Miracle-dz/UniFAR-FTUs huggingface dataset:
Spectral embedding and the latent geometry of multipartite networks

Spectral embedding ﬁnds vector representations of the nodes of a network, based on the eigenvectors of its adjacency or Laplacian matrix, and has found applications throughout the sciences. Many such networks are multipartite, meaning their nodes can be divided into groups and nodes of the same group are never connected. When the network is multipartite, this paper demonstrates that the node representations obtained via spectral embedding live near group-speciﬁc low-dimensional subspaces of a higher-dimensional ambient space. For this reason we propose a follow-on step after spectral embedding, to recover node representations in their intrinsic rather than ambient dimension, proving uniform consistency under a low-rank, inhomogeneous random graph model. Our method nat-urally generalizes bipartite spectral embedding, in which node representations are obtained by singular value decomposition of the biadjacency or bi-Laplacian matrix. Graph embedding describes a family of tools for representing the nodes of a graph (or network) as points in space. Applications include exploratory analyses such as clustering [1, 2] or visualization [3], and predictive tasks such as classiﬁcation [4] or The purpose of this article is to develop bespoke statistical methodology for the case when the graph is multipartite. treatment
Multi-Label Classification of Thoracic Diseases using Dense Convolutional Network on Chest Radiographs

Traditional methods of identifying pathologies in X-ray images rely heavily on skilled human interpretation and are often time-consuming. The advent of deep learning techniques has enabled the development of automated disease diagnosis systems, but the performance of such systems is dependent on the quality of the model and the level of interpretability it provides. In this paper, we propose a multi-label disease diagnosis model for chest X-rays using a dense convolutional neural network (DenseNet) and model interpretability using GRADCAM. We trained our model using frontal X-rays and evaluated its performance using various quantitative metrics, including the area under the receiver operating characteristic curve (AUC). Our proposed model achieved the highest AUC score of 0.896 for the condition Cardiomegaly with an accuracy of 0.826, while the lowest AUC score was obtained for Nodule, at 0.655 with an accuracy of 0.66. To promote model interpretability and build trust in decision making, we generated heatmaps on X-rays to visualize the regions where the model paid attention to make certain predictions. Additionally, we estimated the uncertainty in model predictions by presenting the confidence interval of our measurements. Our proposed automated disease diagnosis model obtained high performance metrics in multi-label disease diagnosis tasks and provided visualization of model predictions for model interpretability.
Backdoor Detection in Reinforcement Learning

While the real world application of reinforcement learning (RL) is becoming popular, the safety concern and the robustness of an RL system require more attention. A recent work reveals that, in a multi-agent RL environment, backdoor trigger actions can be injected into a victim agent ( a.k.a. trojan agent), which can result in a catastrophic failure as soon as it sees the backdoor trigger action. We propose the problem of RL Backdoor Detection, aiming to address this safety vulnerability. An interesting observation we drew from extensive empirical studies is a trigger smoothness property where normal actions similar to the backdoor trigger actions can also trigger low performance of the trojan agent. Inspired by this observation, we propose a reinforcement learning solution TrojanSeeker to ﬁnd approximate trigger actions for the trojan agents, and further propose an efﬁcient approach to mitigate the trojan agents based on machine unlearning. Experiments show that our approach can correctly distinguish and mitigate all the trojan agents across various types of agents and environments.
DuoBFT: Resilience vs. Performance Trade-off in Byzantine Fault Tolerance

This paper presents DuoBFT, a Byzantine fault-tolerant protocol that uses trusted components to provide commit decisions in the Hybrid fault model in addition to commit decisions in the BFT model. By doing so, it enables the clients to choose the response fault model for its commands. Internally, DuoBFT commits each client command under both the hybrid and Byzantine models, but since hybrid commits take fewer communication steps and use smaller quorums than BFT commits, clients can benefit from the low-latency commits in the hybrid model. DuoBFT uses a common view-change change protocol to handle both fault models. To achieve this, we enable a notion called Flexible Quorums in the hybrid fault model by revisiting the quorum intersection requirements in hybrid protocols. The flexible quorum technique enables having a hybrid view change quorum that is of the same size as a BFT view-change quorum. This paves a path for efficiently combining both the fault models within a single unified protocol. Our evaluation on a wide-area deployment reveal that DuoBFT can provide hybrid commits with 30% lower latency to existing protocols without sacrificing throughput. In absolute terms, DuoBFT provides sub-200-millisecond latency in a geographically replicated deployment.
Analysis of A Virus Infection Model with Immune Responses

