Miracle-dz/UniFAR-FTUs huggingface dataset:
The Evolution of Topic Modeling

Topic models have been applied to everything from books to newspapers to social media posts in an effort to identify the most prevalent themes of a text corpus. We provide an in-depth analysis of unsupervised topic models from their inception to today. We trace the origins of different types of contemporary topic models, beginning in the 1990s, and we compare their proposed algorithms, as well as their different evaluation approaches. Throughout, we also describe settings in which topic models have worked well and areas where new research is needed, setting the stage for the next generation of topic models.
Fair termination of binary sessions

A binary session is a private communication channel that connects two processes, each adhering to a protocol description called session type. In this work, we study the first type system that ensures the fair termination of binary sessions. A session fairly terminates if all of the infinite executions admitted by its protocol are deemed unrealistic because they violate certain fairness assumptions. Fair termination entails the eventual completion of all pending input/output actions, including those that depend on the completion of an unbounded number of other actions in possibly different sessions. This form of lock freedom allows us to address a large family of natural communication patterns that fall outside the scope of existing type systems. Our type system is also the first to adopt fair subtyping, a liveness-preserving refinement of the standard subtyping relation for session types that so far has only been studied theoretically. Fair subtyping is surprisingly subtle not only to characterize concisely but also to use appropriately, to the point that the type system must carefully account for all usages of fair subtyping to avoid compromising its liveness-preserving properties.
Smart Cities: Practitioners’ Understanding and Expectations: Results from a Qualitative Study in the City of Koblenz

Over the last decade, Smart City has become a growing trend in urban development. Cities around the world have developed a Smart City concept and initiated a longer-term transformation in different areas, covered by the Smart City concept. Cities are expecting, among others, that the many potential benefits and improvements through Smart City implementations can reduce urban challenges. Despite this great interest on the part of practitioners and some standardization efforts, different definitions of the term exist in the literature and in practice. Depending on the context, different aspects are focused on, which shape an idiosyncratic understanding of dimensions of Smart City. Beyond that, the understanding of the term in theory and in practice may vary substantially. As a result, there is an inevitable high degree of complexity in the understanding of the term and, consequently, uncertainty among the practitioners. Thus, this study explores practitioners' understanding of Smart City through expert interviews in the city of Koblenz, Germany. The paper provides a first insight into the heterogeneity of the understanding of the term in the context of application and compares it to the basic definitions and conceptual approaches associated with Smart City developments in the academic literature.
A relational theory of effects and coeffects

Graded modal types systems and coeffects are becoming a standard formalism to deal with context-dependent, usage-sensitive computations, especially when combined with computational effects. From a semantic perspective, effectful and coeffectful languages have been studied mostly by means of denotational semantics and almost nothing has been done from the point of view of relational reasoning. This gap in the literature is quite surprising, since many cornerstone results — such as non-interference, metric preservation, and proof irrelevance — on concrete coeffects are inherently relational. In this paper, we fill this gap by developing a general theory and calculus of program relations for higher-order languages with combined effects and coeffects. The relational calculus builds upon the novel notion of a corelator (or comonadic lax extension) to handle coeffects relationally. Inside such a calculus, we define three notions of effectful and coeffectful program refinements: contextual approximation, logical preorder, and applicative similarity. These are the first operationally-based notions of program refinement (and, consequently, equivalence) for languages with combined effects and coeffects appearing in the literature. We show that the axiomatics of a corelator (together with the one of a relator) is precisely what is needed to prove all the aforementioned program refinements to be precongruences, this way obtaining compositional relational techniques for reasoning about combined effects and coeffects.
A Case of the Governance of Digital Technology in Tax Administration

Despite the growing importance of digital technology in recent years, a holistic view of its governance in tax administration is yet to be clearly articulated. This shortcoming is attributed to the lack of empirical research in this sensitive, yet important, public administration context. Using IT Governance conceptual framework, this case study research examines executive leadership perspectives (Information & Technology, Operations, and Customer Service) on the governance of digital technologies in a European tax administration. Specifically, the study explores the governance capabilities associated with a digital transformation initiative implemented in the tax administration. Findings revealed specific Structural, Decision-making, and Relational capabilities considered important from three disparate, yet complementary leadership views of digital technology governance in tax administration.
Distributed Adaptive Controller for Output-Synchronization of Networked Strict Feedback Systems with Dead-Zone Nonlinearity with Application in the Voltage Equalization Control of Ultra-Capacitor Type Power Source

