Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of a Decentralised Digital Token Architecture for Public Transport

Digitisation is often viewed as beneficial to a user. Where originally people would physically have to identify to a service, pay for a ticket in cash, or go into a library to access a book, people can now achieve all of this through a click of a button. While these actions may seem functionally identical to their analogue counterparts, they come with one important difference. Namely, in the digital case, a user's actions are automatically recorded. The recording of user's interactions presents a problem because this information can be used outside the control of the person whom it concerns. This issue is only exacerbated by the centralisation of these aforementioned services' authentication mechanisms permitting the collection of even more data. This work aims to motivate the need and establish the feasibility for the application of a privacy-enhancing digital token management service to public transit. A proof-of-concept implementation of the Decentralised Digital Identity Architecture proposed by Goodell and Aste is developed. This implementation was optimised for the public transport use case. Finally, its performance is tested in a local environment to better understand the technical challenges and assess such a system's technical feasibility in a production setting. It was observed that for loads between 1 and 5 requests per second the proof-of-concept performs within acceptable limits with a maximum median response time of 334 milliseconds. Above 5 requests per second response times drastically increase due to hardware bottlenecks. It was concluded that the demonstrated throughput and latency shows that the system can feasibly compete with solutions currently in use. Yet, further work is needed to demonstrate these performance characteristics in an environment similar to that experienced in production.
Bayesian Landmark-based Shape Analysis of Tumor Pathology Images

Medical imaging is a form of technology that has revolutionized the medical field in the past century. In addition to radiology imaging of tumor tissues, digital pathology imaging, which captures histological details in high spatial resolution, is fast becoming a routine clinical procedure for cancer diagnosis support and treatment planning. Recent developments in deep-learning methods facilitate the segmentation of tumor regions at almost the cellular level from digital pathology images. The traditional shape features that were developed for characterizing tumor boundary roughness in radiology are not applicable. Reliable statistical approaches to modeling tumor shape in pathology images are in urgent need. In this paper, we consider the problem of modeling a tumor boundary with a closed polygonal chain. A Bayesian landmark-based shape analysis (BayesLASA) model is proposed to partition the polygonal chain into mutually exclusive segments to quantify the boundary roughness piecewise. Our fully Bayesian inference framework provides uncertainty estimates of both the number and locations of landmarks. The BayesLASA outperforms a recently developed landmark detection model for planar elastic curves in terms of accuracy and efficiency. We demonstrate how this model-based analysis can lead to sharper inferences than ordinary approaches through a case study on the 246 pathology images from 143 non-small cell lung cancer patients. The case study shows that the heterogeneity of tumor boundary roughness predicts patient prognosis (p-value < 0.001). This statistical methodology not only presents a new model for characterizing a digitized object's shape features by using its landmarks, but also provides a new perspective for understanding the role of tumor surface in cancer progression.
Two-sample test based on maximum variance discrepancy

In this article, we introduce a novel discrepancy called the maximum variance discrepancy for the purpose of measuring the difference between two distributions in Hilbert spaces that cannot be found via the maximum mean discrepancy. We also propose a two-sample goodness of fit test based on this discrepancy. We obtain the asymptotic null distribution of this two-sample test, which provides an efficient approximation method for the null distribution of the test.
Game Theoretic Malware Detection

Large software platforms (e.g., mobile app stores, social media, email service providers) must ensure that files on their platform do not contain malicious code. Platform hosts use security tools to analyze those files for potential malware. However, given the expensive runtimes of tools coupled with the large number of exchanged files, platforms are not able to run all tools on every incoming file. Moreover, malicious parties look to find gaps in the coverage of the analysis tools, and exchange files containing malware that exploits these vulnerabilities. To address this problem, we present a novel approach that models the relationship between malicious parties and the security analyst as a leader-follower Stackelberg security game. To estimate the parameters of our model, we have combined the information from the VirusTotal dataset with the more detailed reports from the National Vulnerability Database. Compared to a set of natural baselines, we show that our model computes an optimal randomization over sets of available security analysis tools.
Graph Neural Networks for Particle Tracking and Reconstruction

