Miracle-dz/UniFAR-FTUs huggingface dataset:
Drug Repurposing for COVID-19 via Knowledge Graph Completion

OBJECTIVE To discover candidate drugs to repurpose for COVID-19 using literature-derived knowledge and knowledge graph completion methods. METHODS We propose a novel, integrative, and neural network-based literature-based discovery (LBD) approach to identify drug candidates from both PubMed and COVID-19-focused research literature. Our approach relies on semantic triples extracted using SemRep (via SemMedDB). We identified an informative subset of semantic triples using filtering rules and an accuracy classifier developed on a BERT variant, and used this subset to construct a knowledge graph. Five SOTA, neural knowledge graph completion algorithms were used to predict drug repurposing candidates. The models were trained and assessed using a time slicing approach and the predicted drugs were compared with a list of drugs reported in the literature and evaluated in clinical trials. These models were complemented by a discovery pattern-based approach. RESULTS Accuracy classifier based on PubMedBERT achieved the best performance (F1= 0.854) in classifying semantic predications. Among five knowledge graph completion models, TransE outperformed others (MR = 0.923, Hits@1=0.417). Some known drugs linked to COVID-19 in the literature were identified, as well as some candidate drugs that have not yet been studied. Discovery patterns enabled generation of plausible hypotheses regarding the relationships between the candidate drugs and COVID-19. Among them, five highly ranked and novel drugs (paclitaxel, SB 203580, alpha 2-antiplasmin, pyrrolidine dithiocarbamate, and butylated hydroxytoluene) with their mechanistic explanations were further discussed. CONCLUSION We show that an LBD approach can be feasible for discovering drug candidates for COVID-19, and for generating mechanistic explanations. Our approach can be generalized to other diseases as well as to other clinical questions.
Truncating Temporal Diierences: on the Eecient Implementation of Td() for Reinforcement Learning

Temporal diierence (TD) methods constitute a class of methods for learning predictions in multi-step prediction problems, parameterized by a recency factor. Currently the most important application of these methods is to temporal credit assignment in reinforcement learning. Well known reinforcement learning algorithms, such as AHC or Q-learning, may be viewed as instances of TD learning. This paper examines the issues of the eecient and general implementation of TD() for arbitrary , for use with reinforcement learning algorithms optimizing the discounted sum of rewards. The traditional approach, based on eligibility traces, is argued to suuer from both ineeciency and lack of generality. The TTD (Truncated Temporal Diierences) procedure is proposed as an alternative, that indeed only approximates TD(), but requires very little computation per action and can be used with arbitrary function representation methods. The idea from which it is derived is fairly simple and not new, but probably unexplored so far. Encouraging experimental results are presented, suggesting that using > 0 with the TTD procedure allows one to obtain a signiicant learning speedup at essentially the same cost as usual TD(0) learning.
Personalized modeling of neurodegeneration determines dementia severity from EEG recordings

INTRODUCTION: Early identification of dementia is necessary for a timely onset of therapeutic care. However, cortical structural alterations associated with early dementia are difficult to disclose. METHODS: We developed a cortical model of dementia-related neurodegeneration accounting for slowing of local dynamics and global connectivity degradation. We collected EEG recordings at rest from subjects in healthy (HC), Subjective Cognitive Decline (SCD), and Mild Cognitive Impairment (MCI) condition. For each patient, we estimated neurodegeneration model parameters based on individual EEG recordings. RESULTS: Our model outperformed standard EEG analysis not only in discriminating between HC and MCI conditions (F1 score 0.95 vs 0.85) but also in identifying SCD patients with biological hallmarks of Alzheimer's disease in the cerebrospinal fluid (recall 0.87 vs 0.50). DISCUSSION: Personalized neurodegeneration models could both support classification of MCI and assess the risk of progression from SCD to Alzheimer based only on economical and non-invasive EEG recording
Facade Segmentation in the Wild

Façade Segmentation in the Wild Wamiq Reyaz Para Façade parsing is a fundamental problem in urban modeling that forms the backbone of a variety of tasks including procedural modeling, architectural analysis, urban reconstruction and quite often relies on semantic segmentation as the first step. With the shift to deep learning based approaches, existing small-scale datasets are the bottleneck for making further progress in façade segmentation and consequently façade parsing. In this thesis, we propose a new façade image dataset for semantic segmentation called PSV-22, which is the largest such dataset. We show that PSV-22 captures semantics of façades better than existing datasets. Additionally, we propose three architectural modifications to current state of the art deep-learning based semantic segmentation architectures and show that these modifications improve performance on our dataset and already existing datasets. Our modifications are generalizable to a large variety of semantic segmentation nets, but are façade-specific and employ heuristics which arise from the regular grid-like nature of façades. Furthermore, results show that our proposed architecture modifications improve the performance compared to baseline models as well as specialized segmentation approaches on façade datasets and are either close in, or improve performance on existing datasets. We show that deep models trained on existing data have a substantial performance reduction on our data, whereas models trained only on our data actually improve when evaluated on existing datasets. We intend to release the dataset publically in the future.
SVM training time reduction using vector quantization

