Miracle-dz/UniFAR-FTUs huggingface dataset:
Design, Implementation, and Evaluation of a Variable Stiffness Transradial Hand Prosthesis

We present the design, implementation, and experimental evaluation of a low-cost, customizable, easy-to-use transradial hand prosthesis capable of adapting its compliance. Variable stiffness actuation (VSA) of the prosthesis is based on antagonistically arranged tendons coupled to nonlinear springs driven through a Bowden cable based power transmission. Bowden cable based antagonistic VSA can, not only regulate the stiffness and the position of the prosthetic hand but also enables a light-weight and low-cost design, by the opportunistic placement of motors, batteries, and controllers on any convenient location on the human body, while nonlinear springs are conveniently integrated inside the forearm. The transradial hand prosthesis also features tendon driven underactuated compliant fingers that allow natural adaption of the hand shape to wrap around a wide variety of object geometries, while the modulation of the stiffness of their drive tendons enables the prosthesis to perform various tasks with high dexterity. The compliant fingers of the prosthesis add inherent robustness and flexibility, even under impacts. The control of the variable stiffness transradial hand prosthesis is achieved by an sEMG based natural human-machine interface.
Segmenting Ships in Satellite Imagery With Squeeze and Excitation U-Net

The ship-detection task in satellite imagery presents significant obstacles to even the most state of the art segmentation models due to lack of labelled dataset or approaches which are not able to generalize to unseen images. The most common methods for semantic segmentation involve complex two-stage networks or networks which make use of a multi-scale scene parsing module. In this paper, we propose a modified version of the popular U-Net architecture called Squeeze and Excitation U-Net and train it with a loss that helps in directly optimizing the intersection over union (IoU) score. Our method gives comparable performance to other methods while having the additional benefit of being computationally efficient.
Semi-Asymmetric Parallel Graph Algorithms for NVRAMs

Emerging non-volatile main memory (NVRAM) technologies provide novel features for large-scale graph analytics, combining byte-addressability, low idle power, and improved memory-density. Systems are likely to have an order of magnitude more NVRAM than traditional memory (DRAM), allowing large graph problems to be solved efficiently at a modest cost on a single machine. However, a significant challenge in achieving high performance is in accounting for the fact that NVRAM writes can be significantly more expensive than NVRAM reads. In this paper, we propose an approach to parallel graph analytics in which the graph is stored as a read-only data structure (in NVRAM), and the amount of mutable memory is kept proportional to the number of vertices. Similar to the popular semi-external and semi-streaming models for graph analytics, the approach assumes that the vertices of the graph fit in a fast read-write memory (DRAM), but the edges do not. In NVRAM systems, our approach eliminates writes to the NVRAM, among other benefits. We present a model, the Parallel Semi-Asymmetric Model (PSAM), to analyze algorithms in the setting, and run experiments on a 48-core NVRAM system to validate the effectiveness of these algorithms. To this end, we study over a dozen graph problems. We develop parallel algorithms for each that are efficient, often work-optimal, in the model. Experimentally, we run all of the algorithms on the largest publicly-available graph and show that our PSAM algorithms outperform the fastest prior algorithms designed for DRAM or NVRAM. We also show that our algorithms running on NVRAM nearly match the fastest prior algorithms running solely in DRAM, by effectively hiding the costs of repeatedly accessing NVRAM versus DRAM.
Towards Successful Social Media Advertising: Predicting the Influence of Commercial Tweets

Businesses communicate using Twitter for a variety of reasons -- to raise awareness of their brands, to market new products, to respond to community comments, and to connect with their customers and potential customers in a targeted manner. For businesses to do this effectively, they need to understand which content and structural elements about a tweet make it influential, that is, widely liked, followed, and retweeted. This paper presents a systematic methodology for analyzing commercial tweets, and predicting the influence on their readers. Our model, which use a combination of decoration and meta features, outperforms the prediction ability of the baseline model as well as the tweet embedding model. Further, in order to demonstrate a practical use of this work, we show how an unsuccessful tweet may be engineered (for example, reworded) to increase its potential for success.
Comprehensive analysis of antiviral adaptive immunity formation and reactivation down to single-cell level