Machine learning methods have a long history of applications in high energy physics (HEP). Recently, there is a growing interest in exploiting these methods to reconstruct particle signatures from raw detector data. In order to benefit from modern deep learning algorithms that were initially designed for computer vision or natural language processing tasks, it is common practice to transform HEP data into images or sequences. Conversely, graph neural networks (GNNs), which operate on graph data composed of elements with a set of features and their pairwise connections, provide an alternative way of incorporating weight sharing, local connectivity, and specialized domain knowledge. Particle physics data, such as the hits in a tracking detector, can generally be represented as graphs, making the use of GNNs natural. In this chapter, we recapitulate the mathematical formalism of GNNs and highlight aspects to consider when designing these networks for HEP data, including graph construction, model architectures, learning objectives, and graph pooling. We also review promising applications of GNNs for particle tracking and reconstruction in HEP and summarize the outlook for their deployment in current and future experiments.
Huskysort

Much of the copious literature on the subject of sorting has concentrated on minimizing the number of comparisons and/or exchanges/copies. However, a more appropriate yardstick for the performance of sorting algorithms is based on the total number of array accesses that are required (the"work"). For a sort that is based on divide-and-conquer (including iterative variations on that theme), we can divide the work into linear, i.e. $\textbf{O}(N)$, work and linearithmic, i.e. $\textbf{O}(N log N)$, work. An algorithm that moves work from the linearithmic phase to the linear phase may be able to reduce the total number of array accesses and, indirectly, processing time. This paper describes an approach to sorting which reduces the number of expensive comparisons in the linearithmic phase as much as possible by substituting inexpensive comparisons. In Java, the two system sorts are dual-pivot quicksort (for primitives) and Timsort for objects. We demonstrate that a combination of these two algorithms can run significantly faster than either algorithm alone for the types of objects which are expensive to compare. We call this improved sorting algorithm Huskysort.
Maxwell-Bloch modeling of an x-ray pulse amplification in a 1D photonic crystal.

We present an implementation of the Maxwell-Bloch (MB) formalism for the study of x-ray emission dynamics from periodic multilayer materials whether they are artificial or natural. The treatment is based on a direct Finite-Difference-Time-Domain (FDTD) solution of Maxwell equations combined with Bloch equations incorporating a random spontaneous emission noise. Besides periodicity of the material, the treatment distinguishes between two kinds of layers, those being active (or resonant) and those being off-resonance. The numerical model is applied to the problem of $K\alpha$ emission in multilayer materials where the population inversion could be created by fast inner-shell photoionization by an x-ray free-electron-laser (XFEL). Specificities of the resulting amplified fluorescence in conditions of Bragg diffraction is illustrated by numerical simulations. The corresponding pulses could be used for specific investigations of non-linear interaction of x-rays with matter.
Stochastic Approximation with Markov Noise: Analysis and applications in reinforcement learning

We present for the first time an asymptotic convergence analysis of two time-scale stochastic approximation driven by "controlled" Markov noise. In particular, the faster and slower recursions have non-additive controlled Markov noise components in addition to martingale difference noise. We analyze the asymptotic behavior of our framework by relating it to limiting differential inclusions in both time scales that are defined in terms of the ergodic occupation measures associated with the controlled Markov processes. Using a special case of our results, we present a solution to the off-policy convergence problem for temporal-difference learning with linear function approximation. We compile several aspects of the dynamics of stochastic approximation algorithms with Markov iterate-dependent noise when the iterates are not known to be stable beforehand. We achieve the same by extending the lock-in probability (i.e. the probability of convergence to a specific attractor of the limiting o.d.e. given that the iterates are in its domain of attraction after a sufficiently large number of iterations (say) n_0) framework to such recursions. We use these results to prove almost sure convergence of the iterates to the specified attractor when the iterates satisfy an "asymptotic tightness" condition. This, in turn, is shown to be useful in analyzing the tracking ability of general "adaptive" algorithms. Finally, we obtain the first informative error bounds on function approximation for the policy evaluation algorithm proposed by Basu et al. when the aim is to find the risk-sensitive cost represented using exponential utility. We show that this happens due to the absence of difference term in the earlier bound which is always present in all our bounds when the state space is large.
VM Matters: A Comparison of WASM VMs and EVMs in the Performance of Blockchain Smart Contracts

