the black hole mass to be $6.7-13.9 \ M_{\odot}$, consistent with independent measurements reported in the literature.
The boundary density profile of a Coulomb droplet. Freezing at the edge

We revisit the problem of computing the boundary density profile of a droplet of two-dimensional one-component plasma (2D OCP) with logarithmic interaction between particles in a confining harmonic potential. At a sufficiently low temperature, but still in the liquid phase, the density exhibits oscillations as a function of the distance to the boundary of the droplet. We obtain the density profile numerically using Monte-Carlo simulations of the 2D OCP. We argue that the decay and period of those oscillations can be explained within a picture of the Wigner crystallization near the boundary, where the crystal is gradually melted with the increasing distance to the boundary.
UPB at SemEval-2020 Task 9: Identifying Sentiment in Code-Mixed Social Media Texts Using Transformers and Multi-Task Learning

Sentiment analysis is a process widely used in opinion mining campaigns conducted today. This phenomenon presents applications in a variety of fields, especially in collecting information related to the attitude or satisfaction of users concerning a particular subject. However, the task of managing such a process becomes noticeably more difficult when it is applied in cultures that tend to combine two languages in order to express ideas and thoughts. By interleaving words from two languages, the user can express with ease, but at the cost of making the text far less intelligible for those who are not familiar with this technique, but also for standard opinion mining algorithms. In this paper, we describe the systems developed by our team for SemEval-2020 Task 9 that aims to cover two well-known code-mixed languages: Hindi-English and Spanish-English. We intend to solve this issue by introducing a solution that takes advantage of several neural network approaches, as well as pre-trained word embeddings. Our approach (multlingual BERT) achieves promising performance on the Hindi-English task, with an average F1-score of 0.6850, registered on the competition leaderboard, ranking our team 16 out of 62 participants. For the Spanish-English task, we obtained an average F1-score of 0.7064 ranking our team 17th out of 29 participants by using another multilingual Transformer-based model, XLM-RoBERTa.
Inversion of electromagnetic induction data using a novel wavelet-based and scale-dependent regularization term

The inversion of electromagnetic induction data to a conductivity profile is an ill-posed problem. Regularization improves the stability of the inversion and a smoothing constraint is typically used. However, the conductivity profiles are not always expected to be smooth. Here, we develop a new inversion scheme in which we transform the model to the wavelet space and impose a sparsity constraint. This sparsity constrained inversion scheme will minimize an objective function with a least-squares data misfit and a sparsity measure of the model in the wavelet domain. A model transform to the wavelet domain allows to investigate the temporal resolution (periodicities at different frequencies) and spatial resolution (location of the peaks) characteristics of the model, and penalizing small-scale coefficients effectively reduces the complexity of the model. The novel scale-dependent regularization term can be used to favour either blocky or smooth structures, as well as high-amplitude models in globally smooth structures in the inversion. Depending on the expected conductivity profile, a suitable wavelet basis function can be chosen. The scheme supports multiple types of regularization with the same algorithm and is thus flexible. Finally, we apply this new scheme on a frequency domain electromagnetic sounding data set, but the scheme could equally apply to any other 1-D geophysical method.
New Upper Bounds for Trace Reconstruction

We improve the lower bound on worst case trace reconstruction from $\Omega\left(\frac{n^{5/4}}{\sqrt{\log n}}\right)$ to $\Omega\left(\frac{n^{3/2}}{\log^{7} n}\right)$. As a consequence, we improve the lower bound on average case trace reconstruction from $\Omega\left(\frac{\log^{9/4}n}{\sqrt{\log\log n}}\right)$ to $\Omega\left(\frac{\log^{5/2}n}{(\log\log n)^{7}}\right)$.
Whitham modulation theory for generalized Whitham equations and a general criterion for modulational instability

