Miracle-dz/UniFAR-FTUs huggingface dataset:
Salmonella Screening Procedure with Tests for β-Galactosidase and Flagellar Antigens

Differentiation of Salmonella from other gram-negative bacilli requires several biochemical and serological tests. A simplified 24-hr screening procedure has been devised which allows discarding of large numbers of isolates (picked from selective plating media) before they are subjected to this extensive testing. Cultures of gram-negative organisms isolated to triple sugar-iron slants during routine examination of products for Salmonella were tested for the presence of β-galactosidase and Salmonella flagellar antigens. β-Galactosidase-positive cultures which did not agglutinate in polyvalent flagellar antiserum were considered to be nonsalmonellae. Of 1,103 Salmonella cultures tested, none of the 61 different serotypes was missed by this procedure, whereas 673 (82.3%) of 818 nonsalmonellae were excluded from further testing. This screening procedure eliminates most nonsalmonellae and augments the proportion of cultures undergoing further biochemical and serological testing which will be confirmed as Salmonella.
5-(4-Bromo­phen­yl)-3-methyl­sulfanyl-2-phenyl-1-benzofuran

The title compound, C21H15BrOS, was prepared by the Lewis acid-catalysed reaction of 4′-bromo-1,1′-biphenyl-4-ol with 2-chloro-2-(methyl­sulfan­yl)acetophenone. The structure is stabilized by inter­molecular (benzofuran)C—H⋯O(furan) hydrogen bonds and by aromatic π(benzene ring of benzo­furan)⋯H—C(4-bromo­phen­yl) inter­actions.
INVERTED-REPEATS-AWAREFINITE-CONTEXTMODELSFORDNACODING

Finite-context models have been used for DNA sequence compression as secondary, fall back mechanisms, the generalized opinion being that models with order larger than two or three are inappropriate. In this paper we show that finite-context models can also be used as the main encoding method, and that they are effective for model orders at least as higher as thirteen. Moreover, we propose a new model updating scheme that takes into account inverted repeats, a common characteristic in DNA sequences.
Three equivalent ordinal notation systems in cubical Agda

We present three ordinal notation systems representing ordinals below ε0 in type theory, using recent type-theoretical innovations such as mutual inductive-inductive definitions and higher inductive types. We show how ordinal arithmetic can be developed for these systems, and how they admit a transfinite induction principle. We prove that all three notation systems are equivalent, so that we can transport results between them using the univalence principle. All our constructions have been implemented in cubical Agda.
The Offense-Defense Balance of Scientific Knowledge: Does Publishing AI Research Reduce Misuse?

There is growing concern over the potential misuse of artificial intelligence (AI) research. Publishing scientific research can facilitate misuse of the technology, but the research can also contribute to protections against misuse. This paper addresses the balance between these two effects. Our theoretical framework elucidates the factors governing whether the published research will be more useful for attackers or defenders, such as the possibility for adequate defensive measures, or the independent discovery of the knowledge outside of the scientific community. The balance will vary across scientific fields. However, we show that the existing conversation within AI has imported concepts and conclusions from prior debates within computer security over the disclosure of software vulnerabilities. While disclosure of software vulnerabilities often favours defence, this cannot be assumed for AI research. The AI research community should consider concepts and policies from a broad set of adjacent fields, and ultimately needs to craft policy well-suited to its particular challenges.
Dual Adversarial Domain Adaptation

Unsupervised domain adaptation aims at transferring knowledge from the labeled source domain to the unlabeled target domain. Previous adversarial domain adaptation methods mostly adopt the discriminator with binary or $K$-dimensional output to perform marginal or conditional alignment independently. Recent experiments have shown that when the discriminator is provided with domain information in both domains and label information in the source domain, it is able to preserve the complex multimodal information and high semantic information in both domains. Following this idea, we adopt a discriminator with $2K$-dimensional output to perform both domain-level and class-level alignments simultaneously in a single discriminator. However, a single discriminator can not capture all the useful information across domains and the relationships between the examples and the decision boundary are rarely explored before. Inspired by multi-view learning and latest advances in domain adaptation, besides the adversarial process between the discriminator and the feature extractor, we also design a novel mechanism to make two discriminators pit against each other, so that they can provide diverse information for each other and avoid generating target features outside the support of the source domain. To the best of our knowledge, it is the first time to explore a dual adversarial strategy in domain adaptation. Moreover, we also use the semi-supervised learning regularization to make the representations more discriminative. Comprehensive experiments on two real-world datasets verify that our method outperforms several state-of-the-art domain adaptation methods.
D3S: A Framework for Enabling Unmanned AerialVehicles as a Service