Beyond an emerging popular web applications runtime supported in almost all commodity browsers, WebAssembly (WASM) is further regarded to be the next-generation execution environment for blockchain-based applications. Indeed, many popular blockchain platforms such as EOSIO and NEAR have adopted WASM-based execution engines. Most recently, WASM has been favored by Ethereum, the largest smart contract platform, to replace the state-of-the-art EVM. However, whether and how well current WASM outperforms EVM on blockchain clients is still unknown. This paper conducts the first measurement study to understand the performance on WASM VMs and EVM for executing smart contracts for blockchain-based applications. To our surprise, the current WASM VM does not provide expected satisfactory performance. The overhead introduced by WASM is really non-trivial. Our results shed the light on challenges when deploying WASM in practice, and provide insightful implications for improvement space.
Homotopical Approach to Tensor Products of Modules.

Category theory provides a means through which many far-ranging fields of mathematics can be related by their similar structure. In a paper by Robinson [2], this interconnectivity afforded by categorical perspectives allowed for the realization of torsion products as the homotopy groups of a topological space, which is itself constructed for this express purpose. However, even stating this result formally requires a multitude of preliminaries in algebra, topology, and category theory. The goal of this document is to present a self-contained guide to the fundamental concepts and results, with few proofs, required to do work with this kind of mathematics in hopes of making the field of homotopical algebra more accessible. We only assume familiarity with topological spaces and groups, so it is approachable from an undergraduate level. This project culminates in a discussion of the result of Robinson mentioned above along with a computation as a proof of concept.
Aligning Hyperbolic Representations: an Optimal Transport-based approach

Hyperbolic-spaces are better suited to represent data with underlying hierarchical relationships, e.g., tree-like data. However, it is often necessary to incorporate, through alignment, different but related representations meaningfully. This aligning is an important class of machine learning problems, with applications as ontology matching and cross-lingual alignment. Optimal transport (OT)-based approaches are a natural choice to tackle the alignment problem as they aim to find a transformation of the source dataset to match a target dataset, subject to some distribution constraints. This work proposes a novel approach based on OT of embeddings on the Poincar\'e model of hyperbolic spaces. Our method relies on the gyrobarycenter mapping on M\"obius gyrovector spaces. As a result of this formalism, we derive extensions to some existing Euclidean methods of OT-based domain adaptation to their hyperbolic counterparts. Empirically, we show that both Euclidean and hyperbolic methods have similar performances in the context of retrieval.
Nesting negations in FO2 over infinite words

We consider two-variable first-order logic FO2 over infinite words. Restricting the number of nested negations defines an infinite hierarchy; its levels are often called the half-levels of the FO2 quantifier alternation hierarchy. For every level of this hierarchy, we give an effective characterization. For the lower levels, this characterization is a combination of an algebraic and a topological property. For the higher levels, algebraic properties turn out to be sufficient. Within two-variable first-order logic, each algebraic property is a single ordered identity of omega-terms. The topological properties are the same as for the lower half-levels of the quantifier alternation hierarchy without the two-variable restriction (i.e., the Cantor topology and the alphabetic topology). Our result generalizes the corresponding result for finite words. The proof uses novel techniques and is based on a refinement of Mal'cev products for ordered monoids.
Ultrasound Diagnosis of COVID-19: Robustness and Explainability

