Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Visualisation of Argumentation Graphs to Support Text Interpretation

The recent evolution in Natural Language Processing (NLP) methods, in particular in the field of argumentation mining, has the potential to transform the way we interact with text, supporting the interpretation and analysis of complex discourse and debates. Can a graphic visualisation of complex argumentation enable a more critical interpretation of the arguments? This study focuses on analysing the impact of argumentation graphs (AGs) compared with regular texts for supporting argument interpretation. We found that AGs outperformed the extrinsic metrics throughout most UEQ scales as well as the NASA-TLX workload in all the terms but not in temporal or physical demand. The AG model was liked by a more significant number of participants, despite the fact that both the text-based and AG models yielded comparable outcomes in the critical interpretation in terms of working memory and altering participants decisions. The interpretation process involves reference to argumentation schemes (linked to critical questions (CQs)) in AGs. Interestingly, we found that the participants chose more CQs (using argument schemes in AGs) when they were less familiar with the argument topics, making AG schemes on some scales (relatively) supportive of the interpretation process. Therefore, AGs were considered to deliver a more critical approach to argument interpretation, especially with unfamiliar topics. Based on the 25 participants conducted in this study, it appears that AG has demonstrated an overall positive effect on the argument interpretation process.
A combinatorial proof for the secretary problem with multiple choices

The Secretary problem is a classical sequential decision-making question that can be succinctly described as follows: a set of rank-ordered applicants are interviewed sequentially for a single position. Once an applicant is interviewed, an immediate and irrevocable decision is made if the person is to be offered the job or not and only applicants observed so far can be used in the decision process. The problem of interest is to identify the stopping rule that maximizes the probability of hiring the highest-ranked applicant. A multiple-choice version of the Secretary problem, known as the Dowry problem, assumes that one is given a fixed integer budget for the total number of selections allowed to choose the best applicant. It has been solved using tools from dynamic programming and optimal stopping theory. We provide the first combinatorial proof for a related new \emph{query-based model} for which we are allowed to solicit the response of an expert to determine if an applicant is optimal. Since the selection criteria differ from those of the Dowry problem we obtain nonidentical expected stopping times. Our result indicates that an optimal strategy is the $(a_s, a_{s-1}, \ldots, a_1)$-strategy, i.e., for the $i^{th}$ selection, where $1 \le i \le s$ and $1 \le j = s+1-i \le s$, we reject the first $a_j$ applicants, wait until the decision of the $(i-1)^{th}$ selection (if $i \ge 2$), and then accept the next applicant whose qualification is better than all previously appeared applicants. Furthermore, our optimal strategy is right-hand based, i.e., the optimal strategies for two models with $s_1$ and $s_2$ selections in total ($s_1<s_2$) share the same sequence $a_1, a_2, \ldots, a_{s_1}$ when it is viewed from the right. When the total number of applicants tends to infinity, our result agrees with the thresholds obtained by Gilbert and Mosteller.
Semantic-guided Disentangled Representation for Unsupervised Cross-modality Medical Image Segmentation

Disentangled representation is a powerful technique to tackle domain shift problem in medical image analysis in unsupervised domain adaptation setting.However, previous methods only focus on exacting domain-invariant feature and ignore whether exacted feature is meaningful for downstream tasks.We propose a novel framework, called semantic-guided disentangled representation (SGDR), an effective method to exact semantically meaningful feature for segmentation task to improve performance of cross modality medical image segmentation in unsupervised domain adaptation setting. To exact the meaningful domain-invariant features of different modality, we introduce a content discriminator to force the content representation to be embedded to the same space and a feature discriminator to exact the meaningful representation.We also use pixel-level annotations to guide the encoder to learn features that are meaningful for segmentation task.We validated our method on two public datasets and experiment results show that our approach outperforms the state of the art methods on two evaluation metrics by a significant margin.
R EVISITING THE M OMENT A CCOUNTANT M ETHOD FOR DP-SGD

In order to provide differential privacy, Gaussian noise with standard deviation σ is added to local SGD updates after performing a clipping operation in Differential Private SGD (DP-SGD). By nontrivially improving the account method we prove a simple and easy to evaluate closed form ( , δ)-DP guarantee: DP-SGD is ( , δ)-DP if σ = √ 2( + ln(1/δ))/ and T is at least ≈ 2k/ , where T is the total number of rounds, and K = kN is the total number of gradient computations where k measures K in number of epochs of size N of the local data set. We prove that our expression is close to tight in that if T is more than a constant factor ≈ 8 smaller than the lower bound ≈ 2k/ , then the ( , δ)-DP guarantee is violated. Choosing the smallest possible value T ≈ 2k/ not only leads to a close to tight DP guarantee, but also minimizes the total number of communicated updates and this means that the least amount of noise is aggregated into the global model and accuracy is optimized as confirmed by simulations. In addition this minimizes round communication.
Cyber Vaccine for Deepfake Immunity