In this paper, we consider the use of UAVs to provide wireless connectivity services, for example after failures of wireless network components or to simply provide additional bandwidth on demand, and introduce the concept of UAVs as a service (UaaS). To facilitate UaaS, we introduce a novel framework, dubbed D3S, which consists of four phases: demand, decision, deployment, and service. The main objective of this framework is to develop efficient and realistic solutions to implement these four phases. The technical problems include determining the type and number of UAVs to be deployed, and also their final locations (e.g., hovering or on-ground), which is important for serving certain applications. These questions will be part of the decision phase. They also include trajectory planning of UAVs when they have to travel between charging stations and deployment locations and may have to do this several times. These questions will be part of the deployment phase. The service phase includes the implementation of the backbone communication and data routing between UAVs and between UAVs and ground control stations.
Subexponential and Linear Subpacketization Coded Caching via Line Graphs and Projective Geometry

Large gains in the rate of cache-aided broadcast communication are obtained using coded caching, but to obtain this most existing centralized coded caching schemes require that the files at the server be divisible into a large number of parts (this number is called subpacketization). In fact, most schemes require the subpacketization to be growing asymptotically as exponential in some $r^{th}$ root of the number of users $K$. On the other extreme, few schemes having subpacketization linear in $K$ are known; however they require large number of users to exist, or they offer only little gain in rate. In this work, we propose a new framework known as \textit{caching line graphs} for centralized coded caching and utilize projective geometries over finite fields to construct two new coded caching schemes with low subpacketization and moderate rate gains. Both the schemes achieve the same asymptotic subpacketization, which is exponential in $O((\log K)^2)$ (thus improving on the root-of-$K$ exponent). The first scheme has a larger cache requirement but has at most a constant rate (with increasing $K$), while the second has small cache requirement but has a larger rate. As a special case of our second scheme, we get a new linear subpacketization scheme, which has a more flexible range of parameters than existing linear subpacketization schemes. We also present two new lower bounds on the optimal rate of coded caching for a given subpacketization level. Extending our techniques, we also obtain low subpacketization schemes for other multi-receiver settings such as D2D communications, distributed computing, and the cache-aided interference channel. We validate the performance of all our schemes via extensive numerical comparisons.
Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)

Given the ubiquity of handwritten documents in human transactions, Optical Character Recognition (OCR) of documents have invaluable practical worth. Optical character recognition is a science that enables to translate various types of documents or images into analyzable, editable and searchable data. During last decade, researchers have used artificial intelligence/machine learning tools to automatically analyze handwritten and printed documents in order to convert them into electronic format. The objective of this review paper is to summarize research that has been conducted on character recognition of handwritten documents and to provide research directions. In this Systematic Literature Review (SLR) we collected, synthesized and analyzed research articles on the topic of handwritten OCR (and closely related topics) which were published between year 2000 to 2019. We followed widely used electronic databases by following pre-defined review protocol. Articles were searched using keywords, forward reference searching and backward reference searching in order to search all the articles related to the topic. After carefully following study selection process 176 articles were selected for this SLR. This review article serves the purpose of presenting state of the art results and techniques on OCR and also provide research directions by highlighting research gaps.
Over 78 000 RR Lyrae Stars in the Galactic Bulge and Disk from the OGLE Survey

We present an upgrade of the OGLE Collection of RR Lyrae stars in the Galactic bulge and disk. The size of our sample has been doubled and reached 78 350 RR Lyr variables, of which 56 508 are fundamental-mode pulsators (RRab stars), 21 321 pulsate solely in the first-overtone (RRc stars), 458 are classical double-mode pulsators (RRd stars), and 63 are anomalous RRd variables (including five triple-mode pulsators). For all the newly identified RR Lyr stars, we publish time-series photometry obtained during the OGLE Galaxy Variability Survey. We present the spatial distribution of RR Lyr stars on the sky, provide a list of globular clusters hosting RR Lyr variables, and discuss the Petersen diagram for multimode pulsators. We find new RRd stars belonging to a compact group in the Petersen diagram (with period ratios P_1O/P_F = 0.74 and fundamental-mode periods P_F = 0.44 d) and we show that their spatial distribution is roughly spherically symmetrical around the Milky Way center.
Cost-Function-Dependent Barren Plateaus in Shallow Quantum Neural Networks