Diagnosis of COVID-19 at point of care is vital to the containment of the global pandemic. Point of care ultrasound (POCUS) provides rapid imagery of lungs to detect COVID-19 in patients in a repeatable and cost effective way. Previous work has used public datasets of POCUS videos to train an AI model for diagnosis that obtains high sensitivity. Due to the high stakes application we propose the use of robust and explainable techniques. We demonstrate experimentally that robust models have more stable predictions and offer improved interpretability. A framework of contrastive explanations based on adversarial perturbations is used to explain model predictions that aligns with human visual perception.
Phosphorus solubility and bioavailability from poultry litter supplemented with gypsum and lime

Irshad, M. , Saleem, A. , Faridullah, Hassan, A. , Pervez, A. and Egrinya Eneji, A. 2012. Phosphorus solubility and bioavailability from poultry litter supplemented with gypsum and lime. Can. J. Soil Sci. 92: 893-900. Gypsum and lime are often used as amendments for the regulation of soil reaction (pH), which has an immense influence on soil nutrient availability. We determined the release and bioavailability of phosphorus (P) to spinach (Spinacia oleracea L.) from soils amended with gypsum and lime supplemented with poultry litter (PL). Four types of poultry litter (i.e., broiler litter, breeder litter, layer litter and duck litter) were mixed separately with lime and gypsum at the rates of 0, 2.5 and 5% and incubated at room temperature for 8 wk. The incubated materials were sampled at 4 and 8 wk and analyzed for water-extractable P. Prior to incubation, non-supplemented litter samples were also analyzed for total element contents (P, Ca, Mg, K, Mn, Cu, Zn and Fe). Total P varied in the order duck >broiler >layer >breeder. Water soluble P concentration was highly dependent on the type of PL as well as chemical amendment used. With the application of both gypsum and lime, P concentration decreased with increasing rate of application. There was less water-soluble P with lime than gypsum supplementation for all PL. Time of incubation slightly reduced water-soluble P concentration. Non-supplemented PL had greater P concentration after 8 wk, but soluble P was in the order duck >broiler >breeder >layer. At the 8th week of incubation with 5% gypsum, the soluble P decreased by 28% in duck, 22% in broiler, 17% in breeder and 16% in layer. The 5% lime treatment reduced P solubility by 26% in duck, 23% in broiler, 16% in breeder and 20% in layer. The addition of the supplemented PL to soil significantly reduced water-soluble P, especially with the lime treatment. Application of non-supplemented PL produced greater spinach biomass yield than PL supplemented with gypsum or lime, possibly on account of higher soluble P and micro elements (Zn, Cu, Mn and Fe). Although chemical amendments such as gypsum and lime lowered P concentration and even spinach yield, it may help reduce eutrophication of surface waters in areas where large amounts of PL are applied regularly.
Flux ropes in SANE disks

Three-dimensional numerical simulations of a hot accretion flow around a supermassive black hole are performed using the general relativity magneto-hydrodynamic (GRMHD) code Athena++. We focus on the case of SANE, with the initial magnetic field consisting of multiple loops with oppositely directed poloidal magnetic field in the torus. Using the simulation data, we investigate the formation of flux ropes, follow the forming of flux ropes atop the disk, and their release into corona.
Sequence Generation using Deep Recurrent Networks and Embeddings: A study case in music

Automatic generation of sequences has been a highly explored field in the last years. In particular, natural language processing and automatic music composition have gained importance due to the recent advances in machine learning and Neural Networks with intrinsic memory mechanisms such as Recurrent Neural Networks. This paper evaluates different types of memory mechanisms (memory cells) and analyses their performance in the field of music composition. The proposed approach considers music theory concepts such as transposition, and uses data transformations (embeddings) to introduce semantic meaning and improve the quality of the generated melodies. A set of quantitative metrics is presented to evaluate the performance of the proposed architecture automatically, measuring the tonality of the musical compositions.
Random walk patterns to identify weighted motifs

