Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-Supervised Detection of Contextual Synonyms in a Multi-Class Setting: Phenotype Annotation Use Case

Contextualised word embeddings is a powerful tool to detect contextual synonyms. However, most of the current state-of-the-art (SOTA) deep learning concept extraction methods remain supervised and underexploit the potential of the context. In this paper, we propose a self-supervised pre-training approach which is able to detect contextual synonyms of concepts being training on the data created by shallow matching. We apply our methodology in the sparse multi-class setting (over 15,000 concepts) to extract phenotype information from electronic health records. We further investigate data augmentation techniques to address the problem of the class sparsity. Our approach achieves a new SOTA for the unsupervised phenotype concept annotation on clinical text on F1 and Recall outperforming the previous SOTA with a gain of up to 4.5 and 4.0 absolute points, respectively. After fine-tuning with as little as 20% of the labelled data, we also outperform BioBERT and ClinicalBERT. The extrinsic evaluation on three ICU benchmarks also shows the benefit of using the phenotypes annotated by our model as features.
Recommendation System Simulations: A Discussion of Two Key Challenges

As recommendation systems become increasingly standard for online platforms, simulations provide an avenue for understanding the impacts of these systems on individuals and society. When constructing a recommendation system simulation, there are two key challenges: first, defining a model for users selecting or engaging with recommended items and second, defining a mechanism for users encountering items that are not recommended to the user directly by the platform, such as by a friend sharing specific content. This paper will delve into both of these challenges, reviewing simulation assumptions from existing research and proposing alternative assumptions. We also include a broader discussion of the limitations of simulations and outline of open questions in this area.
Recommending Researchers in Machine Learning based on Author-Topic Model

The aim of this paper is to uncover the researchers in machine learning using author-topic model (ATM). We collect 16,855 scientific papers from six top journals in the field of machine learning published from 1997 to 2016 and analyze them using ATM. The dataset is broken down into 4 intervals to identify the top researchers and find similar researchers using their similarity score. The similarity score is calculated using Hellinger distance. The researchers are plotted using t-SNE, which reduces the dimensionality of the data while keeping the same distance between the points. The analysis of our study helps the upcoming researchers to find the top researchers in their area of interest.
OCTAVA: An open-source toolbox for quantitative analysis of optical coherence tomography angiography images

Optical coherence tomography angiography (OCTA) performs non-invasive visualization and characterization of microvasculature in research and clinical applications mainly in ophthalmology and dermatology. A wide variety of instruments, imaging protocols, processing methods and metrics have been used to describe the microvasculature, such that comparing different study outcomes is currently not feasible. With the goal of contributing to standardization of OCTA data analysis, we report a user-friendly, open-source toolbox, OCTAVA (OCTA Vascular Analyzer), to automate the pre-processing, segmentation, and quantitative analysis of en face OCTA maximum intensity projection images in a standardized workflow. We present each analysis step, including optimization of filtering and choice of segmentation algorithm, and definition of metrics. We perform quantitative analysis of OCTA images from different commercial and non-commercial instruments and samples and show OCTAVA can accurately and reproducibly determine metrics for characterization of microvasculature. Wide adoption could enable studies and aggregation of data on a scale sufficient to develop reliable microvascular biomarkers for early detection, and to guide treatment, of microvascular disease.
Hamiltonian study of the asymptotic symmetries of gauge theories