We analyze a within-host model of virus infection with antibody and CD8 cytotoxic T lymphocyte (CTL) responses proposed by Schwartz et al. (2013). The goal of this work is to gain an overview of the stability of the biologically-relevant equilibria as a function of the model’s immune response parameters. We show that the equilibria undergo at most two forward transcritical bifurcations. The model is also explored numerically and results are applied to equine infectious anemia virus infection. In order to arrive at stability of the biologically-relevant endemic equilibrium characterized by coexistence of antibody and CTL responses, the parameters promoting CTL responses need to be boosted over parameters promoting antibody production. This result may seem counter-intuitive (in that a weaker antibody response is better) but can be understood in terms of a balance between CTL and antibody responses that is needed to permit existence of CTLs. In conclusion, an intervention such as a vaccine that is intended to control a persistent viral infection with both immune responses should moderate the antibody response to allow for stimulation of the CTL response.
Web 2.0 tools as interventions for training and performance improvement

This research examined how wikis, podcast, and videocast Web 2.0 technologies were employed as interventions and the effectiveness for training and performance improvement within an organization. This dissertation examines the design, development and implementation of a social network within a workplace to promote formal and informal learning, and a collaborative workspace. The social network incorporates a variety of Web 2.0 tools including wikis, discussion forums, and the use of user generated videocasts or screencasts. The research documents the process using case study research through the context of the human performance technology model. Potential best practices for starting and promoting a workplace online collaborative community are identified. Data collected demonstrates that certain Web 2.0 tools within the workplace can lead to behavior change, and increased business results.
Both general- and central- obesity are causally associated with polycystic ovarian syndrome: Findings of a Mendelian randomization study

Introduction Obesity is observed in a majority of women with polycystic ovarian syndrome (PCOS). Using body mass index (BMI) as a proxy, previous Mendelian randomization studies revealed general obesity potentially causes PCOS. Central obesity frequently demonstrates a stronger association with PCOS, although evidence on its causality is sparse. Objectives To investigate causal effects of both central- and general- obesity on the development of PCOS via two-sample Mendelian randomization (2SMR). Methods Summary GWAS data of female-only, large-sample cohorts of European ancestry were retrieved for anthropometric markers of central obesity (waist circumference (WC), hip circumference (HC), waist-to-hip ratio (WHR)) and general obesity (BMI and its constituent variables - weight and height), from the IEU Open GWAS Project. As the outcome data, we acquired summary data from a large-sample GWAS (96391 samples; 219 cases and 96172 controls) from the FinnGen cohort. Four 2SMR methods were applied: inverse variance weighted (IVW); MR Egger (MRE); weighted median (WME); weighted mode (WMO). Single SNP-, leave-one-out-, heterogeneity-, horizontal pleiotropy- and outlier- analyses were conducted. Genetic architectures underlying causal associations were explored. Results All SNPs selected as instrumental variables demonstrated no weak instrument bias (F > 10). Three anthropometric exposures, namely, BMI (OR: 5.55 - 7.24, WC (OR: 6.79 - 24.56), and HC (OR: 6.78 - 24.56), significantly causally associated with PCOS as per IVW, WME, and WMO models. Single SNP- and leave-one-out- sensitivity analysis results were indicative of robust causal estimates. No significant heterogeneity, horizontal pleiotropy, and outliers were observed. We observed a considerable degree of overlap (7 SNPs; 17 genes) across significant causal findings as well as a number of SNPs and genes that were not shared between causal associations. Conclusions This study revealed that both and general- and central obesity potentially cause PCOS. Findings underscore the importance of addressing obesity and adiposity for the prevention and management of PCOS.
Relative numerical middle in rhesus monkeys

Animals show vast numerical competence in tasks that require both ordinal and cardinal numerical representations, but few studies have addressed whether animals can identify the numerical middle in a sequence. Two rhesus monkeys (Macaca mulatta) learned to select the middle dot in a horizontal sequence of three dots on a touchscreen. When subsequently presented with longer sequences composed of 5, 7 or 9 items, monkeys transferred the middle rule. Accuracy decreased as the length of the sequence increased. In a second test, we presented monkeys with asymmetrical sequences composed of nine items, where the numerical and spatial middle were distinct and both monkeys selected the numerical middle over the spatial middle. Our results demonstrate that rhesus macaques can extract an abstract numerical rule to bisect a discrete set of items.
Seasonal variations of serotonin in the visual system of an ant revealed by immunofluorescence and a machine learning approach