Over the last two decades, network theory has shown to be a fruitful paradigm in understanding the organization and functioning of real complex systems. Particularly relevant in this sense appears the identification of significant subgraphs that can shed light onto the underlying evolutionary processes. Such patterns, called motifs, have received much attention in binary networks, but a similar deep investigation for weighted networks is still lagging behind. Here, we proposed a novel methodology based on a random walker and a fixed maximum number of steps to study weighted motifs of limited size. The novelty is represented by the introduction of a sink node to balance the network and allow the detection of configurations within an a priori fixed number of steps for the random walker. We applied this approach to different real networks and selected a specific benchmark model based on maximum-entropy to test the significance of weighted motifs occurrence. We found that identified similarities enable the classifications of systems according to functioning mechanisms associated with specific configurations.
Open-Ended Multi-Modal Relational Reason for Video Question Answering

People with visual impairments urgently need helps, not only on the basic tasks such as guiding and retrieving objects , but on the advanced tasks like picturing the new environments. More than a guiding dog, they might want some devices which are able to provide linguistic interaction. Building on various research literature, we aim to conduct a research on the interaction between the robot agent and visual impaired people. The robot agent, applied VQA techniques, is able to analyze the environment, process and understand the pronouncing questions, and provide feedback to the human user. In this paper, we are going to discuss the related questions about this kind of interaction, the techniques we used in this work, and how we conduct our research.
Modeling and Simulation of Dissolved Oxygen Concentrations in a Full-Scale Textile Dyeing Wastewater Treatment Plant

A Modified Activated Sludge Model No.1 (M-ASM1), including six COD components (SI, SS, XI, XS, XH and SO) and three biochemical processes (aerobic growth of heterotrophs, aerobic decay of heterotrophs and hydrolysis of entrapped organics) is used to simulate the anaerobic hydrolysis-aeration- sedimentation treatment series in a full-scale textile dyeing wastewater treatment plant with an influent flowrate of 200,000 m3/d. M-ASM1 has the following characteristics: (1) the effect of temperature variation on biochemical reactions is included; (2) adding a parameter: ? Using this parameter, the anaerobic hydrolysis tank and the aerobic initial compartments in which the dissolved oxygen concentrations are equal to "0 mg/L" can be differentiated; (3) the oxygen transfer coefficient KLa is excluded. Namely, as long as inputting the aeration flowrate, the pressure in the aeration pipe and other running data on air supply into this model, M-ASM1 can simulate the oxygen concentrations of each compartment in a plug-flow aeration tank.
Multiepitope Subunit Vaccine Design against COVID-19 Based on the Spike Protein of SARS-CoV-2: An In Silico Analysis

The global health crisis caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causal agent of COVID-19, has resulted in a negative impact on human health and on social and economic activities worldwide. Researchers around the globe need to design and develop successful therapeutics as well as vaccines against the novel COVID-19 disease. In the present study, we conducted comprehensive computer-assisted analysis on the spike glycoprotein of SARS-CoV-2 in order to design a safe and potent multiepitope vaccine. In silico epitope prioritization shortlisted six HLA I epitopes and six B-cell-derived HLA II epitopes. These high-ranked epitopes were all connected to each other via flexible GPGPG linkers, and at the N-terminus side, the sequence of Cholera Toxin β subunit was attached via an EAAAK linker. Structural modeling of the vaccine was performed, and molecular docking analysis strongly suggested a positive association of a multiepitope vaccine with Toll-like Receptor 3. The structural investigations of the vaccine-TLR3 complex revealed the formation of fifteen interchain hydrogen bonds, thus validating its integrity and stability. Moreover, it was found that this interaction was thermodynamically feasible. In conclusion, our data supports the proposition that a multiepitope vaccine will provide protective immunity against COVID-19. However, further in vivo and in vitro experiments are needed to validate the immunogenicity and safety of the candidate vaccine.