Asymptotic symmetries are a general and important feature of theories with long-ranging fields, such as gravity, electromagnetism, and Yang-Mills. They appear in the formalism once the analytic behaviour of fields near infinity is specified and have received a renewed interest in the last years after a possible connection with the information-loss paradox has been conjectured. One of the various methods used to study the asymptotic symmetries of field theories relies on the Hamiltonian formalism and was introduced in the seminal work of Henneaux and Troessaert, who successfully applied it to the case of gravity and electrodynamics. The main advantage of this approach is that the study of the asymptotic symmetries ensues from clear-cut first principles. After an extensive review of how the Hamiltonian approach to study asymptotic symmetries of gauge theories works, we apply these methods to two specific situations of physical interest. First, we deal with the non-abelian Yang-Mills case and we show that the above principles lead to trivial asymptotic symmetries (nothing else than the Poincar\'e group) and, as a consequence, to a vanishing total colour charge. This is a new and somewhat unexpected result. It implies that no globally colour-charged states exist in classical non-abelian Yang-Mills theory. The second situation considered is a scalar field minimally-coupled to an abelian gauge field, which can be used to study, at the same time, two specific cases: scalar electrodynamics and the abelian Higgs model. We show that the situation in scalar electrodynamics amply depends on whether the scalar field is massive or massless, insofar as, in the latter case, one cannot canonically implement asymptotic symmetries. Furthermore, we illustrate that, in the abelian Higgs model, the asymptotic canonical symmetries reduce to the Poincar\'e group in an unproblematic fashion.
Insider Detection using Deep Autoencoder and Variational Autoencoder Neural Networks

Insider attacks are one of the most challenging cybersecurity issues for companies, businesses and critical infrastructures. Despite the implemented perimeter defences, the risk of this kind of attack is still very high. In fact, the detection of insider attacks is a very complicated security task and presents a serious challenge to the research community. In this paper, we aim to address this issue by using deep learning algorithms Autoencoder and Variational Autoencoder deep. We will especially investigate the usefulness of applying these algorithms to automatically defend against potential internal threats, without human intervention. The effectiveness of these two models is evaluated on the public dataset CERT dataset (CERT r4.2). This version of the CERT Insider Threat Test dataset includes both benign and malicious activities generated from 1000 simulated users. The comparison results with other models show that the Variational Autoencoder neural network provides the best overall performance with a greater detection accuracy and a reasonable false positive rate
Automata for Dynamic Answer Set Solving: Preliminary Report

We explore different ways of implementing temporal constraints expressed in an extension of Answer Set Programming (ASP) with language constructs from dynamic logic. Foremost, we investigate how automata can be used for enforcing such constraints. The idea is to transform a dynamic constraint into an automaton expressed in terms of a logic program that enforces the satisfaction of the original constraint. What makes this approach attractive is its independence of time stamps and the potential to detect unsatisfiability. On the one hand, we elaborate upon a transformation of dynamic formulas into alternating automata that relies on meta-programming in ASP. This is the first application of reification applied to theory expressions in gringo. On the other hand, we propose two transformations of dynamic formulas into monadic second-order formulas. These can then be used by off-the-shelf tools to construct the corresponding automata. We contrast both approaches empirically with the one of the temporal ASP solver telingo that directly maps dynamic constraints to logic programs. Since this preliminary study is restricted to dynamic formulas in integrity constraints, its implementations and (empirical) results readily apply to conventional linear dynamic logic, too.
Network Modulation Synthesis: New Algorithms for Generating Musical Audio Using Autoencoder Networks

A new framework is presented for generating musical audio using autoencoder neural networks. With the presented framework, called network modulation synthesis, users can create synthesis architectures and use novel generative algorithms to more easily move through the complex latent parameter space of an autoencoder model to create audio. Implementations of the new algorithms are provided for the open-source CANNe synthesizer network, and can be applied to other autoencoder networks for audio synthesis. Spectrograms and time-series encoding analysis demonstrate that the new algorithms provide simple mechanisms for users to generate time-varying parameter combinations, and therefore auditory possibilities, that are difficult to create by generating audio from handcrafted encodings.
QSSA: an SSA-based IR for Quantum computing