Hibernation, as an adaptation to seasonal environmental changes in temperate or boreal regions, has profound effects on mammalian brains. Social insects of temperate regions hibernate as well, but despite abundant knowledge on structural and functional plasticity in insect brains, the question of how seasonal activity variations affect insect central nervous systems has not yet been thoroughly addressed. Here, we studied potential variations of serotonin-immunoreactivity in visual information processing centres in the brain of the long-lived ant species Lasius niger. Quantitative immunofluorescence analysis revealed stronger serotonergic signals in the lamina and medulla of the optic lobes of wild or active laboratory workers than in hibernating animals. Instead of statistical inference by testing, differentiability of seasonal serotonin-immunoreactivity was confirmed by a machine learning analysis using convolutional artificial neuronal networks (ANNs) with the digital immunofluorescence images as input information. Machine learning models revealed additional differences in the third visual processing centre, the lobula. We further investigated these results by gradient-weighted class activation mapping. We conclude that seasonal activity variations are represented in the ant brain, and that machine learning by ANNs can contribute to the discovery of such variations.
New opabiniid diversifies the weirdest wonders of the euarthropod stem group

Once considered ‘weird wonders’ of the Cambrian, the emblematic Burgess Shale animals Anomalocaris and Opabinia are now recognized as lower stem-group euarthropods and have provided crucial data for constraining the polarity of key morphological characters in the group. Anomalocaris and its relatives (radiodonts) had worldwide distribution and survived until at least the Devonian. However, despite intense study, Opabinia remains the only formally described opabiniid to date. Here we reinterpret a fossil from the Wheeler Formation of Utah as a new opabiniid, Utaurora comosa nov. gen. et sp. By visualizing the sample of phylogenetic topologies in treespace, our results fortify support for the position of U. comosa beyond the nodal support traditionally applied. Our phylogenetic evidence expands opabiniids to multiple Cambrian stages. Our results underscore the power of treespace visualization for resolving imperfectly preserved fossils and expanding the known diversity and spatio-temporal ranges within the euarthropod lower stem group.
Spicing It Up: From Ubiquitous Devices to Tangible Things Through Provocation

In this pictorial, we conceptualize how modern designs take on the roles of either ‘devices’ or ‘things’ to shape everyday practices in fundamentally different ways. Drawing on Borgmann's device paradigm and provocative design, we visually recount how two designs, starting from the same brief using the same materials ultimately culminate in a ‘device’ and a ‘thing’. As householders embedded the two designs in their sustainable practices, different design qualities emerged. We use these householders’ experiences to discuss how ‘spicing up’ a design process with elements of provocation can help people engage more deeply with environmental challenges.
Gapeau: Enhancing the Sense of Distance to Others with a Head-Mounted Sensor

Human perception lacks the capabilities to accurately assess distance. The recent Covid-19 pandemic outbreak rendered this ability particularly important. Augmenting our sense of distance can help maintain safe separation from others when required. To explore how systems can help users maintain physical distance, we designed, implemented and evaluated Gapeau—a head-mounted system for augmenting the sense of distance. Our system uses proximity sensors and thermal sensing to detect and measure the distance to other people. We conducted a validation protocol, an experiment, in which we compared different feedback modalities, and an in-the-wild study to evaluate Gapeau’s performance and suitability for use in social contexts. We found that our system enabled users to more accurately determine whether they were maintaining a safe distance from others. Vibration and auditory feedback were found most effective and usable. Gapeau was perceived as socially acceptable. Our work contributes insights for augmented sensing systems with social relevance.
Can robots be good public speakers?

Our research aims at understanding if robots could be good at public speaking, what they need to achieve the level of a good public speaker and how they may surpass a human public speaker. Previous research results indicate that designing a robot speaker by mimicking some of the behaviours of a human speaker is not enough to create an effective robot speech performance. It can in fact be counter productive to strive for human-likeliness. In this paper, we describe how we programmed a toy-like, non-anthropomorphic small robot (the Anki Vector robot) to deliver a speech by extracting pose and facial expression information from the video of a human speaker and loosely retargeting this information to the robot. We also describe our experimental plans to compare Vector’s speech delivery performance with the performance of a more anthropomorphic robot (the SoftBank Pepper robot), which has been programmed to closely mimic the human speaker’s behaviour. They are compared in terms of their ability to evoke the positive affective responses necessary to spark interest, motivate the audience to listen, and engage the audience in meaningful ways.
Gr!pp: Integrating Activities of Daily Living into Hand Rehabilitation

Impaired hand function impacts many stroke patients’ daily lives, leading to a lack of independence and difficulty in performing activities of daily living (ADL). There are various assistive devices and interactive exercise monitoring systems that aim to help stroke patients perform unsupervised at-home hand rehabilitation exercises. However, for these efforts to work, the patients are required to self-supervise their rehabilitation process at home and allocate time in their daily schedule, which often is challenging. In this paper, we propose an alternative method. We hypothesize and test the idea of seamlessly integrating hand rehabilitation exercises with the products used for cooking. To this aim, we followed a research through design approach, developed Gr! pp and tested it with two stroke patients. Our results show that Gr! pp can facilitate intuitive use by its form. We also found other application areas of Gr! pp (e.g., eating) and improvement points (e.g., gamification) to promote motivation. We conclude our paper with further implications and our contribution to the tangible interaction design field.
Sensing Hand Interactions with Everyday Objects by Profiling Wrist Topography

