Miracle-dz/UniFAR-FTUs huggingface dataset:
Fairness-Aware Naive Bayes Classifier for Data with Multiple Sensitive Features

Fairness-aware machine learning seeks to maximise utility in generating predictions while avoiding unfair discrimination based on sensitive attributes such as race, sex, religion, etc. An important line of work in this field is enforcing fairness during the training step of a classifier. A simple yet effective binary classification algorithm that follows this strategy is two-naive-Bayes (2NB), which enforces statistical parity - requiring that the groups comprising the dataset receive positive labels with the same likelihood. In this paper, we generalise this algorithm into N-naive-Bayes (NNB) to eliminate the simplification of assuming only two sensitive groups in the data and instead apply it to an arbitrary number of groups. We propose an extension of the original algorithm's statistical parity constraint and the post-processing routine that enforces statistical independence of the label and the single sensitive attribute. Then, we investigate its application on data with multiple sensitive features and propose a new constraint and post-processing routine to enforce differential fairness, an extension of established group-fairness constraints focused on intersectionalities. We empirically demonstrate the effectiveness of the NNB algorithm on US Census datasets and compare its accuracy and debiasing performance, as measured by disparate impact and DF-$\epsilon$ score, with similar group-fairness algorithms. Finally, we lay out important considerations users should be aware of before incorporating this algorithm into their application, and direct them to further reading on the pros, cons, and ethical implications of using statistical parity as a fairness criterion.
Graph Partitioning in Connected Components with Minimum Size Constraints via Mixed Integer Programming

In this work, a graph partitioning problem in a fixed number of connected components is considered. Given an undirected graph with costs on the edges, the problem consists on partitioning the set of nodes into a fixed number of subsets with minimum size, where each subset induces a connected subgraph with minimal edge cost. Mixed Integer Programming formulations together with a variety of valid inequalities are demonstrated and implemented in a Branch&Cut framework. A column generation approach is also proposed for this problem with additional cuts. Finally, the methods are tested for several simulated instances and computational results are discussed.
Absorption-based Circumgalactic Medium Line Emission Estimates

Motivated by integral field units (IFUs) on large ground telescopes and proposals for ultraviolet-sensitive space telescopes to probe circumgalactic medium (CGM) emission, we survey the most promising emission lines and how such observations can inform our understanding of the CGM and its relation to galaxy formation. We tie our emission estimates to both HST/COS absorption measurements of ions around z ≈ 0.2 Milky Way mass halos and models for the density and temperature of gas. We also provide formulas that simplify extending our estimates to other samples and physical scenarios. We find that O iii 5007 Å and N ii 6583 Å, which at fixed ionic column density are primarily sensitive to the thermal pressure of the gas they inhabit, may be detectable with KCWI and especially IFUs on 30 m telescopes out to half a virial radius. O v 630 Å and O vi 1032,1038 Å are perhaps the most promising ultraviolet lines, with models predicting intensities >100 γ cm−2 s−1 sr−1 in the inner 100 kpc of Milky Way-like systems. A detection of O vi would confirm the collisionally ionized picture and constrain the density profile of the CGM. Other ultraviolet metal lines constrain the amount of gas that is actively cooling and mixing. We find that C iii 978 Å and C iv 1548 Å may be detectable if an appreciable fraction of the observed O vi column is associated with mixing or cooling gas. Hα emission within 100 kpc of Milky Way-like galaxies is within reach of current IFUs even for the minimum signal from ionizing background fluorescence, while Hydrogen n > 2 Lyman-series lines are too weak to be detectable.
Is the temporary migration regime in Asia future-ready?

ABSTRACT In pre-pandemic times, the labour migration regime in Asia based on principles of enforced transience is largely dependent on the ease and low cost of transnational mobility across national borders. Stalled mobility in COVID-19 times has both deepened the precarity that transient migrant workers face and also laid bare the unsustainability of the temporary migration for nation-states. At the same time, the pandemic presents an opportunity to reconfigure and move temporary migration toward a more sustainable and equitable basis. This could involve offering visas and contracts of longer duration and selective pathways towards residency status to reduce ‘churning’, incorporating migrant workers into national healthcare safety nets to improve migrant welfare and societal resilience, and careful recalibration of using automation and technological substitutes to replace migrant labour.
High-Throughput Screening of a Promoter Library Reveals New Persister Mechanisms in Escherichia Coli