The Whitham equation was proposed as a model for surface water waves that combines the quadratic flux nonlinearity f(u)=12u2 of the Korteweg–de Vries equation and the full linear dispersion relation Ω(k)=ktanhk of unidirectional gravity water waves in suitably scaled variables. This paper proposes and analyzes a generalization of Whitham's model to unidirectional nonlinear wave equations consisting of a general nonlinear flux function f(u) and a general linear dispersion relation Ω(k) . Assuming the existence of periodic traveling wave solutions to this generalized Whitham equation, their slow modulations are studied in the context of Whitham modulation theory. A multiple scales calculation yields the modulation equations, a system of three conservation laws that describe the slow evolution of the periodic traveling wave's wavenumber, amplitude, and mean. In the weakly nonlinear limit, explicit, simple criteria in terms of general f(u) and Ω(k) establishing the strict hyperbolicity and genuine nonlinearity of the modulation equations are determined. This result is interpreted as a generalized Lighthill–Whitham criterion for modulational instability.
Capturing dynamics of post-earnings-announcement drift using genetic algorithm-optimised supervised learning

While Post-Earnings-Announcement Drift (PEAD) is one of the most studied stock market anomalies, the current literature is often limited in explaining this phenomenon by a small number of factors using simpler regression methods. In this paper, we use a machine learning based approach instead, and aim to capture the PEAD dynamics using data from a large group of stocks and a wide range of both fundamental and technical factors. Our model is built around the Extreme Gradient Boosting (XGBoost) and uses a long list of engineered input features based on quarterly financial announcement data from 1,106 companies in the Russell 1000 index between 1997 and 2018. We perform numerous experiments on PEAD predictions and analysis and have the following contributions to the literature. First, we show how Post-Earnings-Announcement Drift can be analysed using machine learning methods and demonstrate such methods' prowess in producing credible forecasting on the drift direction. It is the first time PEAD dynamics are studied using XGBoost. We show that the drift direction is in fact driven by different factors for stocks from different industrial sectors and in different quarters and XGBoost is effective in understanding the changing drivers. Second, we show that an XGBoost well optimised by a Genetic Algorithm can help allocate out-of-sample stocks to form portfolios with higher positive returns to long and portfolios with lower negative returns to short, a finding that could be adopted in the process of developing market neutral strategies. Third, we show how theoretical event-driven stock strategies have to grapple with ever changing market prices in reality, reducing their effectiveness. We present a tactic to remedy the difficulty of buying into a moving market when dealing with PEAD signals.
Communication-efficient distributed eigenspace estimation

Distributed computing is a standard way to scale up machine learning and data science algorithms to process large amounts of data. In such settings, avoiding communication amongst machines is paramount for achieving high performance. Rather than distribute the computation of existing algorithms, a common practice for avoiding communication is to compute local solutions or parameter estimates on each machine and then combine the results; in many convex optimization problems, even simple averaging of local solutions can work well. However, these schemes do not work when the local solutions are not unique. Spectral methods are a collection of such problems, where solutions are orthonormal bases of the leading invariant subspace of an associated data matrix, which are only unique up to rotation and reflections. Here, we develop a communication-efficient distributed algorithm for computing the leading invariant subspace of a data matrix. Our algorithm uses a novel alignment scheme that minimizes the Procrustean distance between local solutions and a reference solution, and only requires a single round of communication. For the important case of principal component analysis (PCA), we show that our algorithm achieves a similar error rate to that of a centralized estimator. We present numerical experiments demonstrating the efficacy of our proposed algorithm for distributed PCA, as well as other problems where solutions exhibit rotational symmetry, such as node embeddings for graph data and spectral initialization for quadratic sensing.
A Simple and General Graph Neural Network with Stochastic Message Passing

Graph neural networks (GNNs) are emerging machine learning models on graphs. One key property behind the expressiveness of existing GNNs is that the learned node representations are permutation-equivariant. Though being a desirable property for certain tasks, however, permutation-equivariance prevents GNNs from being proximity-aware, i.e., preserving the walk-based proximities between pairs of nodes, which is another critical property for graph analytical tasks. On the other hand, some variants of GNNs are proposed to preserve node proximities, but they fail to maintain permutation-equivariance. How to empower GNNs to be proximity-aware while maintaining permutation-equivariance remains an open problem. In this paper, we propose Stochastic Message Passing (SMP), a general and simple GNN to maintain both proximity-awareness and permutation-equivariance properties. Specifically, we augment the existing GNNs with stochastic node representations learned to preserve node proximities. Though seemingly simple, we prove that such a mechanism can enable GNNs to preserve node proximities in theory while maintaining permutation-equivariance with certain parametrization. Extensive experimental results demonstrate the effectiveness and efficiency of SMP for tasks including node classification and link prediction.