Miracle-dz/UniFAR-FTUs huggingface dataset:
Relative Location Estimation in Wireless Sensor Networks

Self-configuration in wireless sensor networks is a general class of estimation problems which we study via the Cramér-Rao bound (CRB). Specifically, we consider sensor location estimation when sensors measure received signal strength (RSS) or timeof-arrival (TOA) between themselves and neighboring sensors. A small fraction of sensors in the network have known location while the remaining locations must be estimated. We derive CRBs and maximumlikelihood estimators (MLEs) under Gaussian and lognormal models for the TOA and RSS measurements, respectively. An extensive TOA and RSS measurement campaign in an indoor office area illustrates MLE performance. Finally, relative location estimation algorithms are implemented in a wireless sensor network testbed and deployed in indoor and outdoor environments. The measurements and testbed experiments demonstrate 1 m RMS location errors using TOA, and 1 m to 2 m RMS location errors using RSS.
Adapting a Natural Language Processing Tool to Facilitate Clinical Trial Curation for Personalized Cancer Therapy

The design of personalized cancer therapy based upon patients’ molecular profile requires an enormous amount of effort to review, analyze and integrate molecular, pharmacological, clinical and patient-specific information. The vast size, rapid expansion and non-standardized formats of the relevant information sources make it difficult for oncologists to gather pertinent information that can support routine personalized treatment. In this paper, we introduce informatics tools that assist the retrieval and curation of cancer-related clinical trials involving targeted therapies. Particularly, we adapted and extended an existing natural language processing tool, and explored its applicability in facilitating our annotation efforts. The system was evaluated using a gold standard of 539 curated clinical trials, demonstrating promising performance and good generalizability (81% accuracy in predicting genotype-selected trials and an average recall of 0.85 in predicting specific selection criteria).
A study of afferent input to the inferior olivary complex in the rat by retrograde axonal transport of horseradish peroxidase

Under direct visualization, minute amounts of horseradish peroxidase were injected by controlled air pressure through glass micropipettes into the inferior olive of the albino rat. Well localized unilateral injections were obtained, without damage to other brainstem areas or spread into the adjacent reticular formation. After 24 to 36 hours, the entire brain and spinal cord were examined by light microscopy for labelled neurons supplying afferents to the inferior olive.
Training Self-supervised Class-conditional GANs with Classifier Gradient Penalty and Dynamic Prior

Class-conditional GAN generates class-conditional data from continuous latent distribution and categorical distribution. Typically, a class-conditional GAN can be trained only when the label, which is the conditional categorical distribution of the target data, is given. In this paper, we propose a novel GAN that allows the model to perform self-supervised class-conditional data generation and clustering without knowing labels, optimal prior categorical probability, or metric function. The proposed method uses a discriminator, a classifier, and a generator. The classifier is trained with cross-entropy loss to predict the conditional vector of the fake data. Also, the conditional vector of real data predicted by the classifier is used to train the class-conditional GAN. When training class-conditional GAN with this classifier, the decision boundary of the classifier falls to the local optima where the density of the data is minimized. The proposed method adds a classifier gradient penalty loss to the classifier loss to prevent the classifier’s decision boundary from falling into narrow a range of local optima. It regulates the gradient of the classifier’s output to prevent the gradient near the decision boundary from becoming too large. As the classifier gradient penalty loss weight increases, the decision boundary falls into a wider range of local optima. It means that the sensitivity of each class can be adjusted by the weight of the gradient penalty loss. Additionally, the proposed method updates the prior categorical probability with the categorical probability of real data predicted by the classifier. As training progresses, the entropy of the prior categorical probability decreases and converges according to the classifier gradient penalty loss weight.
Text Line Extraction Method Using Domain-Based Active Contour Model