In this paper, we describe a new method for training SVM on large data sets. Vector quantization is applied to reduce a large data set by replacing examples by prototypes. Training time for choosing optimal parameters is greatly reduced. Some experimental results yields to demonstrate that this method can reduce training time by a factor of 100, while preserving classification rate. Moreover this method allows to find a decision function with a low complexity when the training data set includes noisy or error examples.
Improving the Effectiveness of Software Testing through Test Case Reduction

the efficiency of software testing, which is based on a conventional attempt to reduce test cases that have to be tested for any given software. The approach utilizes the advantage of Regression Testing where fewer test cases would lessen time consumption of the testing as a whole. The technique also offers a means to perform test case generation automatically. Compared to one of the techniques in the literature where the tester has no option but to perform the test case generation manually, the proposed technique provides a better option. As for the test cases reduction, the technique uses simple algebraic conditions to assign fixed values to variables (Maximum, minimum and constant variables). By doing this, the variables values would be limited within a definite range, resulting in fewer numbers of possible test cases to process. The technique can also be used in program loops and arrays.
Depth Adjustment for Depth-Image-Based Rendering in 3D TV System ⋆

This paper focuses on how to adjust the perceived depth for depth-image-based rendering (DIBR) in a 3D TV system. The 3D image warping equations for DIBR are introduced first. In our system the shift-sensor camera setup is used, and the 3D image warping equations are simplified to a formula which implies horizontal sensor parallax. Then, the geometry of 3D TV display system is analyzed, and the perceived depth is expressed as a function of horizontal screen parallax. Finally, the relationship between horizontal sensor parallax and horizontal screen parallax is given. Based on this relationship, a depth adjustment method which controls the amount of horizontal sensor parallax of stereoscopic images by changing the values of variables in camera space during the processing of DIBR is proposed. Subjective evaluations show that the proposed depth adjustment method can generate comfortable stereoscopic images with different perceived depths that people desire.
A Machine Learning Perspective on Predictive Coding with PAQ8

PAQ8 makes use of several simple machine learning models and algorithms. We show how understanding PAQ8 enables us to improve the algorithms. We also present a broad range of new applications of PAQ8 to machine learning tasks including language modeling and adaptive text prediction, adaptive game playing, classification, and lossy compression using features acquired via unsupervised learning.
Random Matrices From Linear Codes and Wigner’s Semicircle Law

In this paper, we consider a new normalization of matrices obtained by choosing distinct codewords at random from linear codes over finite fields and find that under some natural algebraic conditions of the codes their empirical spectral distribution converges to Wigner’s semicircle law as the length of the codes goes to infinity. One such condition is that the dual distance of the codes is at least five. This is analogous to previous work on the empirical spectral distribution of similar matrices obtained in this fashion that converges to the Marchenko–Pastur law.
Counterfactual Off-Policy Training for Neural Dialogue Generation

Open-domain dialogue generation suffers from the data insufficiency problem due to the vast size of potential responses. In this paper, we propose to explore potential responses by counterfactual reasoning. Given an observed response, the counterfactual reasoning model automatically infers the outcome of an alternative policy that could have been taken. The resulting counterfactual response synthesized in hindsight is of higher quality than the response synthesized from scratch. Training on the counterfactual responses under the adversarial learning framework helps to explore the high-reward area of the potential response space. An empirical study on the DailyDialog dataset shows that our approach significantly outperforms the HRED model as well as the conventional adversarial learning approaches.
Randomized Algorithms for Large-scale Inverse Problems with General Regularizations

We shall investigate randomized algorithms for solving large-scale linear inverse problems with general regularizations. We first present some techniques to transform inverse problems of general form into the ones of standard form, then apply randomized algorithms to reduce large-scale systems of standard form to much smaller-scale systems and seek their regularized solutions in combination with some popular choice rules for regularization parameters. Then we will propose a second approach to solve large-scale ill-posed systems with general regularizations. This involves a new randomized generalized SVD algorithm that can essentially reduce the size of the original large-scale ill-posed systems. The reduced systems can provide approximate regularized solutions with about the same accuracy as the ones by the classical generalized SVD, and more importantly, the new approach gains obvious robustness, stability and computational time as it needs only to work on problems of much smaller size. Numerical results are given to demonstrated the efficiency of the algorithms.
On Optimal Deadlock Detection Scheduling