Persistence is a nonheritable trait by which normal growing cells switch phenotypically to antibiotic tolerant persister cells. This transient state enables persister cells to recover and grow into an antibiotic-sensitive population. ABSTRACT Persister cells are a small subpopulation of phenotypic variants that survive high concentrations of bactericidal antibiotics. Their survival mechanisms are not heritable and can be formed stochastically or triggered by environmental stresses such as antibiotic treatment. In this study, high-throughput screening of an Escherichia coli promoter library and subsequent validation experiments identified several genes whose expression was upregulated by antibiotic treatment. Among the identified genes, waaG, guaA, and guaB were found to be important in persister cell formation in E. coli as their deletion significantly enhanced the sensitivity of cells to various antibiotics. The GuaA and GuaB enzymes form the upstream reactions of ppGpp (a global persister molecule) biosynthesis, and the deletion of guaA and guaB drastically perturbs the ppGpp regulon in E. coli. WaaG, a lipopolysaccharide glucosyltransferase, plays an important role in shaping the outer membrane structure, and the deletion of waaG dissipates the proton gradient (ΔpH) component of cellular proton motive force (PMF), perturbs cellular ATP production, and reduces type I persister formation in stationary phase. Active respiration in the stationary phase, which drives the PMF, was previously shown to play a critical role in type I persister formation, and our results associated with the waaG deficient strain further corroborate these findings. IMPORTANCE Persistence is a nonheritable trait by which normal growing cells switch phenotypically to antibiotic tolerant persister cells. This transient state enables persister cells to recover and grow into an antibiotic-sensitive population. Persister cells have been observed in many pathogenic and nonpathogenic bacteria. Previous studies highlight the complexity and diversity of bacterial persister-cell mechanisms, many of which still remain to be elucidated. Here, using promoter and knockout cell libraries in Escherichia coli, we have identified genes that reveal novel persister mechanisms. As persistence is a critical survival strategy that evolved in many bacteria, our study will enhance the current molecular-level understanding of this conserved mechanism.
Conjunctive Regular Path Queries with Capture Groups

In practice, regular expressions are usually extended by so-called capture groups or capture variables, which allow to capture a subexpression by a variable that can be referenced in the regular expression in order to describe repetitions of subwords. We investigate how this concept could be used for pattern-based graph querying; i.e., we investigate conjunctive regular path queries (CRPQs) that are extended by capture variables. If capture variables are added to CRPQs in a completely unrestricted way, then Boolean evaluation becomes PSPACE-hard in data complexity, even for single-edge graph patterns. On the other hand, if capture variables do not occur under a Kleene star, then the data complexity drops to NL-completeness. Combined complexity is in EXPSPACE but drops to PSPACE-completeness if the depth (i.e., the nesting depth of capture variables) is bounded, and it drops to NP-completeness if the size of the images of capture variables is bounded by a constant (regardless of the depth or of whether capture variables occur under a Kleene star). In the application of regular expressions as string searching tools, references to capture variables only describe exact repetitions of subwords (i.e., they implement the equality relation on strings). Following recent developments in graph database research, we also study CRPQs with capture variables that describe arbitrary regular relations. We show that if the expressions have depth 0, or if the size of the images of capture variables is bounded by a constant, then we can allow arbitrary regular relations while staying in the same complexity bounds. We also investigate the problems of checking whether a given tuple is in the solution set and computing the whole solution set. On the conceptual side, we add capture variables to CRPQs in such a way that they can be defined in an expression on one arc of the graph pattern but also referenced in expressions on other arcs. Hence, they add to CRPQs the possibility to define inter-dependencies between different paths, which is a relevant feature of pattern-based graph querying.
Sleeping Cell Detection for Resiliency Enhancements in 5G/B5G Mobile Edge-Cloud Computing Networks

The rapid increase of data traffic has brought great challenges to the maintenance and optimization of 5G and beyond, and some smart critical infrastructures, e.g., small base stations (SBSs) in cellular cells, are facing serious security and failure threats, causing resiliency degradation concerns. Among special smart critical infrastructure failures, the sleeping cell failure is hard to address since no alarm is generally triggered. Sleeping cells can remain undetected for a long time and can severely affect the quality of service/quality of experience to users. To enhance the resiliency of the SBSs in sleeping cells, we design a mobile edge-cloud computing system and propose a semi-supervised learning-based framework to dynamically detect the sleeping cells. Particularly, we consider two indicators, recovery proportion and recovery speed, to measure the resiliency of the SBSs. Moreover, in the proposed scheme, experts’ optimization experience and each period’s detection results can be utilized to iteratively improve the performance. Then we adopt a dataset from real-world networks for performance evaluation. Trace-driven evaluation results demonstrate that the proposed scheme outperforms existing sleeping cell detection schemes, and can also reduce the communication and runtime costs and enhance the resiliency of the SBSs.
A Simulation-Based Participatory Modelling Framework For Stakeholder Involvement In Urban Logistics