Variational quantum algorithms (VQAs) optimize the parameters $\boldsymbol{\theta}$ of a quantum neural network $V(\boldsymbol{\theta})$ to minimize a cost function $C$. While VQAs may enable practical applications of noisy quantum computers, they are nevertheless heuristic methods with unproven scaling. Here, we rigorously prove two results, assuming $V(\boldsymbol{\theta})$ is a hardware-efficient ansatz composed of blocks forming local 2-designs. Our first result states that defining $C$ in terms of global observables leads to an exponentially vanishing gradient (i.e., a barren plateau) even when $V(\boldsymbol{\theta})$ is shallow. This implies that several VQAs in the literature must revise their proposed cost functions. On the other hand, our second result states that defining $C$ with local observables leads to at worst a polynomially vanishing gradient, so long as the depth of $V(\boldsymbol{\theta})$ is $\mathcal{O}(\log n)$. Taken together, our results establish a connection between locality and trainability. Finally, we illustrate these ideas with large-scale simulations, up to 100 qubits, of a particular VQA known as quantum autoencoders.
Long-Term Visitation Value for Deep Exploration in Sparse Reward Reinforcement Learning

Reinforcement learning with sparse rewards is still an open challenge. Classic methods rely on getting feedback via extrinsic rewards to train the agent, and in situations where this occurs very rarely the agent learns slowly or cannot learn at all. Similarly, if the agent receives also rewards that create suboptimal modes of the objective function, it will likely prematurely stop exploring. More recent methods add auxiliary intrinsic rewards to encourage exploration. However, auxiliary rewards lead to a non-stationary target for the Q-function. In this paper, we present a novel approach that (1) plans exploration actions far into the future by using a long-term visitation count, and (2) decouples exploration and exploitation by learning a separate function assessing the exploration value of the actions. Contrary to existing methods that use models of reward and dynamics, our approach is off-policy and model-free. We further propose new tabular environments for benchmarking exploration in reinforcement learning. Empirical results on classic and novel benchmarks show that the proposed approach outperforms existing methods in environments with sparse rewards, especially in the presence of rewards that create suboptimal modes of the objective function. Results also suggest that our approach scales gracefully with the size of the environment.
Combining interdependent climate model outputs in CMIP5: A spatial Bayesian approach

Projections of future climate change rely heavily on climate models, and combining climate models through a multi-model ensemble is both more accurate than a single climate model and valuable for uncertainty quantification. However, Bayesian approaches to multi-model ensembles have been criticized for making oversimplified assumptions about bias and variability, as well as treating different models as statistically independent. This paper extends the Bayesian hierarchical approach of Sansom et al. (2017) by explicitly accounting for spatial variability and inter-model dependence. We propose a Bayesian hierarchical model that accounts for bias between climate models and observations, spatial and inter-model dependence, the emergent relationship between historical and future periods, and natural variability. Extensive simulations show that our model provides better estimates and uncertainty quantification than the commonly used simple model mean. These results are illustrated using data from the CMIP5 model archive. As examples, for Central North America our projected mean temperature for 2070--2100 is about 0.8 K lower than the simple model mean, while for East Asia it is about 0.5 K higher; however, in both cases, the widths of the 90% credible intervals are of the order 3--6 K, so the uncertainties overwhelm the relatively small differences in projected mean temperatures.
Entanglement Dynamics of Random GUE Hamiltonians

In this work, we consider a model of a subsystem interacting with a reservoir and study dynamics of entanglement assuming that the overall time-evolution is governed by non-integrable Hamiltonians. We also compare to an ensemble of Integrable Hamiltonians. To do this, we make use of unitary invariant ensembles of random matrices with either Wigner-Dyson or Poissonian distributions of energy. Using the theory of Weingarten functions, we derive universal average time evolution of the reduced density matrix and the purity and compare these results with several physical Hamiltonians: randomized versions of the transverse field Ising and XXZ models, Spin Glass and, Central Spin and SYK model. The theory excels at describing the latter two. Along the way, we find general expressions for exponential $n$-point correlation functions in the gas of GUE eigenvalues.
Mobile Small Polarons Explain Conductivity in Lithium Titanium Oxide Battery Electrodes.

Lithium titanium oxide Li$_4$Ti$_5$O$_{12}$ (LTO) is an intriguing anode material promising particularly long lived batteries, due to its remarkable phase stability during (dis)charging of the cell. However, its usage is limited by its low intrinsic electronic conductivity. Introducing oxygen vacancies can be one method to overcome this drawback, possibly by altering the charge carrier transport mechanism. We use Hubbard corrected density-functional theory (DFT+U) to show that polaronic states in combination with a possible hopping mechanism can play a crucial role in the experimentally observed increase of electronic conductivity. To gauge polaronic charge mobility, we compute relative stabilities of different localization patterns and estimate polaron hopping barrier heights. With this we finally show how defect engineering can indeed raise the electronic conductivity of LTO up to the level of its ionic conductivity, thereby explaining first experimental results for reduced LTO.
Asymptotic convergence rate of the longest run in an inflating Bernoulli net.