Quantum computing hardware has progressed rapidly. Simultaneously, there has been a proliferation of programming languages and program optimization tools for quantum computing. Existing quantum compilers use intermediate representations (IRs) where quantum programs are described as circuits. Such IRs fail to leverage existing work on compiler optimizations. In such IRs, it is non-trivial to statically check for physical constraints such as the no-cloning theorem, which states that qubits cannot be copied. We introduce QSSA, a novel quantum IR based on static single assignment (SSA) that enables decades of research in compiler optimizations to be applied to quantum compilation. QSSA models quantum operations as being side-effect-free. The inputs and outputs of the operation are in one-to-one correspondence; qubits cannot be created or destroyed. As a result, our IR supports a static analysis pass that verifies no-cloning at compile-time. The quantum circuit is fully encoded within the def-use chain of the IR, allowing us to leverage existing optimization passes on SSA representations such as redundancy elimination and dead-code elimination. Running our QSSA-based compiler on the QASMBench and IBM Quantum Challenge datasets, we show that our optimizations perform comparably to IBM’s Qiskit quantum compiler infrastructure. QSSA allows us to represent, analyze, and transform quantum programs using the robust theory of SSA representations, bringing quantum compilation into the realm of well-understood theory and practice.
Light pseudoscalar meson masses under strong magnetic fields within the SU(3) Nambu–Jona-Lasinio model

We calculate the pole masses of pseudoscalar mesons in a strongly magnetized medium within the framework of the SU(3) Nambu-Jona–Lasinio model, using a magnetic fieldindependent regularization scheme. We employ both a constant and a magnetic fielddependent coupling G(B), the latter being fitted to reproduce lattice QCD results for the pseudocritical chiral transition temperature. Numerical results for the pole masses are obtained for definite parametrizations of the model. For neutral mesons, the use of G(B) provides closer agreement with lattice QCD results, which reveal a decrease of the mass with the external field. On the contrary, charged mesons masses are enhanced by B, showing no sign of the non-monotonous behavior found in recent lattice QCD simulations. ∗ sidney.avancini@ufsc.br † joana.sodre@gmail.com ‡ coppola@tandar.cnea. gov.ar § scoccola@tandar.cnea. gov.ar
Extensive Lensing Survey of Optical and Near-infrared Dark Objects (El Sonido): HST H-faint Galaxies behind 101 Lensing Clusters

We present a Spitzer/IRAC survey of H-faint (H 160 ≳ 26.4, < 5σ) sources in 101 lensing cluster fields. Across a CANDELS/Wide-like survey area of ∼648 arcmin2 (effectively ∼221 arcmin2 in the source plane), we have securely discovered 53 sources in the IRAC Channel-2 band (CH2, 4.5 μm; median CH2 = 22.46 ± 0.11 AB mag) that lack robust HST/WFC3-IR F160W counterparts. The most remarkable source in our sample, namely ES-009 in the field of Abell 2813, is the brightest H-faint galaxy at 4.5 μm known so far (CH2 = 20.48 ± 0.03 AB mag). We show that the H-faint sources in our sample are massive (median M star = 1010.3±0.3 M ⊙), star-forming (median star formation rate =100−40+60 M ⊙ yr−1), and dust-obscured (A V = 2.6 ± 0.3) galaxies around a median photometric redshift of z = 3.9 ± 0.4. The stellar continua of 14 H-faint galaxies can be resolved in the CH2 band, suggesting a median circularized effective radius (R e,circ; lensing corrected) of 1.9 ± 0.2 kpc and <1.5 kpc for the resolved and whole samples, respectively. This is consistent with the sizes of massive unobscured galaxies at z ∼ 4, indicating that H-faint galaxies represent the dusty tail of the distribution of a wider galaxy population. Comparing with the ALMA dust continuum sizes of similar galaxies reported previously, we conclude that the heavy dust obscuration in H-faint galaxies is related to the compactness of both stellar and dust continua (R e,circ ∼ 1 kpc). These H-faint galaxies make up 16−7+13 % of the galaxies in the stellar-mass range of 1010 − 1011.2 M ⊙ at z = 3 ∼ 5, contributing to 8−4+8 % of the cosmic star formation rate density in this epoch and likely tracing the early phase of massive galaxy formation.
Why it takes a village to manage and share data