In this paper, we propose a novel text line extraction method using domain-based optimization for complex documents such as engineering drawings. In the complex documents, variations of text line due to differences in size, interval, and direction, as well as the complex layout (text lines and drawing objects are mixed) make the problem of automatic text line extraction extremely challenging. The proposed method divides a document into some domains based on line objects or spaces. Then, this method generates a potential map in each domain. Finally, text lines are extracted by an active contour model based on the potential map. In this process, the potential map is generated by a potential function that has some parameters concerning interval, character size and drawing objects. By optimizing these parameters, this method extracts text lines easily in complex documents. Our experimental result shows better performance than a conventional method for engineering drawings.
Using genomic data to estimate genetic correlations between countries with different levels of connectedness

Genetic correlations ( r g ) between countries are required for international evaluations. The estimation of those r g is challenging or even unfeasible using only pedigree and phenotypes when poor connectedness between countries is structural in the data due to a limited number of bulls having recorded (grand-)offspring across countries. Genomic information could be used to estimate r g between countries by capturing connectedness that is not traced by pedigree recordings. Indeed, populations that appear as (completely) disconnected through pedigree can, theoretically, be connected through genomic data. Thus, our study aimed to investigate if estimates of r g between countries based on genomic information are more accurate compared to estimates based on pedigree data, considering different levels of genetic connectedness. A maternally affected trait mimicking weaning weight was simulated for two beef cattle populations of the same breed. Different levels of connectedness between populations were simulated by exchanging different proportions of top sires in the last five generations: 0% (completely disconnected), 2.5% (lowly connected), 5% (medium), and 20% (high). Genomic data in the form of individual SNP genotypes at medium density were stored in the last three generations and used only for the estimation process. r g between populations were estimated using three different relationship matrices: i) a pedigree-based relationship matrix ( A ) including all phenotyped animals; ii) a genomic relationship matrix ( G ) including phenotyped and genotyped animals only from the last three generations; and iii) a combined pedigree and genomic relationship matrix ( H ) including all phenotyped and genotyped animals. With disconnected and lowly connected populations, estimates of direct and maternal r g were, on average, close to the simulated values when using genomic data through G or H . With lowly connected populations, estimates of direct r g were close to the simulated values when using A , but estimates of maternal r g showed large variation. With more connected populations, estimates obtained with A , G , and H matrices were overall similar. For all scenarios, when using genomic data in the estimation process, estimates of r g had smaller standard errors. Our results show that genomic data can help the estimation of r g between countries and especially reduce their standard errors for populations that appear as completely disconnected or lowly connected through pedigree information, such as in beef and (small) dairy cattle populations.
Identiﬁcation of TMEM126A as OXA1L-interacting protein reveals cotranslational quality control in mitochondria

SUMMARY Cellular proteostasis requires transport of polypeptides across membranes. Although defective transport processes trigger cytosolic rescue and quality control mechanisms that clear translocases and membranes from unproductive cargo, proteins that are synthesized within mitochondria are not accessible to these mechanisms. Mitochondrial-encoded proteins are inserted cotranslationally into the inner membrane by the conserved insertase OXA1L. Here, we identify TMEM126A as a OXA1L-interacting protein. TMEM126A associates with mitochondrial ribosomes and translation products. Loss of TMEM126A leads to the destabilization of mitochondrial translation products, triggering an inner membrane quality control process, in which newly synthesized proteins are degraded by the mitochondrial i AAA protease. Our data reveal that TMEM126A cooperates with OXA1L in protein insertion into the membrane. Upon loss of TMEM126A, the cargo-blocked OXA1L insertase complexes undergo proteolytic clearance by the i AAA protease machinery together with its cargo.
Construção e validação da Escala Multifatorial de Atração por Eventos Mórbidos