Deadlock detection scheduling is an important, yet often overlooked problem that can significantly affect the overall performance of deadlock handling. Excessive initiation of deadlock detection increases overall message usage, resulting in degraded system performance in the absence of deadlocks; while insufficient initiation of deadlock detection increases the deadlock persistence time, resulting in an increased deadlock resolution cost in the presence of deadlocks. The investigation of this performance tradeoff, however, is missing in the literature. This paper studies the impact of deadlock detection scheduling on the overall performance of deadlock handling. In particular, we show that there exists an optimal deadlock detection frequency that yields the minimum long-run mean average cost, which is determined by the message complexities of the deadlock detection and resolution algorithms being used, as well as the rate of deadlock formation, denoted as λ. For the best known deadlock detection and resolution algorithms, we show that the asymptotically optimal frequency of deadlock detection scheduling that minimizes the overall message overhead is O((λn)1/3), when the total number n of processes is sufficiently large. Furthermore, we show that in general fully distributed (uncoordinated) deadlock detection scheduling cannot be performed as efficiently as centralized (coordinated) deadlock detection scheduling. 1
Low-M-Rank Tensor Completion and Robust Tensor PCA

In this paper, we propose a new approach to solve low-rank tensor completion and robust tensor PCA. Our approach is based on some novel notion of (even-order) tensor ranks, to be called the M-rank, the symmetric M-rank, and the strongly symmetric M-rank. We discuss the connections between these new tensor ranks and the CP-rank and the symmetric CP-rank of an even-order tensor. We show that the M-rank provides a reliable and easy-computable approximation to the CP-rank. As a result, we propose to replace the CP-rank by the M-rank in the low-CP-rank tensor completion and robust tensor PCA. Numerical results suggest that our new approach based on the M-rank outperforms existing methods that are based on low-n-rank, t-SVD, and KBR approaches for solving low-rank tensor completion and robust tensor PCA when the underlying tensor has low CP-rank.
Novelty Detection in Sequential Data by Informed Clustering and Modeling

Novelty detection in discrete sequences is a challenging task, since deviations from the process generating the normal data are often small or intentionally hidden. Novelties can be detected by modeling normal sequences and measuring the deviations of a new sequence from the model predictions. However, in many applications data is generated by several distinct processes so that models trained on all the data tend to over-generalize and novelties remain undetected. We pro-pose to approach this challenge through decomposition: by clustering the data we break down the problem, obtaining simpler modeling task in each cluster which can be modeled more accurately. However, this comes at a trade-oﬀ, since the amount of training data per cluster is reduced. This is a particular problem for discrete sequences where state-of-the-art models are data-hungry. The success of this approach thus depends on the quality of the clustering, i.e., whether the individual learning problems are suﬃciently simpler than the joint problem. While clustering discrete sequences automatically is a challenging and domain-speciﬁc task, it is often easy for human domain experts, given the right tools. In this paper, we adapt a state-of-the-art visual analytics tool for discrete sequence clustering to obtain informed clusters from domain experts and use LSTMs to model each cluster individually. Our extensive empirical evaluation indicates that this informed clustering outperforms automatic ones and that our approach outperforms state-of-the-art novelty detection methods for discrete sequences in three real-world application scenarios. In particular, decomposition outperforms a global model despite less training data on each individual cluster.
Post-Younger Dryas seasonality in the North American midcontinent region as recorded in Lake Huron varved sediments

A sediment core from northwestern Lake Huron contains a sequence of red and gray layered sediments that are postglacial and early Holocene glaciolacustrine varves. Two hundred and sixty four couplets were identified and measured using a newly developed image-analysis technique that produces records of seasonal and total varve thickness. On the premise that changes in varve thickness can serve as a proxy for climate variability, we use this information, along with oxygen-isotope records and seismic reflection profile data, to characterize a period immediately following the Younger Dryas. A time-series analysis of the thickness data was performed using two different techniques to characterize changes in spectral density that occur throughout the time span of the record. The results indicate that there are concentrations in variance that occur in the same frequency band as the quasi-periodic El Nino - Southern oscillation (ENSO) and the quasi-biennial oscillation (QBO). The effects of ENSO-like variability h.
Suppressed Due to Excessive Length Dialog Data Template User Goal NLG Rule Data generator Template Interact Input : Generate Output : User Simulator Train Framework State 2 Seq