In image detection, one problem is to test whether the set, though mostly consisting of uniformly scattered points, also contains a small fraction of points sampled from some (a priori unknown) curve, for example, a curve with $C^{\alpha}$-norm bounded by $\beta$. One approach is to analyze the data by counting membership in multiscale multianisotropic strips, which involves an algorithm that delves into the length of the path connecting many consecutive "significant" nodes. In this paper, we develop the mathematical formalism of this algorithm and analyze the statistical property of the length of the longest significant run. The rate of convergence is derived. Using percolation theory and random graph theory, we present a novel probabilistic model named pseudo-tree model. Based on the asymptotic results for pseudo-tree model, we further study the length of the longest significant run in an "inflating" Bernoulli net. We find that the probability parameter $p$ of significant node plays an important role: there is a threshold $p_c$, such that in the cases of $p p_c$, very different asymptotic behaviors of the length of the significant are observed. We apply our results to the detection of an underlying curvilinear feature and argue that we achieve the lowest possible detectable strength in theory.
Learning-Aided Deep Path Prediction for Sphere Decoding in Large MIMO Systems

In this paper, we propose a novel learning-aided sphere decoding (SD) scheme for large multiple-input–multiple-output systems, namely, deep path prediction-based sphere decoding (DPP-SD). In this scheme, we employ a neural network (NN) to predict the minimum metrics of the “deep” paths in sub-trees before commencing the tree search in SD. To reduce the complexity of the NN, we employ the input vector with a reduced dimension rather than using the original received signals and full channel matrix. The outputs of the NN, i.e., the predicted minimum path metrics, are exploited to determine the search order between the sub-trees, as well as to optimize the initial search radius, which may reduce the computational complexity of SD. For further complexity reduction, an early termination scheme based on the predicted minimum path metrics is also proposed. Our simulation results show that the proposed DPP-SD scheme provides a significant reduction in computational complexity compared with the conventional SD algorithm, despite achieving near-optimal performance.
Prediction in Locally Stationary Time Series

Abstract We develop an estimator for the high-dimensional covariance matrix of a locally stationary process with a smoothly varying trend and use this statistic to derive consistent predictors in nonstationary time series. In contrast to the currently available methods for this problem the predictor developed here does not rely on fitting an autoregressive model and does not require a vanishing trend. The finite sample properties of the new methodology are illustrated by means of a simulation study and a financial indices study. Supplementary materials for this article are available online.
Deep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future Trends

Research on speech processing has traditionally considered the task of designing hand-engineered acoustic features (feature engineering) as a separate distinct problem from the task of designing efficient machine learning (ML) models to make prediction and classification decisions. There are two main drawbacks to this approach: firstly, the feature engineering being manual is cumbersome and requires human knowledge; and secondly, the designed features might not be best for the objective at hand. This has motivated the adoption of a recent trend in speech community towards utilisation of representation learning techniques, which can learn an intermediate representation of the input signal automatically that better suits the task at hand and hence lead to improved performance. The significance of representation learning has increased with advances in deep learning (DL), where the representations are more useful and less dependent on human knowledge, making it very conducive for tasks like classification, prediction, etc. The main contribution of this paper is to present an up-to-date and comprehensive survey on different techniques of speech representation learning by bringing together the scattered research across three distinct research areas including Automatic Speech Recognition (ASR), Speaker Recognition (SR), and Speaker Emotion Recognition (SER). Recent reviews in speech have been conducted for ASR, SR, and SER, however, none of these has focused on the representation learning from speech---a gap that our survey aims to bridge.
Temporal-Spatial Neural Filter: Direction Informed End-to-End Multi-channel Target Speech Separation

Target speech separation refers to extracting the target speaker's speech from mixed signals. Despite the recent advances in deep learning based close-talk speech separation, the applications to real-world are still an open issue. Two main challenges are the complex acoustic environment and the real-time processing requirement. To address these challenges, we propose a temporal-spatial neural filter, which directly estimates the target speech waveform from multi-speaker mixture in reverberant environments, assisted with directional information of the speaker(s). Firstly, against variations brought by complex environment, the key idea is to increase the acoustic representation completeness through the jointly modeling of temporal, spectral and spatial discriminability between the target and interference source. Specifically, temporal, spectral, spatial along with the designed directional features are integrated to create a joint acoustic representation. Secondly, to reduce the latency, we design a fully-convolutional autoencoder framework, which is purely end-to-end and single-pass. All the feature computation is implemented by the network layers and operations to speed up the separation procedure. Evaluation is conducted on simulated reverberant dataset WSJ0-2mix and WSJ0-3mix under speaker-independent scenario. Experimental results demonstrate that the proposed method outperforms state-of-the-art deep learning based multi-channel approaches with fewer parameters and faster processing speed. Furthermore, the proposed temporal-spatial neural filter can handle mixtures with varying and unknown number of speakers and exhibits persistent performance even when existing a direction estimation error. Codes and models will be released soon.