In this paper, distributed robust adaptive backstepping control algorithm is developed for the output synchronization of multiple strict feedback systems with dead-zone nonlinearity. No knowledge could be assumed on the dead-zone for the design and implementation of controller and also the unknown system parameters. By combining graph theory with backstepping method, a distributed controller is designed for each agent recursively in order to make its output synchronized with the output of its neighbors, meanwhile, the overall system maintains bounded. It is proven that the output of all agents in the network could be synchronized when we connects the undirected graph, i.e., cooperative output synchronization of the network can be realized. The voltage equalization control of ultra-capacitor is presented as a numerical example to prove the effectiveness of the proposed controllers.
Dual-Stream Guided-Learning via a Priori Optimization for Person Re-identification

The task of person re-identification (re-ID) is to find the same pedestrian across non-overlapping camera views. Generally, the performance of person re-ID can be affected by background clutter. However, existing segmentation algorithms cannot obtain perfect foreground masks to cover the background information clearly. In addition, if the background is completely removed, some discriminative ID-related cues (i.e., backpack or companion) may be lost. In this article, we design a dual-stream network consisting of a Provider Stream (P-Stream) and a Receiver Stream (R-Stream). The R-Stream performs an a priori optimization operation on foreground information. The P-Stream acts as a pusher to guide the R-Stream to concentrate on foreground information and some useful ID-related cues in the background. The proposed dual-stream network can make full use of the a priori optimization and guided-learning strategy to learn encouraging foreground information and some useful ID-related information in the background. Our method achieves Rank-1 accuracy of 95.4% on Market-1501, 89.0% on DukeMTMC-reID, 78.9% on CUHK03 (labeled), and 75.4% on CUHK03 (detected), outperforming state-of-the-art methods.
Return of CFA: call-site sensitivity can be superior to object sensitivity even for object-oriented programs

In this paper, we challenge the commonly-accepted wisdom in static analysis that object sensitivity is superior to call-site sensitivity for object-oriented programs. In static analysis of object-oriented programs, object sensitivity has been established as the dominant flavor of context sensitivity thanks to its outstanding precision. On the other hand, call-site sensitivity has been regarded as unsuitable and its use in practice has been constantly discouraged for object-oriented programs. In this paper, however, we claim that call-site sensitivity is generally a superior context abstraction because it is practically possible to transform object sensitivity into more precise call-site sensitivity. Our key insight is that the previously known superiority of object sensitivity holds only in the traditional k-limited setting, where the analysis is enforced to keep the most recent k context elements. However, it no longer holds in a recently-proposed, more general setting with context tunneling. With context tunneling, where the analysis is free to choose an arbitrary k-length subsequence of context strings, we show that call-site sensitivity can simulate object sensitivity almost completely, but not vice versa. To support the claim, we present a technique, called Obj2CFA, for transforming arbitrary context-tunneled object sensitivity into more precise, context-tunneled call-site-sensitivity. We implemented Obj2CFA in Doop and used it to derive a new call-site-sensitive analysis from a state-of-the-art object-sensitive pointer analysis. Experimental results confirm that the resulting call-site sensitivity outperforms object sensitivity in precision and scalability for real-world Java programs. Remarkably, our results show that even 1-call-site sensitivity can be more precise than the conventional 3-object-sensitive analysis.
On type-cases, union elimination, and occurrence typing

We extend classic union and intersection type systems with a type-case construction and show that the combination of the union elimination rule of the former and the typing rules for type-cases of our extension encompasses occurrence typing. To apply this system in practice, we define a canonical form for the expressions of our extension, called MSC-form. We show that an expression of the extension is typable if and only if its MSC-form is, and reduce the problem of typing the latter to the one of reconstructing annotations for that term. We provide a sound algorithm that performs this reconstruction and a proof-of-concept implementation.
Hand Gesture Recognition in Range-Doppler Images Using Binary Activated Spiking Neural Networks