The diverse repertoire of T-cell receptors (TCR) plays a key role in the adaptive immune response to infections. Previous studies show that secondary responses to the yellow fever vaccine — the model for acute infection in humans — are weaker than primary ones, but only quantitative measurements can describe the concentration changes and lineage fates for distinct T-cell clones in vivo over time. Using TCR alpha and beta repertoire sequencing for T-cell subsets, as well as single-cell RNAseq and TCRseq, we track the concentrations and phenotypes of individual T-cell clones in response to primary and secondary yellow fever immunization showing their large diversity. We confirm the secondary response is an order of magnitude weaker, albeit 10 days faster than the primary one. Estimating the fraction of the T-cell response directed against the single immunodominant epitope, we identify the sequence features of TCRs that define the high precursor frequency of the two major TCR motifs specific for this particular epitope. We also show the consistency of clonal expansion dynamics between bulk alpha and beta repertoires, using a new methodology to reconstruct alpha-beta pairings from clonal trajectories.
Fair Generative Modeling via Weak Supervision

Real-world datasets are often biased with respect to key demographic factors such as race and gender. Due to the latent nature of the underlying factors, detecting and mitigating bias is especially challenging for unsupervised machine learning. We present a weakly supervised algorithm for overcoming dataset bias for deep generative models. Our approach requires access to an additional small, unlabeled reference dataset as the supervision signal, thus sidestepping the need for explicit labels on the underlying bias factors. Using this supplementary dataset, we detect the bias in existing datasets via a density ratio technique and learn generative models which efficiently achieve the twin goals of: 1) data efficiency by using training examples from both biased and reference datasets for learning; and 2) data generation close in distribution to the reference dataset at test time. Empirically, we demonstrate the efficacy of our approach which reduces bias w.r. t. latent factors by an average of up to 34.6% over baselines for comparable image generation using generative adversarial networks.
A framework for second-order eigenvector centralities and clustering coefficients

We propose and analyse a general tensor-based framework for incorporating second-order features into network measures. This approach allows us to combine traditional pairwise links with information that records whether triples of nodes are involved in wedges or triangles. Our treatment covers classical spectral methods and recently proposed cases from the literature, but we also identify many interesting extensions. In particular, we define a mutually reinforcing (spectral) version of the classical clustering coefficient. The underlying object of study is a constrained nonlinear eigenvalue problem associated with a cubic tensor. Using recent results from nonlinear Perron–Frobenius theory, we establish existence and uniqueness under appropriate conditions, and show that the new spectral measures can be computed efficiently with a nonlinear power method. To illustrate the added value of the new formulation, we analyse the measures on a class of synthetic networks. We also give computational results on centrality and link prediction for real-world networks.
To use or not to use synthetic stellar spectra in population synthesis models?

Stellar population synthesis (SPS) models are invaluable to study star clusters and galaxies. They provide means to extract stellar masses, stellar ages, star formation histories, chemical enrichment, and dust content of galaxies from their integrated spectral energy distributions, colours, or spectra. As most models, they contain uncertainties that can hamper our ability to model and interpret observed spectra. This work aims at studying a specific source of model uncertainty: the choice of an empirical versus a synthetic stellar spectral library. Empirical libraries suffer from limited coverage of parameter space, while synthetic libraries suffer from modelling inaccuracies. Given our current inability to have both ideal stellar-parameter coverage with ideal stellar spectra, what should one favour: better coverage of the parameters (synthetic library) or better spectra on a star-by-star basis (empirical library)? To study this question, we build a synthetic stellar library mimicking the coverage of an empirical library, and SPS models with different choices of stellar library tailored to these investigations. Through the comparison of model predictions and the spectral fitting of a sample of nearby galaxies, we learned that predicted colours are more affected by the coverage effect than the choice of a synthetic versus empirical library; the effects on predicted spectral indices are multiple and defy simple conclusions; derived galaxy ages are virtually unaffected by the choice of the library, but are underestimated when SPS models with limited parameter coverage are used; metallicities are robust against limited HRD coverage, but are underestimated when using synthetic libraries.
Dual IV: A Single Stage Instrumental Variable Regression

We present a novel single-stage procedure for instrumental variable (IV) regression called DualIV which simplifies traditional two-stage regression via a dual formulation. We show that the common two-stage procedure can alternatively be solved via generalized least squares. Our formulation circumvents the first-stage regression which can be a bottleneck in modern two-stage procedures for IV regression. We also show that our framework is closely related to the generalized method of moments (GMM) with specific assumptions. This highlights the fundamental connection between GMM and two-stage procedures in IV literature. Using the proposed framework, we develop a simple kernel-based algorithm with consistency guarantees. Lastly, we give empirical results illustrating the advantages of our method over the existing two-stage algorithms.
Is it a Fruit, an Apple or a Granny Smith? Predicting the Basic Level in a Concept Hierarchy