Deepfakes pose an evolving cybersecurity threat that calls for the development of automated countermeasures. While considerable forensic research has been devoted to the detection and localisation of deepfakes, solutions for ‘fake-to-real’ reversal are yet to be developed. In this study, we introduce the concept of cyber vaccination for conferring immunity to deepfakes. In other words, we aim to impart a self-healing ability to the face-containing media so that the original content can be recovered after manipulation by AI-based deepfake technology. Analogous to biological vaccination which uses injected antigens to induce immunity prior to infection by an actual pathogen, cyber vaccination simulates deepfakes and performs adversarial training to build a defensive immune system. Aiming to build up attack-agnostic immunity with limited computational resources, we propose simulating various deepfakes with one single overpowering attack: face masking. The proposed immune system consists of a vaccinator for inducing immunity and a neutraliser for recovering facial content. Experimental evaluations demonstrate effective immunity to face replacement and various types of corruption.
Social Cue Detection and Analysis Using Transfer Entropy

Robots that work close to humans need to understand and use social cues to act in a socially acceptable manner. Social cues are a form of communication (i.e., information flow) between people. In this paper, a framework is introduced to detect and analyse a class of perceptible social cues that are nonverbal and episodic, and the related information transfer using an information-theoretic measure, namely, transfer entropy. We use a group-joining setting to demonstrate the practicality of transfer entropy for analysing communications between humans. Then we demonstrate the framework in two settings involving social interactions between humans: object-handover and person-following. Our results show that transfer entropy can identify information flows between agents and when and where they occur. Potential applications of the framework include information flow or social cue analysis for interactive robot design and socially-aware robot planning.
Modified Green-Hyperbolic Operators

Green-hyperbolic operators - partial differential operators on globally hyperbolic spacetimes that (together with their formal duals) possess advanced and retarded Green operators - play an important role in many areas of mathematical physics. Here, we study modifications of Green-hyperbolic operators by the addition of a possibly nonlocal operator acting within a compact subset $K$ of spacetime, and seek corresponding '$K$-nonlocal' generalised Green operators. Assuming the modification depends holomorphically on a parameter, conditions are given under which $K$-nonlocal Green operators exist for all parameter values, with the possible exception of a discrete set. The exceptional points occur precisely where the modified operator admits nontrivial smooth homogeneous solutions that have past- or future-compact support. Fredholm theory is used to relate the dimensions of these spaces to those corresponding to the formal dual operator, switching the roles of future and past. The $K$-nonlocal Green operators are shown to depend holomorphically on the parameter in the topology of bounded convergence on maps between suitable Sobolev spaces, or between suitable spaces of smooth functions. An application to the LU factorisation of systems of equations is described.
Requirements for upgrading trusted nodes to a repeater chain over 900 km of optical fiber

We perform a numerical study of the distribution of entanglement on a real-world fiber grid connecting the German cities of Bonn and Berlin. The connection is realized using a chain of processing-node quantum repeaters spanning roughly 900 kilometers. We investigate how minimal hardware requirements depend on the target application, as well as on the number of repeaters in the chain. We find that requirements for blind quantum computing are markedly different than those for quantum key distribution, with the required coherence time being around two and a half times larger for the former. Further, we observe a trade-off regarding how target secret-key rates are achieved when using different numbers of repeaters: comparatively low-quality entangled states generated at a high rate are preferred for higher numbers of repeaters, whereas comparatively high-quality states generated at a lower rate are favored for lower numbers of repeaters. To obtain our results we employ an extensive simulation framework implemented using NetSquid, a discrete-event simulator for quantum networks. These are combined with an optimization methodology based on genetic algorithms to determine minimal hardware requirements.
Real-space pseudopotential method for first principles calculations of general periodic and partially periodic systems