Objetivo. Este estudo buscou construir e validar a Escala Multifatorial de Atração por Eventos Mórbidos (AMAEM). Método. Do primeiro estudo participaram 252 pessoas da população geral, as quais responderam à EMAEM e a perguntas sociodemográficas. A análise de componentes principais indicou uma estrutura de cinco fatores. No segundo estudo, a amostra foi de 609 pessoas da população geral, as quais responderam a EMAEM, escala de personalidade e busca de sensações. Resultado. A análise fatorial confirmatória apoaiou a adequação do modelo de cinco fatores. Adicionalmente, demonstrou ainda que os fatores da EMAEM podem contribuir com a compreensão de características da personalidade e sua associação com o engajamento em comportamentos antissociais.
Acoustic feature selection utilizing multiple kernel learning for classification of children with autism spectrum and typically developing children

This paper reports the result of a classification experiment carried out using acoustic features for children with autism spectrum, where a new feature-weighting method using a multiple kernel learning (MKL) algorithm is proposed for classification between children with autism spectrum and typically developing children. Our MKL-SVM simultaneously estimates both the classification boundary and weight of each acoustic feature, where 484 acoustic features are used in our experiments. The estimated weight indicates how acoustic features are useful for classification. Our results show the large weight acoustic features mainly for line spectral frequencies in the classification experiment using acoustic features for children with autism spectrum.
Design and modeling of dexterous aerial manipulator

In this paper, the design, modeling and experimental verification of a large workspace, parallel aerial manipulator is presented. The proposed manipulator has 3 Degrees of Freedom (DoFs) and enables physical interaction on the sides, as well as below the aerial robot. The design parameters of the manipulator are chosen such that it achieves large and singularity-free workspace in combination with high dexterity. A Global Conditioning Index (GCI) is defined and used as a performance index for the manipulator over its complete workspace. Given the manipulator design parameters, a holistic model of the redundant aerial manipulation system is derived capturing the coupled dynamics of the aerial vehicle and the manipulator. Free flight experimental studies are utilized to validate the system, demonstrate its redundant DoFs, and evaluate its performance.
Using eye tracking for interaction

The development of cheaper eye trackers and open source software for eye tracking and gaze interaction brings the possibility to integrate eye tracking into everyday use devices as well as highly specialized equipment. Apart from providing means for analyzing eye movements, eye tracking also offers the possibility of a natural user interaction modality. Gaze control interfaces are already used within assistive applications for disabled users. However, this novel user interaction possibility comes with its own set of limitations and challenges. The aim of this SIG is to provide a forum for Designers, Researchers and Usability Professionals to discuss the role of eye tracking as a user interaction method in the future as well as the technical and user interaction challenges that using eye tracking as an interaction method brings.
Multi-Goal Motion Memory

—Autonomous mobile robots (e.g., warehouse logistics robots) often need to traverse complex, obstacle-rich, and changing environments to reach multiple fixed goals (e.g., warehouse shelves). Traditional motion planners need to calculate the entire multi-goal path from scratch in response to changes in the environment, which result in a large consumption of computing resources. This process is not only time-consuming but also may not meet real-time requirements in application scenarios that require rapid response to environmental changes. In this paper, we provide a novel Multi-Goal Motion Memory technique that allows robots to use previous planning experiences to accelerate future multi-goal planning in changing environments. Specifically, our technique predicts collision-free and dynamically-feasible trajectories and distances between goal pairs to guide the sampling process to build a roadmap, to inform a Traveling Salesman Problem (TSP) solver to compute a tour, and to efficiently produce motion plans. Experiments conducted with a vehicle and a snake-like robot in obstacle-rich environments show that the proposed Motion Memory technique can substantially accelerate planning speed by up to 90%. Furthermore, the solution quality is comparable to state-of-the-art algorithms and even better in some environments.
Alterations in Resting Length‐Tension Relations of Cardiac Muscle Induced by Changes In Contractile Force