The "basic level", according to experiments in cognitive psychology, is the level of abstraction in a hierarchy of concepts at which humans perform tasks quicker and with greater accuracy than at other levels. We argue that applications that use concept hierarchies - such as knowledge graphs, ontologies or taxonomies - could significantly improve their user interfaces if they `knew' which concepts are the basic level concepts. This paper examines to what extent the basic level can be learned from data. We test the utility of three types of concept features, that were inspired by the basic level theory: lexical features, structural features and frequency features. We evaluate our approach on WordNet, and create a training set of manually labelled examples that includes concepts from different domains. Our findings include that the basic level concepts can be accurately identified within one domain. Concepts that are difficult to label for humans are also harder to classify automatically. Our experiments provide insight into how classification performance across domains could be improved, which is necessary for identification of basic level concepts on a larger scale.
Ghost-free resummation of gravitational interactions of a two-form gauge field

We find that the recently-proposed ghost-free interaction of a 2-form gauge field in four dimensions, which contains derivative couplings in a nonperturbative manner, can be regarded as a resummation of ghostly interaction terms. We investigate the higher derivative structure of this model in a minisuperspace description and demonstrate that the higher derivative terms can be removed by taking appropriate combinations of the Euler-Lagrange equations, while a truncation at a finite order spoils this structure. We also show that this nature is peculiar to four dimensions.
New methods to assess and improve LIGO detector duty cycle

A network of three or more gravitational wave detectors simultaneously taking data is required to generate a well-localized sky map for gravitational wave sources, such as GW170817. Local seismic disturbances often cause the LIGO and Virgo detectors to lose light resonance in one or more of their component optic cavities, and the affected detector is unable to take data until resonance is recovered. In this paper, we use machine learning techniques to gain insight into the predictive behavior of the LIGO detector optic cavities during the second LIGO–Virgo observing run. We identify a minimal set of optic cavity control signals and data features which capture interferometer behavior leading to a loss of light resonance, or lockloss. We use these channels to accurately distinguish between lockloss events and quiet interferometer operating times via both supervised and unsupervised machine learning methods. This analysis yields new insights into how components of the LIGO detectors contribute to lockloss events, which could inform detector commissioning efforts to mitigate the associated loss of uptime. Particularly, we find that the state of the component optical cavities is a better predictor of loss of lock than ground motion trends. We report prediction accuracies of 98% for times just prior to lock loss, and 90% for times up to 30 s prior to lockloss, which shows promise for this method to be applied in near-real time to trigger preventative detector state changes. This method can be extended to target other auxiliary subsystems or times of interest, such as transient noise or loss in detector sensitivity. Application of these techniques during the third LIGO–Virgo observing run and beyond would maximize the potential of the global detector network for multi-messenger astronomy with gravitational waves.
Joint Analysis of Social and Item Response Networks with Latent Space Models

The adjustment of students to a school environment is fundamentally linked to the friendship networks they form with their peers. Consequently, the complete picture of a student's adjustment can only be obtained by taking into account both their friendship network and their reported perceptions of the school environment. However, there is a lack of flexible statistical models and methods that can jointly analyze a social network with an item-response data matrix. In this paper, we propose a latent space model for heterogeneous (multimodal) networks (LSMH) and its extension LSMH-Item, which combine the framework of latent space modeling in network analysis with item response theory in psychometrics. Using LSMH, we summarize the information from the social network and the item responses in a person-item joint latent space. We use a Variational Bayesian Expectation-Maximization estimation algorithm to estimate the item and person locations in the joint latent space. This methodology allows for effective integration, informative visualization, and prediction of social networks and item responses. We apply the proposed methodology to data collected from 16 third-grade classrooms comprised of 451 third-grade students' self-reported friendships and school liking, which were collected as part of the Early Learning Ohio project. Through the person-item joint latent space, we are able to identify students with potential adjustment difficulties and found consistent connection between students' friendship networks and their well-being. We believe that using LSMH, researchers will be able to easily identify students in need of intervention and revolutionize the understanding of social behaviors.
Deep causal representation learning for unsupervised domain adaptation