We present a real-space method for electronic-structure calculations of systems with general full or partial periodicity. The method is based on the self-consistent solution of the Kohn-Sham equations, using first principles pseudopotentials, on a uniform three-dimensional non-Cartesian grid. Its efficacy derives from the introduction of a new generalized high-order finite-difference method that avoids the numerical evaluation of mixed derivative terms and results in a simple yet accurate finite difference operator. Our method is further extended to systems where periodicity is enforced only along some directions (e.g., surfaces), by setting up the correct electrostatic boundary conditions and by properly accounting for the ion-electron and ion-ion interactions. Our method enjoys the main advantages of real-space grid techniques over traditional plane-wave representations for density functional calculations, namely, improved scaling and easier implementation on parallel computers, as well as inherent immunity to spurious interactions brought about by artificial periodicity. We demonstrate its capabilities on bulk GaAs and Na for the fully periodic case and on a monolayer of Si-adsorbed polar nitrobenzene molecules for the partially periodic case.
On the Use of Safety Critical Control for Cyber-Physical Security in the Smart Grid

The tight coupling between communication and control in cyber-physical systems is necessary to enable the complex regulation required to operate these systems. Unfortunately, cyberattackers can exploit network vulnerabilities to compromise communication and force unsafe decision-making and dynamics. If a cyberattack is not detected and isolated in a timely manner, the control process must balance adhering to the received measurement signals to maintain system operation and ensuring that temporary compromise of the signals does not force unsafe dynamics. For this purpose, we present and employ a safety critical controller based on control barrier functions to mitigate attacks against load frequency control in smart power grids. We validate the paper’s findings using simulation on a high-fidelity testbed.
HyperPose: Camera Pose Localization using Attention Hypernetworks

In this study, we propose the use of attention hypernetworks in camera pose localization. The dynamic nature of natural scenes, including changes in environment, perspective, and lighting, creates an inherent domain gap between the training and test sets that limits the accuracy of contemporary localization networks. To overcome this issue, we suggest a camera pose regressor that integrates a hypernetwork. During inference, the hypernetwork generates adaptive weights for the localization regression heads based on the input image, effectively reducing the domain gap. We also suggest the use of a Transformer-Encoder as the hypernetwork, instead of the common multilayer perceptron, to derive an attention hypernetwork. The proposed approach achieves superior results compared to state-of-the-art methods on contemporary datasets. To the best of our knowledge, this is the first instance of using hypernetworks in camera pose regression, as well as using Transformer-Encoders as hypernetworks. We make our code publicly available.
Visual Place Recognition: A Tutorial

Localization is an essential capability for mobile robots. A rapidly growing field of research in this area is Visual Place Recognition (VPR), which is the ability to recognize previously seen places in the world based solely on images. This present work is the first tutorial paper on visual place recognition. It unifies the terminology of VPR and complements prior research in two important directions: 1) It provides a systematic introduction for newcomers to the field, covering topics such as the formulation of the VPR problem, a general-purpose algorithmic pipeline, an evaluation methodology for VPR approaches, and the major challenges for VPR and how they may be addressed. 2) As a contribution for researchers acquainted with the VPR problem, it examines the intricacies of different VPR problem types regarding input, data processing, and output. The tutorial also discusses the subtleties behind the evaluation of VPR algorithms, e.g., the evaluation of a VPR system that has to find all matching database images per query, as opposed to just a single match. Practical code examples in Python illustrate to prospective practitioners and researchers how VPR is implemented and evaluated.
Reverse Engineering Word-Level Models from Look-Up Table Netlists

Reverse engineering of FPGA designs from bitstreams to RTL models aids in understanding the high level functionality of the design and for validating and reconstructing legacy designs. Fast carry-chains are commonly used in synthesis of operators in FPGA designs. We propose a method to detect word-level structures by analyzing these carry-chains in LUT (Look-Up Table) level netlists. We also present methods to adapt existing techniques to identify combinational operations and sequential modules in ASIC netlists to LUT netlists. All developed and adapted techniques are consolidated into an integrated tool-chain to aid in reverse engineering of word-level designs from LUT-level netlists. When evaluated on a set of real-world designs, the tool-chain infers 34% to 100% of the elements in the netlist to be part of a known word-level operation or a known sequential module.
Mortality Rates of US Counties: Are they Reliable and Predictable?

We examine US County-level observational data on Lung Cancer mortality rates in 2012 and overall Circulatory Respiratory mortality rates in 2016 as well as their"Top Ten"potential causes from Federal or State sources. We find that these two mortality rates for 2,812 US Counties have remarkably little in common. Thus, for predictive modeling, we use a single"compromise"measure of mortality that has several advantages. The vast majority of our new findings have simple implications that we illustrate graphically.
Well-Connected Communities in Real-World Networks