Many hand gesture recognition systems use radar to sense the motion of the hand due to its independence of lighting and its inherent privacy. As in the case of cameras, complex signal processing chains consisting of classical algorithms and neural network-base approaches are necessary to evaluate the incoming data stream. Especially on mobile devices, the reduction of the total energy consumption of the recognition system is crucial as it would lead to an increased battery life. Spiking neural networks have been shown to consume much less energy than current networks by operating event-driven and using time as the main information carrier. However, practical applications in which they are on par with classical approaches are rare. In this paper we utilize spiking neural networks to perform hand gesture recognition in radar data. We show that the temporal affinity of spiking networks and the possibility to binarize the radar-generated range-Doppler images without large loss of information introduces a promising synergy. Using simple networks consisting of 75 recurrently connected spiking neurons, we are able to reach current state-of-the-art performance on two public datasets. With this approach, gesture recognition systems can operate much more energy-efficient, making spiking neural networks viable alternatives to current solutions.
Self-Supervised Patch Localization for Cross-Domain Facial Action Unit Detection

Automatic detection of Facial Action Units (AUs) is a fundamental block for objective facial expression analysis. Computer vision-based detection of facial action units is susceptible to variations across corpora. To address this problem, we propose a novel architecture that can be jointly trained for self-supervised optical flow estimation, patch localization, supervised action unit detection, and adversarial domain adaptation. Patch localization allows the encoder to learn local features that are critical to detecting subtle changes caused by the presence of AUs in face. Specifically, an encoder-decoder architecture is used to estimate optical flow from every image. The optical flow is simultaneously used for AU detection, patch localization and adversarial domain adaptation. Majority of the existing work on facial expression analysis is evaluated within corpora. In this paper, we develop and evaluate this novel architecture for facial action unit detection across corpora. The experimental results indicate that our framework improves cross-domain performance (5.5 % F1-score on average), suggesting that the proposed patch localization guides the network to learn a more generalizable representation.
Three ResNet Deep Learning Architectures Applied in Pulmonary Pathologies Classification

During the diagnosis of pulmonary diseases, the doctor listens to the lung sounds on the patient’s chest using a traditional stethoscope for the earlier detection of abnormal respiratory sounds. Currently, due to developments in the digital technology domain, an electronic stethoscope can instead be used to acquire respiratory sounds and save these as data for further processing and analysing. Many algorithms for automatic classification have been implemented to distinguish between several lung diseases. In this study, we implemented a deep residual network (ResNet) model with three different architectures based on different numbers of layers (ResNet-50/101/152) for the classification of pulmonary pathologies. To evaluate this method, we used it for the analysis of gammatonegrams, which transform lung sounds from onedimensional to two-dimensional representations. The image outputs obtained with the gammatonegram are fed as inputs to the Three-ResNet architecture. The ICBHI database was used to classify three types of pulmonary conditions, namely, healthy, chronic obstructive pulmonary disease (COPD) and pneumonia conditions. The results showed that, ResNet-50, ResNet-101 and ResNet-152 presented accuracies of 90.37%, 89.79% and 67.57%, respectively. Therefore, our results demonstrate that the residual networks can achieve significant accuracy for the classification of these three types of pulmonary conditions.
Multi-Modal Learning for AU Detection Based on Multi-Head Fused Transformers

Multi-modal learning has been intensified in recent years, especially for applications in facial analysis and action unit detection whilst there still exist two main challenges in terms of 1) relevant feature learning for representation and 2) efficient fusion for multi-modalities. Recently, there are a number of works have shown the effectiveness in utilizing the attention mechanism for AU detection, however, most of them are binding the region of interest (ROI) with features but rarely apply attention between features of each AU. On the other hand, the transformer, which utilizes a more efficient self-attention mechanism, has been widely used in natural language processing and computer vision tasks but is not fully explored in AU detection tasks. In this paper, we propose a novel end-to-end Multi-Head Fused Transformer (MFT) method for AU detection, which learns AU encoding features representation from different modalities by transformer encoder and fuses modalities by another fusion transformer module. Multi-head fusion attention is designed in the fusion transformer module for the effective fusion of multiple modalities. Our approach is evaluated on two public multi-modal AU databases, BP4D, and BP4D+, and the results are superior to the state-of-the-art algorithms and baseline models. We further analyze the performance of AU detection from different modalities.
A Preclinical Rat Model of Heart Failure With Preserved Ejection Fraction With Multiple Comorbidities