Effects of sustained postextrasystolic potentiation (P.S.), norepinephrine (NE), and calcium on resting length-tension relations of heart muscle were studied in the cat papillary muscle and isovolumic dog ventricle. In papillary muscles, P.S. , calcium or NE produced no change in diastolic compliance when the muscles were freely isotonic, or afterloaded, and force remained constant. However, under isometric conditions, P.S. induced a small fall in diastolic tension while systolic force rose. Similar decrements in diastolic tension were observed when force of contraction was augmented by simply increasing afterload from a constant initial muscle length. In the isovolumic ventricle, P.S. , NE, and calcium each induced substantial increments in developed pressure, accompanied by small decreases in end-diastolic pressure. The latter was reduced or abolished by augmenting the initial contractile state with calcium or NE, so that the super-imposition of P.S. induced only trivial increments in developed pressure. It is concluded that P.S. , calcium, or NE do not induce changes in the resting length-tension relations of heart muscle per se. However, resting length at any given resting tension does increase slightly upon the augmentation of systolic force alone. It is postulated that these findings may be explained by the existence of a series viscous component.
Hybrid sensorless control for SPMSM With multiple saliencies

Surface-mounted permanent magnet synchronous machine with concentrated windings (cwSPMSM) is widely adopted in many industry applications. However, strong multiple saliencies is a main drawback impeding applying sensorless control over this type of machine. This work proposes a hybrid sensorless control scheme which integrates a novel Secondary Saliency Tracking (SST) algorithm and an improved Active Flux Observer (AFO) for cwSPMSMs. The saliency signals of a typical cwPMSM under consideration are experimentally investigated. Instead of processing the primary saliency signal, secondary saliency signal which has a better signal to noise ratio and more precise resolution, is processed by a special designed band pass filter for speed and rotor position estimation reaching a very low speed range with full-load. An improved AFO method with robust structure but effective performance is adopted for higher speed range estimation. The transient phase (switching between the SST and AFO) performance is guaranteed by a smooth transition region design, reaching a Hybrid Sensorless Control with wide speed range. Finally the effectiveness and accuracy of the newly proposed Hybrid Sensorless Control method are both verified by experimental results.
A Consumer-Centric Market for Database Computation in the Cloud

The availability of public computing resources in the cloud has revolutionized data analysis, but requesting cloud resources often involves complex decisions for consumers. Under the current pricing mechanisms, cloud service providers offer several service options and charge consumers based on the resources they use. Before they can decide which cloud resources to request, consumers have to estimate the completion time and cost of their computational tasks for different service options and possibly for different service providers. This estimation is challenging even for expert cloud users. We propose a new market-based framework for pricing computational tasks in the cloud. Our framework introduces an agent between consumers and cloud providers. The agent takes data and computational tasks from users, estimates time and cost for evaluating the tasks, and returns to consumers contracts that specify the price and completion time. Our framework can be applied directly to existing cloud markets without altering the way cloud providers offer and price services. In addition, it simplifies cloud use for consumers by allowing them to compare contracts, rather than choose resources directly. We present design, analytical, and algorithmic contributions focusing on pricing computation contracts, analyzing their properties, and optimizing them in complex workflows. We conduct an experimental evaluation of our market framework over a real-world cloud service and demonstrate empirically that our market ensures three key properties: competitiveness, fairness, and resilience. Finally, we present a fine-grained pricing mechanism for complex workflows and show that it can increase agent profits by more than an order of magnitude in some cases.
Benchmark of Probabilistic Programming Languages

Probabilistic programming is a tool for statistical modelling that automatically infers statistical information to extract the data pattern and supports experts in analysing the surrounding world by observing the current world of knowledge without hypotheses. Probabilistic programming languages (PPLs) are the intuitional programming languages for probabilistic programming. Compared with other general-purpose programming languages like C, Java and Python, PPLs naturally infer based on the data distribution and the prior possibilities that experts define and grasp the data pattern for research and applications purposes. Like machine learning models (e.g., deep neural networks (DNNs)), model training with PPLs has an analogous process by replacing the machine learning model with the graphical model. However, although PPLs seem to be essential tools in modern machine learning, there has been no su ffi cient research into PPLs in the last decades. Recently, PPLs were anew raising researchers’ interests in exploring them. In this case, helping researchers find the most e ffi cient PPL with an adequate trade-o ff of accuracy becomes crucial. This project examined four PPLs based on three Bayesian Networks (BNs), mainly based on a Cardinality Estimation (CardEst) use case called BayesCard. We focus on making exact inferences on BNs. By experiments, we found that the PPL named dice has outperformed our baseline, BayesCard, in terms of latencies on the Census dataset while achieving almost the same accuracy as BayesCard.
Learning sparse features in granular space for multi-view face detection