We demonstrate rich inferences about unaugmented everyday objects and hand object interactions by measuring minute skin surface deformations at the wrist using a sensing technique based on capacitance. The wristband prototype infers muscle and tendon tension, pose, and motion, which we then map to force (9 users, 13.66 +/- 9.84 N regression error on classes 0–49.1 N), grasp (9 users, 81 +/- 7 % classification accuracy on 6 grasps), and continuous interaction (10 users, 99 +/- 1 % discrimination accuracy between 6 interactions, 89–97 % accuracy on 3 states within each interaction) using basic machine learning models. We wrapped these sensing capabilities into a proof-of-concept end-to-end system, Ubiquitous Controls, that enables virtual range inputs by sensing continuous interactions with unaugmented objects. Eight users leveraged our system to control UI widgets (like sliders and dials) with object interactions (like “cutting with scissors’’ and “squeezing a ball”). Finally, we discuss the implications and opportunities of using hands as a ubiquitous sensor of our surroundings.
MultiJam: Fabricating Jamming User Interface using Multi-material 3D Printing

This study proposes a method of fabricating jamming user interfaces using multi-material 3D printing. Through customized fabrication software, users can decide the properties of components of the interface, such as the size/shape of beads. The software automatically renders a membrane and an inner complex of beads after users decide the properties, which simplifies the manufacturing process of the jamming interface. Moreover, 3D printing allows the interface to have a double structure consisting of multiple rooms functioning jamming independently, which realizes a richer haptic experience. Finally, this paper describes the manufacturing process of the jamming interface using 3D modeling software/3D printing. It demonstrates the merits of this method with several applications, such as the haptic device and the shape-changing controller.
Tangible Self-Report Devices: Accuracy and Resolution of Participant Input

Tangible input has been explored as a means for participants to self-report experiences while minimising disruption and allowing for discrete data collection. However, the accuracy of these tangible devices has not been studied systematically. We compared six input techniques, including slider, slider with resistance, capacitive touch slider, squeeze, rotary knob, and joystick, to understand their accuracy and resolution profile. Each of these wireless devices was designed in a similar form factor and intended to be operated discretely with one hand. We assessed input accuracy and participant perceptions across devices through a controlled lab study (N = 20), highlighting diverging limits to the accuracy of the input technique and possible explanations for the differences in resolution. Our results indicate that participant accuracy was highest using a slider, and lowest using a squeeze-based input. We discuss the suitability and challenges of discreet tangible self-report techniques, and highlight open research questions for future work.
The Effect of Social Media Use On Competency and Task Performance Among Faculty in Kenya Private Universities

Universities are depending on faculty's job performance for their competitiveness and growth. There are, however, rising concerns in Kenya about the job performance of faculty members in its universities. Given the popularity of social media in universities, with faculty engaging in social media for teaching, research and service, the issue of its contribution to faculty job performance and the potential to use it to enhance job performance have assumed special importance. Yet, there is a gap in our knowledge regarding both the patterns and intensity of social media use in Kenyan universities and its impact on performance. This study aims to fill, in part, this gap by investigating the influence of social media use on job performance and intrinsic motivation, specifically focusing on task performance and competence- an intrinsic motivation measure. A research model was proposed and data was collected from 388 faculty members from Kenyan Private universities using online questionnaires. Results revealed that social media use positively affected both task performance and competence. In addition, social media use also influenced task performance through the mediation of competence.
Effects of Anxiety on Active Class Attitude in E-Learning: Analysis by considering learning strategy and willingness to learn

In this study, we clarified how anxiety during learning in e-learning affects active class attitude. Furthermore, we clarified mechanisms by which the causal relationship between anxiety and active class attitude. At that time, based on results of our previous studies and reviews of previous studies, we analyzed anxiety during learning and active class attitude in consideration of learning strategy and willingness to learn. As a result, we found that the higher anxiety of learner at the start of semester, the lower active class attitude and deep information processing at the end of semester. On the other hand, when willingness to learn is high, willingness to learn suppresses effects of anxiety, on the contrary, enhances active class attitude. Our study suggests that the causal relationship between anxiety and active class attitude is adjusted by willingness to learn. These findings obtained in this study can be evaluated as useful for maximizing learning effects in e-learning.