Heart failure with preserved ejection fraction (HFpEF) is a common complex clinical syndrome for which there are currently few evidence-based therapies. As patients with HFpEF very often present with comorbidities comprising the metabolic syndrome, we hypothesized, that metabolic syndrome could lead over time to the development of diastolic dysfunction and HFpEF. Obesity-prone rats were exposed to high-fat diet and compared to obesity-resistant rats fed with standard chow. Phenotyping of metabolic syndrome, associated with echocardiographic and cardiac hemodynamic measurements, was performed after 4 and 12 months. Blood and myocardial tissue sampling were performed for pathobiological evaluation. High-fat diet in obesity-prone rats elicited metabolic syndrome, characterized by increased body and abdominal fat weights, glucose intolerance and hyperlipidemia, as well as increased left ventricular (LV) systolic pressure (after 12 months). This was associated with LV diastolic dysfunction (assessed by increased LV end-diastolic pressure) and pulmonary hypertension (assessed by increased right ventricular systolic pressure). Echocardiography revealed significant concentric LV hypertrophy, while LV ejection fraction was preserved. LV remodeling was associated with cardiomyocyte hypertrophy, as well as myocardial and perivascular fibrosis. Circulating levels of soluble ST2 (the interleukin-1 receptor-like) markedly increased in rats with HFpEF, while plasma NT-proBNP levels decreased. RNA-sequencing analysis identified clusters of genes implicated in fatty acid metabolism and calcium-dependent contraction as upregulated pathways in the myocardium of rats with HFpEF. High-fat diet during 12 months in obesity-prone rats led to the development of a relevant preclinical model of HFpEF with multiple comorbidities, suitable for investigating novel therapeutic interventions.
Frequency-Selective Rasorbers: A View of Frequency-Selective Rasorbers and Their Application in Reducing the Radar Cross Sections of Antennas

A frequency-selective surface (FSS) can be employed as a spatial filter for incident electromagnetic (EM) waves, which are most often used in radome design to protect the antennas and to reduce the radar cross section (RCS) of the antenna system <xref ref-type="bibr" rid="ref1">[1]</xref>–<xref ref-type="bibr" rid="ref2"/><xref ref-type="bibr" rid="ref3">[3]</xref>. In-band signals transmit through the FSS with little power loss, while out-of-band incoming waves are reflected strongly, making them detectable by radar. Thus, to further reduce the biostatic/multistate RCS of the radome, circuit analog absorbers or absorbing materials are introduced into the radome design. As a result, the out-of-band signals are absorbed and reflection is reduced.
Status and Extension of the Geant4-DNA Dielectric Models for Application to Electron Transport

The development of accurate physics models that enable track structure simulations of electrons in liquid water medium over a wide energy range, from the eV to the MeV scale, is a subject of continuous efforts due to its importance (among other things) in theoretical studies of radiation quality for application in radiotherapy and radiation protection. A few years ago, the Geant4-DNA very low-energy extension of the Geant4 Monte Carlo code had offered to users an improved set of physics models for discrete electron transport below 10 keV. In this work we present refinements to this model set and its extension to energies up to 1 MeV. Preliminary comparisons against the existing Geant4-DNA physics models with respect to total and differential ionization cross sections of electrons in liquid water are reported and discussed.
Gap-Filling Eddy Covariance Latent Heat Flux: Inter-Comparison of Four Machine Learning Model Predictions and Uncertainties in Forest Ecosystem

Environmental monitoring using satellite remote sensing is challenging because of data gaps in eddy-covariance (EC)-based in situ flux tower observations. In this study, we obtain the latent heat flux (LE) from an EC station and perform gap filling using two deep learning methods (two-dimensional convolutional neural network (CNN) and long short-term memory (LSTM) neural networks) and two machine learning (ML) models (support vector machine (SVM), and random forest (RF)), and we investigate their accuracies and uncertainties. The average model performance based on ~25 input and hysteresis combinations show that the mean absolute error is in an acceptable range (34.9 to 38.5 Wm−2), which indicates a marginal difference among the performances of the four models. In fact, the model performance is ranked in the following order: SVM > CNN > RF > LSTM. We conduct a robust analysis of variance and post-hoc tests, which yielded statistically insignificant results (p-value ranging from 0.28 to 0.76). This indicates that the distribution of means is equal within groups and among pairs, thereby implying similar performances among the four models. The time-series analysis and Taylor diagram indicate that the improved two-dimensional CNN captures the temporal trend of LE the best, i.e., with a Pearson’s correlation of >0.87 and a normalized standard deviation of ~0.86, which are similar to those of in situ datasets, thereby demonstrating its superiority over other models. The factor elimination analysis reveals that the CNN performs better when specific meteorological factors are removed from the training stage. Additionally, a strong coupling between the hysteresis time factor and the accuracy of the ML models is observed.