The popularity of both computer-based simulations and participatory modelling individually have supported design and research of many case studies. However, not much work has been done in the collaborative area wherein both the decision-making tools are used together for problem solving in the domain of urban logistics and the peer-reviewed literature on it remains sparse. This paper suggests a combination of the two fields for developing research in the area of development of urban logistics intensifying sustainability. In response to the requirements of simulation-based participatory modelling, we present a generic framework for developing these models. The framework facilitates dialogue among stakeholders with the help of a participation scheme which defines the level of participation of each stakeholder. Though the framework is presented in context of simulation-based participatory modelling, it can be easily extended to other modelling techniques.
Data Generation With Prospect: A Probability Specification Tool

Stochastic simulations of complex systems often rely on sampling dependent discrete random variables. Currently, their users are limited in expressing their intention about how these variables are distributed and related to each other over time. This limitation leads the users to program complex and error-prone sampling algorithms. This paper introduces a way to specify, declaratively and precisely, a temporal distribution over discrete variables. Our tool Prospect infers and samples this distribution by solving a system of polynomial equations. The evaluation on three simulation scenarios shows that the declarative specifications are easier to write, 3x more succinct than imperative sampling programs, and are processed correctly by Prospect.
MUSCLE ACTIVATION SEQUENCE COMPROMISES VERTICAL JUMP PERFORMANCE

The vertical jump performance is influenced by biomechanical and neuromuscular features. An electromyographic assessment was made to evaluate the influence of motor control in jumping. Fifteen healthy subjects carried out three consecutive trials of vertical jumps, during which electromyography (EMG) was recorded of the muscles of vastus medialis (VM), rectus femuris (RF), biceps femuris (BF) and gastrocnemius lateralis (LG). The time of flight and the phases of the jump were identified by a pressure sensor. The results demonstrated a significant difference (p<0.05) between the two jump trials, which were divided into the best and the worst performances. The EMG analysis showed a significant difference (p<0.05) in the EMG onset time of the BF muscle, which was activated early in the worst performance when compared to the better result. No difference was identified in the EMG activity (RMS) in 400ms before the takeoff phase of the jump, which was different from the EMG onset time. Our results suggest that early activation of the BF may influence the joint power transfer and the appropriate power resultant to promote proper performance in vertical jumps.
Wireless Battery-Free Body Temperature Sensing Device for Key Workers

We propose a battery-free temperature monitoring device that can be fitted inside the ear for an accurate body temperature measurement of a subject. The proposed application consists of two primary systems: 1) a battery-free temperature sensing ultra-high-frequency radio frequency identification sensory tag and 2) an auxiliary energy harvesting system, which enhances the sensing device's measurement accuracy and precision. The system can record changes in the localized body temperature of authenticated users with an average latency of 501 ms. The assembly demonstrated a temperature average accuracy of ± 0.14 °C operating at 866 MHz. The system performance demonstrated high stability and repeatability of reported temperature measurements. The device's dimension is a form factor that can easily fit in a front shirt pocket, with a wire tethered earbud temperature sensor. The system is developed to make sensor measurements without requiring a battery for the device. Measurements are made remotely as users pass by checkpoints installed throughout a building. The device is a cost-effective solution for monitoring body temperature in work environments.
Simulating Online Social Response: A Stimulus/Response Perspective

The paper describes a methodology for simulating online social media activities that occur in response to external events. A large number of social media simulators model information diffusion on online social networks. However, information cascades do not originate in vacuum. Rather, they often originate as a reaction to events external to the online medium. Thus, to predict activity on the social medium, one must investigate the relation between external stimuli and online social responses. The paper presents a simulation pipeline that features stimulus/response models describing how social systems react to external events of relevance to them. Two case studies are presented to test the fidelity of different models. One investigates online responses to events in the Venezuela election crisis. The other investigates online responses to developments of the China Pakistan Economic Corridor (CPEC). These case studies indicate that simple macroscopic stimulus/response models can accurately predict aggregate online trends.
T*$\boldsymbol{\varepsilon}$—Bounded-Suboptimal Efficient Motion Planning for Minimum-Time Planar Curvature-Constrained Systems

We consider the problem of finding collision-free paths for curvature-constrained systems in the presence of obstacles while minimizing execution time. Specifically, we focus on the setting where a planar system can travel at some range of speeds with unbounded acceleration. This setting can model many systems, such as fixed-wing drones. Unfortunately, planning for such systems might require evaluating many (local) time-optimal transitions connecting two close-by configurations, which is computationally expensive. Existing methods either pre-compute all such transitions in a preprocessing stage or use heuristics to speed up the search, thus foregoing any guarantees on solution quality. Our key insight is that computing all the time-optimal transitions is both (i) computationally expensive and (ii) unnecessary for many problem instances. We show that by finding bounded-suboptimal solutions (solutions whose cost is bounded by $\boldsymbol{1+\varepsilon}$ times the cost of the optimal solution for any user-provided $\boldsymbol{\varepsilon}$) and not time-optimal solutions, one can dramatically reduce the number of time-optimal transitions used. We demonstrate using empirical evaluation that our planning framework can reduce the runtime by several orders of magnitude compared to the state-of-the-art while still providing guarantees on the quality of the solution.
A Simulation-Based Approach To Compare Policies and Stakeholders’ Behaviors For The Ride-Hailing Assignment Problem