Community detection in real-world networks is typically addressed through the use of graph clustering methods that partition the nodes of a network into disjoint subsets. While the deﬁ-nition of community may vary, it is generally accepted that elements of a community should be “well-connected”. We evaluated clusters generated by the Leiden algorithm and the Iterative K-core (IKC) clustering algorithm for their susceptibility to become disconnected by the deletion of a small number of edges. A striking observation is that for Leiden clustering of real-world networks, except for cases with large resolution parameter values, the majority of clusters do not meet the relatively mild condition we enforce for well-connected clusters. We also constructed a modular pipeline to enable well-connected output clusters that allows a user-speciﬁed criterion for a valid community considering cluster size and minimum edge cut size and describe the use of this pipeline on real world and synthetic networks. An interesting trend we observed is that the ﬁnal clusterings on real-world networks had small node coverage, suggesting that not all nodes in a network belong in communities.
End-to-End Speech Recognition: A Survey

In the last decade of automatic speech recognition (ASR) research, the introduction of deep learning has brought considerable reductions in word error rate of more than 50% relative, compared to modeling without deep learning. In the wake of this transition, a number of all-neural ASR architectures have been introduced. These so-called end-to-end (E2E) models provide highly integrated, completely neural ASR models, which rely strongly on general machine learning knowledge, learn more consistently from data, with lower dependence on ASR domain-specific experience. The success and enthusiastic adoption of deep learning, accompanied by more generic model architectures has led to E2E models now becoming the prominent ASR approach. The goal of this survey is to provide a taxonomy of E2E ASR models and corresponding improvements, and to discuss their properties and their relationship to classical hidden Markov model (HMM) based ASR architectures. All relevant aspects of E2E ASR are covered in this work: modeling, training, decoding, and external language model integration, discussions of performance and deployment opportunities, as well as an outlook into potential future developments.
Non-parametric reconstruction of photon escape fraction from reionization

One of the most crucial yet poorly constrained parameters in modelling the ionizing emissivity is the escape fraction of photons from star-forming galaxies. Several theoretical and observational studies have been conducted over the past few years, but consensus regarding its redshift evolution has yet to be achieved. We present here the first non-parametric reconstruction of this parameter as a function of redshift from a data-driven reionization model using a Gaussian Process Regression method. Our finding suggests a mild redshift evolution of escape fraction with a mean value of $4~{{\%}},7~{{\%}},\sim 10~{{\%}}$ at z = 2, 6, 12. However, a constant escape fraction of $6-10~{{\%}}$ at z ≳ 6 is still allowed by current data and also matches other reionization-related observations. With the detection of fainter high redshift galaxies from upcoming observations of JWST, the approach presented here will be a robust tool to put the most stringent constraint on escape fraction as well as reionization histories.
Ensemble Reinforcement Learning: A Survey

Reinforcement Learning (RL) has emerged as a highly effective technique for addressing various scientific and applied problems. Despite its success, certain complex tasks remain challenging to be addressed solely with a single model and algorithm. In response, ensemble reinforcement learning (ERL), a promising approach that combines the benefits of both RL and ensemble learning (EL), has gained widespread popularity. ERL leverages multiple models or training algorithms to comprehensively explore the problem space and possesses strong generalization capabilities. In this study, we present a comprehensive survey on ERL to provide readers with an overview of recent advances and challenges in the field. Firstly, we provide an introduction to the background and motivation for ERL. Secondly, we conduct a detailed analysis of strategies such as model selection and combination that have been successfully implemented in ERL. Subsequently, we explore the application of ERL, summarize the datasets, and analyze the algorithms employed. Finally, we outline several open questions and discuss future research directions of ERL. By offering guidance for future scientific research and engineering applications, this survey significantly contributes to the advancement of ERL.
ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based Polishing

Zero-shot capability has been considered as a new revolution of deep learning, letting machines work on tasks without curated training data. As a good start and the only existing outcome of zero-shot image captioning (IC), ZeroCap abandons supervised training and sequentially searches every word in the caption using the knowledge of large-scale pre-trained models. Though effective, its autoregressive generation and gradient-directed searching mechanism limit the diversity of captions and inference speed, respectively. Moreover, ZeroCap does not consider the controllability issue of zero-shot IC. To move forward, we propose a framework for Controllable Zero-shot IC, named ConZIC. The core of ConZIC is a novel sampling-based non-autoregressive language model named Gibbs-BERT, which can generate and continuously polish every word. Extensive quantitative and qualitative results demonstrate the superior performance of our proposed ConZIC for both zero-shot IC and controllable zero-shot IC. Especially, ConZIC achieves about $5\times$ generation speed than ZeroCap, and about $1.5\times$ diversity scores, with accurate generation given different control signals. Our code is available at https://github.com/joeyz0z/ConZIC.