Implementation plans for the National Institutes of Health policy for data management and sharing, which takes effect in 2023, provide an opportunity to reflect on the stakeholders, infrastructures, practice, economics, and sustainability of data sharing. Responsibility for fulfilling data sharing requirements tends to fall on principal investigators, whereas it takes a village of stakeholders to construct, manage, and sustain the necessary knowledge infrastructure for disseminating data products. Individual scientists have mixed incentives, and many disincentives to share data, all of which vary by research domain, methods, resources, and other factors. Motivations and investments for data sharing also vary widely among academic institutional stakeholders such as university leadership, research computing, libraries, and individual schools and departments. Stakeholder concerns are interdependent along many dimensions, seven of which are explored: what data to share; context and credit; are using big data to track students in an effort to boost graduation rates, but it comes at a cost . American Public Media.
Feasibility of Proof of Authority as a Consensus Protocol Model

Blockchain is a type of decentralized distributed network which acts as an immutable digital ledger. Despite the absence of any central governing authority to validate the blocks in the ledger, it is considered secure and immutable due to the consensus protocol among various nodes of the network. A consensus algorithm is a mechanism that guarantees the reliability of the blockchain and helps all connected nodes or peers to reach common ground regarding the present state of the blockchain network thus an ideal consensus algorithm must be secure, reliable, and fast. There are several different algorithms to reach a consensus among the nodes thus this article seeks to test the practicality of Proof of Authority in the blockchain network as a consensus algorithm and its comparison with current mainstream consensus algorithms.
GamePlan: Game-Theoretic Multi-Agent Planning with Human Drivers at Intersections, Roundabouts, and Merging

We present a new method for multi-agent planning involving human drivers and autonomous vehicles (AVs) in unsignaled intersections, roundabouts, and during merging. In multi-agent planning, the main challenge is to predict the actions of other agents, especially human drivers, as their intentions are hidden from other agents. Our algorithm uses game theory to develop a new auction, called GamePlan, that directly determines the optimal action for each agent based on their driving style (which is observable via commonly available sensors). GamePlan assigns a higher priority to more aggressive or impatient drivers and a lower priority to more conservative or patient drivers; we theoretically prove that such an approach is game-theoretically optimal prevents collisions and deadlocks. We compare our approach with prior state-of-the-art auction techniques including economic auctions, time-based auctions (first-in first-out), and random bidding and show that each of these methods result in collisions among agents when taking into account driver behavior. We additionally compare with methods based on deep reinforcement learning, deep learning, and game theory and present our benefits over these approaches. Finally, we show that our approach can be implemented in the real-world with human drivers.
From Alignment to Assignment: Frustratingly Simple Unsupervised Entity Alignment

Cross-lingual entity alignment (EA) aims to find the equivalent entities between crosslingual KGs (Knowledge Graphs), which is a crucial step for integrating KGs. Recently, many GNN-based EA methods are proposed and show decent performance improvements on several public datasets. However, existing GNN-based EA methods inevitably inherit poor interpretability and low efficiency from neural networks. Motivated by the isomorphic assumption of GNN-based methods, we successfully transform the cross-lingual EA problem into an assignment problem. Based on this re-definition, we propose a frustratingly Simple but Effective Unsupervised entity alignment method (SEU) without neural networks. Extensive experiments have been conducted to show that our proposed unsupervised approach even beats advanced supervised methods across all public datasets while having high efficiency, interpretability, and stability.
Two new compartmental epidemiological models and their equilibria