Recent reinforcement learning algorithms for task-oriented dialogue system absorbs a lot of interest. However, an unavoidable obstacle for training such algorithms is that annotated dialogue corpora are often unavailable. One of the popular approaches addressing this is to train a dialogue agent with a user simulator. Traditional user simulators are built upon a set of dialogue rules and therefore lack response diversity. This severely limits the simulated cases for agent training. Later data-driven user models work better in diversity but suffer from data scarcity problem. To remedy this, we design a new corpus-free framework that taking advantage of their benefits. The framework builds a user simulator by first generating diverse dialogue data from templates and then build a new State2Seq user simulator on the data. To enhance the performance, we propose the State2Seq user simulator model to efficiently leverage dialogue state and history. Experiment results on an open dataset show that our user simulator helps agents achieve an improvement of 6.36% on success rate. State2Seq model outperforms the seq2seq baseline for 1.9 F-score.
A convergent hierarchy of semidefinite programs characterizing the set of quantum correlations

We are interested in the problem of characterizing the correlations that arise when performing local measurements on separate quantum systems. In a previous work (Navascués et al 2007 Phys. Rev. Lett. 98 010401), we introduced an infinite hierarchy of conditions necessarily satisfied by any set of quantum correlations. Each of these conditions could be tested using semidefinite programming. We present here new results concerning this hierarchy. We prove in particular that it is complete, in the sense that any set of correlations satisfying every condition in the hierarchy has a quantum representation in terms of commuting measurements. Although our tests are conceived to rule out non-quantum correlations, and can in principle certify that a set of correlations is quantum only in the asymptotic limit where all tests are satisfied, we show that in some cases it is possible to conclude that a given set of correlations is quantum after performing only a finite number of tests. We provide a criterion to detect when such a situation arises, and we explain how to reconstruct the quantum states and measurement operators reproducing the given correlations. Finally, we present several applications of our approach. We use it in particular to bound the quantum violation of various Bell inequalities.
An experimental study of turbulent relative dispersion models

We report measurements of the spreading rate of pairs of tracer particles in an intensely turbulent laboratory water flow. We compare our measurements of this turbulent relative dispersion with the longstanding work of Richardson and Batchelor, and find excellent agreement with Batchelor's predictions. The distance neighbour function, the probability density function of the relative dispersion, is measured and compared with existing models. We also investigate the recently proposed exit time analysis of relative dispersion.
Sub-optimality of treating interference as noise in the cellular uplink

Despite the simplicity of the scheme of treating interference as noise (TIN), it was shown to be sum-capacity optimal in the noisy interference regime of the Gaussian 2-user interference channel in [1]-[3]. In this paper, an interference network consisting of a point-to-point channel interfering with a multiple access channel (MAC) is considered, with focus on the weak interference scenario. Naive TIN in this network is performed by using Gaussian codes at the transmitters, joint decoding at the MAC receiver while treating interference as noise, and single user decoding at the point-to-point receiver while treating both interferers as noise. It is shown that this naive TIN scheme is never optimal in this scenario. In fact, a scheme that combines both time division multiple access and TIN outperforms the naive TIN scheme. An upper bound on the sum-capacity of the given network is also derived.
Improving Robustness of Deep Reinforcement Learning Agents: Environment Attacks based on Critic Networks

To improve policy robustness of deep reinforcement learning agents, a line of recent works focus on producing disturbances of the environment. Existing approaches of the literature to generate meaningful disturbances of the environment are adversarial reinforcement learning methods. These methods set the problem as a two-player game between the protagonist agent, which learns to perform a task in an environment, and the adversary agent, which learns to disturb the protagonist via mod-iﬁcations of the considered environment. Both protagonist and adversary are trained with deep reinforcement learning algorithms. Alternatively, we propose in this paper to build on gradient-based adversarial attacks, usually used for classiﬁcation tasks for instance, that we apply on the critic network of the protagonist to identify eﬃcient disturbances of the environment. Rather than learning an attacker policy, which usually reveals as very complex and unstable, we leverage the knowledge of the critic network of the protagonist, to dynamically complexify the task at each step of the learning process. We show that our method, while being faster and lighter, leads to signiﬁcantly better improvements in policy robustness than existing methods of the literature.
Incomputability after Alan Turing

Incomputability as a mathematical notion arose from work of Alan Turing and Alonzo Church in the 1930s. Like Turing himself, it attracted less attention than it deserved beyond the confines of mathematics. Today our experiences in computer science, physics, biology, artificial intelligence, economics and the humanities point to the importance of the notion for understanding the world around us. This article takes a Turing centenary look at how the interface between the computable world and the incomputable formed a central theme in Turing's work - from the early establishment of the standard model of the stored program computer, to the late work on emergence of form in nature, and the first approaches to understanding and simulating human intelligence. Turing's thinking was remarkably prescient, and his legacy still impacts on much of our work. The incomputable may turn out to be a specially important part of the legacy, with consequences that Alan Turing himself could not have envisaged.