This study focused on the ride-hailing assignment problem, aiming to optimize drivers' behaviors with respect to simultaneous objectives such as maximizing service level, minimizing CO2 emissions, and minimizing riders' waiting times. Four different policies were proposed and tested with a real-world case study. With respect to current literature, we present a more realistic simulation model, capturing all characteristics of a ride-hailing system and using road networks to approximate real-time road conditions. Furthermore, it is the first work that tests the effects of different passengers' arrival conditions and analyzes the multiple objectives for different zones of a large city. Results suggest that different passengers' arrival conditions affect the four proposed policies nearly identically. Finally, the policy of drivers remaining static instead of driving while searching for passengers had the highest service level and lowest average distance per ride.
Information Consistency of Stochastic Kriging and Its Implications

In this paper, we introduce the concept of information consistency for Bayesian Gaussian process models and further provide the information consistency results for stochastic kriging (SK). It is found that, to ensure information consistency of SK, the budget allocated should grow in a fashion that is commensurate with the smoothness level of the mean response function to estimate, as the number of design points approaches infinity. Moreover, it is recommended that an experiment design consist of a relatively large number of design points with a few replications at each when given a fixed budget to expend.
Random Forest based Intrusion Detection System for AMI

The next generation in the electricity grid develops an Advanced Metering Infrastructure (AMI) with bi-directional communication. The Internet of Things (IoT) supports AMI to collect data from smart meters and identify the energy consumption pattern of consumers. A widely applied IoT routing protocol in AMI is Routing Protocol for Low-Power and Lossy Networks (RPL). The RPL protocol plays a vital role in ensuring a timely and reliable communication service on AMI. However, several factors affect the reliability of RPL in AMI, and those factors are security, delay, and unequal energy consumption between smart meters. To solve such issues, the Random Forest based Intrusion Detection System (IDS) (RFIDS) is designed for AMI. The proposed RF-IDS takes into account the results of offline traffic analysis using RF. However, it is insufficient to improve the RPL reliability since the design of RPL based only on offline traffic analysis does not necessarily mean that the designed protocol does not perform well online. Rather, the proposed IDS exploits an offline evaluation can be a first step to decide a security measure against several routing metrics. After offline traffic analysis, the RF-IDS can perform online traffic analysis to obtain delay and energy-related metrics. The offline traffic analysis process in RF-IDS selects an important feature set of the RPL protocol and improves the performance of RF over AMI. Moreover, the RF-IDS attempts to improve AMI communication reliability using a balanced energy factor and prolong the lifetime of smart meters in AMI. The efficiency of RF-IDS over AMI in terms of security, reliability, and energy is evaluated using the Cooja simulator.
On-Demand Logistics Service for Packages: Package Bidding Mechanism vs. Platform Pricing

This paper is an exploratory analysis of an on-demand service platform for packages, where the packages bid for transportation service through various auction mechanisms, trucks offer transportation services, and distribution centers match demand and supply. All agents are independent and individually incentivized to participate. Using a utility-based model, we characterize the participation incentives for all the agents, implement the state-of-the-art pricing mechanisms from industry and academia, and design and implement a first-price auction-based mechanism. Using simulation and through performance indicators like throughput, profit of the distribution center, consumer surplus, among others, we find that the package bidding mechanism significantly outperforms the status quo. Furthermore, we extend our analysis to include uniform price and Vickrey-Clarke-Groves auctions. We find that the packages prefer the Vickrey-Clarke-Groves auction, whereas the trucks and distribution centers prefer the first-price auction; although all of them prefer the bidding mechanism to the status-quo pricing mechanism.
A Gentle Introduction to Bayesian Optimization

Bayesian optimization is a sample efficient sequential global optimization method for black-box, expensive and multi-extremal functions. It generates, and keeps updated, a probabilistic surrogate model of the objective function, depending on the performed evaluations, and optimizes an acquisition function to choose a new point to evaluate. The acquisition function deals with the exploration-exploitation dilemma depending on surrogate's predictive mean and uncertainty. Many alternatives are available offering different trade-off mechanisms; different options are also possible for the probabilistic surrogate model: Gaussian Process regression is best suited for optimization over continuous search spaces while other approaches, such as Random Forests or Gaussian Prcesses with ah-hoc kernels, deal with complex search spaces spanned by nominal, numeric and conditional variables. This tutorial offers an introduction to these topics and a discussion on available tools, real-life applications, and recent advances, such as unknown constraints, multi-information sources and cost-awareness, and multi-objective optimization.