In this paper, a novel sparse feature set is introduced into the Adaboost learning framework for multi-view face detection (MVFD), and a learning algorithm based on heuristic search is developed to select sparse features in granular space. Compared with Haar-like features, sparse features are more generic and powerful to characterize multi-view face pattern that is more diverse and asymmetric than frontal face pattern. In order to cut down search space to a manageable size, we propose a multi-scaled search algorithm that is about 6 times faster than brute-force search. With this method, a MVFD system is implemented that covers face pose changes over +/-45deg rotation in plane (RIP) and +/-90deg rotation off plane (ROP). Experiments over well-know test set are reported to show its high performance in both accuracy and speed
Ethanol‐Induced Impairment in the Biosynthesis of N‐Linked Glycosylation

Deficiency in N‐linked protein glycosylation is a long‐known characteristic of alcoholic liver disease and congenital disorders of glycosylation. Previous investigations of ethanol‐induced glycosylation deficiency demonstrated perturbations in the early steps of substrate synthesis and in the final steps of capping N‐linked glycans in the Golgi. The significance of the biosynthesis of N‐glycan precursors in the endoplasmic reticulum, however, has not yet been addressed in alcoholic liver disease. Ethanol‐metabolizing hepatoma cells were treated with increasing concentrations of ethanol. Transcript analysis of genes involved in the biosynthesis of N‐glycans, activity assays of related enzymes, dolichol‐phosphate quantification, and analysis of dolichol‐linked oligosaccharides were performed. Upon treatment of cells with ethanol, we found a decrease in the final N‐glycan precursor Dol‐PP‐GlcNAc2Man9Glc3 and in C95‐ and C100‐dolichol‐phosphate levels. Transcript analysis of genes involved in N‐glycosylation showed a 17% decrease in expression levels of DPM1, a subunit of the dolichol‐phosphate‐mannose synthase, and an 8% increase in RPN2, a subunit of the oligosaccharyl transferase. Ethanol treatment decreases the biosynthesis of dolichol‐phosphate. Consequently, the formation of N‐glycan precursors is affected, resulting in an aberrant precursor assembly. Messenger RNA levels of genes involved in N‐glycan biosynthesis are slightly affected by ethanol treatment, indicating that the assembly of N‐glycan precursors is not regulated at the transcriptional level. This study confirms that ethanol impairs N‐linked glycosylation by affecting dolichol biosynthesis leading to impaired dolichol‐linked oligosaccharide assembly. Together our data help to explain the underglycosylation phenotype observed in alcoholic liver disease and congenital disorders of glycosylation. J. Cell. Biochem. 115: 754–762, 2014. © 2013 Wiley Periodicals, Inc.
On using energy storage systems in switching attacks that destabilize smart grid systems

A new class of switching attacks in smart grid systems is investigated in this work. The proposed attack relies on calculated switchings of a fast-acting energy storage system (ESS) in order to drive the system state variable of the target generator beyond the stability boundary. Based on understanding the structure of the power system, an adversary uses the swing equation to find the stability boundary of the target generator. In order to conduct a successful switching attack, the adversary intercepts the power system's measurements and switches the circuit breaker of the ESS back-and-forth depending on the value of system state variable. Numerical results show the effectiveness of the proposed switching attack when applied to the New England power system.