Compartmental models have long served as important tools in mathematical epidemiology, with their usefulness highlighted by the recent COVID-19 pandemic. However, most of the classical models fail to account for certain features of this disease and others like it, such as the ability of exposed individuals to recover without becoming infectious, or the possibility that asymptomatic individuals can indeed transmit the disease but at a lesser rate than the symptomatic. Furthermore, the rise of new disease variants and the imperfection of vaccines suggest that concept of endemic equilibrium is perhaps more pertinent than that of herd immunity. Here we propose a new compartmental epidemiological model and study its equilibria, characterizing the stability of both the endemic and disease-free equilibria in terms of the basic reproductive number. Moreover, we introduce a second compartmental model, generalizing our first, which accounts for vaccinated individuals, and begin an analysis of its equilibria.
Multiagent Online Learning in Time-Varying Games

We examine the long-run behavior of multiagent online learning in games that evolve over time. Specifically, we focus on a wide class of policies based on mirror descent, and we show that the induced sequence of play (a) converges to a Nash equilibrium in time-varying games that stabilize in the long run to a strictly monotone limit, and (b) it stays asymptotically close to the evolving equilibrium of the sequence of stage games (assuming they are strongly monotone). Our results apply to both gradient- and payoff-based feedback—that is, when players only get to observe the payoffs of their chosen actions.
TagPick: A System for Bridging Micro-Video Hashtags and E-commerce Categories

Hashtag, a product of user tagging behavior, which can well describe the semantics of the user-generated content personally over social network applications, e.g., the recently popular micro-videos. Hashtags have been widely used to facilitate various micro-video retrieval scenarios, such as search engine and categorization. In order to leverage hashtags on micro-media platform for effective e-commerce marketing campaign, there is a demand from e-commerce industry to develop a mapping algorithm bridging its categories and micro-video hashtags. In this demo paper, we therefore proposed a novel solution called TagPick that incorporates clues from all user behavior metadata (hashtags, interactions, multimedia information) as well as relational data (graph-based network) into a unified system to reveal the correlation between e-commerce categories and hashtags in industrial scenarios. In particular, we provide a tag-level popularity strategy to recommend the relevant hashtags for e-Commerce platform (e.g., eBay).
Pointspectrum: Equivariance Meets Laplacian Filtering for Graph Representation Learning

Graph Representation Learning (GRL) has revolutionized learning on graphs, by combining the graph’s structural information with node/edge attributes. While Graph Neural Networks (GNNs) have been used in state-of-the-art GRL architectures, they suffer from over smoothing when many GNN layers need to be stacked. In a different approach, spectral methods based on graph filtering have emerged addressing over smoothing; however, up to now, they employ traditional neural networks that cannot efficiently exploit the structure of graph data. Motivated by this, we propose PointSpectrum, a spectral method that incorporates a set equivariant network to account for graph structure. PointSpectrum enhances the efficiency and expressiveness of spectral methods, while it outperforms or competes with state-of-the-art GRL methods. Overall, PointSpectrum addresses over smoothing through graph filtering and captures a graph’s structure through set equivariance. Our findings are promising for this architectural shift for (spectral) GRL methods.
Teaching Autoregressive Language Models Complex Tasks By Demonstration

This paper demonstrates that by fine-tuning an autoregressive language model (GPT-Neo) on appropriately structured step-by-step demonstrations, it is possible to teach it to execute a mathematical task that has previously proved difficult for Transformers - longhand modulo operations - with a relatively small number of examples. Specifically, we fine-tune GPT-Neo to solve the numbers__div_remainder task from the DeepMind Mathematics Dataset; Saxton et al. (arXiv:1904.01557) reported below 40% accuracy on this task with 2 million training examples. We show that after fine-tuning on 200 appropriately structured demonstrations of solving long division problems and reporting the remainders, the smallest available GPT-Neo model achieves over 80% accuracy. This is achieved by constructing an appropriate dataset for fine-tuning, with no changes to the learning algorithm. These results suggest that fine-tuning autoregressive language models on small sets of well-crafted demonstrations may be a useful paradigm for enabling individuals without training in machine learning to coax such models to perform some kinds of complex multi-step tasks.