Studies show that the representations learned by deep neural networks can be transferred to similar prediction tasks in other domains for which we do not have enough labeled data. However, as we transition to higher layers in the model, the representations become more task-specific and less generalizable. Recent research on deep domain adaptation proposed to mitigate this problem by forcing the deep model to learn more transferable feature representations across domains. This is achieved by incorporating domain adaptation methods into deep learning pipeline. The majority of existing models learn the transferable feature representations which are highly correlated with the outcome. However, correlations are not always transferable. In this paper, we propose a novel deep causal representation learning framework for unsupervised domain adaptation, in which we propose to learn domain-invariant causal representations of the input from the source domain. We simulate a virtual target domain using reweighted samples from the source domain and estimate the causal effect of features on the outcomes. The extensive comparative study demonstrates the strengths of the proposed model for unsupervised domain adaptation via causal representations.
Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications

Suicide is a critical issue in modern society. Early detection and prevention of suicide attempts should be addressed to save people’s life. Current suicidal ideation detection (SID) methods include clinical methods based on the interaction between social workers or experts and the targeted individuals and machine learning techniques with feature engineering or deep learning for automatic detection based on online social contents. This article is the first survey that comprehensively introduces and discusses the methods from these categories. Domain-specific applications of SID are reviewed according to their data sources, i.e., questionnaires, electronic health records, suicide notes, and online user content. Several specific tasks and data sets are introduced and summarized to facilitate further research. Finally, we summarize the limitations of current work and provide an outlook of further research directions.
Household Units in the Analysis of Prehistoric Social Complexity, Southern Cook Islands

Polynesian and other Oceanic societies have often informed research into social complexity. McGuire (1983) has proposed a means of measuring complexity that does not assume any particular organizational form. The examination of prehistoric household remains allows archaeologists to compare common units of social organization across societies for more meaningful comparisons of past social organization. This paper discusses house remains excavated on three islands in the Southern Cook Islands of central Polynesia for the information they provide about past social organization on the islands and provides comparison between three closely related island societies.
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Many real-world networks such as Twitter and YouTube are given as fully dynamic graph streams represented as sequences of edge insertions and deletions. (e.g., users can subscribe and unsubscribe to channels on YouTube). Existing similarity estimation methods such as MinHash and OPH are customized to static graphs. We observe that they are indeed sampling methods and exhibit a sampling bias when applied to fully dynamic graph streams, which results in large estimation errors. To solve this challenge, we develop a fast and accurate sketch method VOS. VOS processes each edge in the graph stream of interest with small time complexity O(1) and uses small memory space to build a compact sketch of the dynamic graph stream over time. Based on the sketch built on-the-fly, we develop a method to estimate user similarities over time. We conduct extensive experiments and the experimental results demonstrate the efficiency and efficacy of our method.
2. Planning Infrastructure in the Face of Complexity: Enhancing the Adaptive Capacity of Mega Projects

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The Socio-Economic Causes of Obesity

An increasing number of Americans are obese, with a body mass index of 30 or more. In fact, the latest estimates indicate that about 30% of Americans are currently obese, which is roughly a 100% increase from 25 years ago. It is well accepted that weight gain is caused by caloric imbalance, where more calories are consumed than expended. Nevertheless, it is not clear why the prevalence of obesity has increased so dramatically over the last 30 years. We simultaneously estimate the effects of the various socio-economic factors on weight status, considering in our analysis many of the socio-economic factors that have been identified by other researchers as important influences on caloric imbalance: employment, physical activity at work, food prices, the prevalence of restaurants, cigarette smoking, cigarette prices and taxes, food stamp receipt, and urbanization. We use 1979- and 1997-cohort National Longitudinal Survey of Youth (NLSY) data, which allows us to compare the prevalence of obesity between cohorts surveyed roughly 25 years apart. Using the traditional Blinder-Oaxaca decomposition technique, we find that cigarette smoking has the largest effect: the decline in cigarette smoking explains about 2% of the increase in the weight measures. The other significant factors explain less.
Microsoft Word-UML4IoTInManufacturing4ArXiv_5

Internet of Things is changing the world. The manufacturing industry has already identified that the IoT brings great opportunities to retain its leading position in economy and society. However, the adoption of this new technology changes the development process of the manufacturing system and raises many challenges. In this paper the modern manufacturing system is considered as a composition of cyber-physical, cyber and human components, and IoT is used as a glue for their integration as far as it regards their cyber interfaces. The key idea is a UML profile for the IoT with an alternative to apply the approach also at the source code level specification of the component in case that a UML design specification is not available. The proposed approach, namely UML4IoT, fully automates the generation process of the IoT-compliant layer that is required for the cyberphysical component to be integrated in the modern IoT manufacturing environment. A prototype implementation of the myLiqueur laboratory system has been developed to demonstrate the applicability and effectiveness of the UML4IoT approach.