Miracle-dz/UniFAR-FTUs huggingface dataset:
Belief Function Theory in Constraint Satisfaction Problems: a Unifying Approach

The Constraint Satisfaction Problem (CSP) is acknowledged as a simple declarative formalism for modeling welldefined decision problems. However, real-world problems are usually ill-defined, especially, under uncertain circumstances. In such situation, uncertainty evokes the need for flexibility or softness where we accept satisfying some constraints to some degree. Moreover, when the relevance of some constraints depends on other factors, we should prioritize those constraints. Eventually, the modeled uncertainty, as well as the expressed soft and prioritized constraints induce preferences over the solutions set. Previous work employing mathematical uncertainty theories are either uncertainty-based frameworks or preference-based ones and the only attempt to handle both uncertainty and preferences is performed using two uncertainty theories under a commensurability assumption. In this paper, we propose a unifying CSP extension, labeled Belief CSP, that deals jointly with all these four concepts, i.e., uncertainty, soft and prioritized constraints and preferences over the solutions set, by exploiting the expressiveness of the belief function theory.
Lithology: A Landlab submodule for spatially variable rock properties

1 University of Colorado at Boulder, Department of Geological Sciences 2 University of Colorado at Boulder, Cooperative Institute for Research in Environmental Sciences 3 University of Colorado at Boulder, Community Surface Dynamics Modeling System Integration Facility 4 University of Colorado at Boulder, Institute for Arctic and Alpine Research 5 Tulane University, Department of Earth and Environmental Sciences
Feature noise tuning for resource efficient Bayesian Network Classifiers

Emerging portable applications require always-on sensing technologies to continuously monitor the environment and their user’s needs. Yet, the high power consumption that results from this continuous sensing often hampers these systems’ always-on functionality. In this paper we propose a hardware-aware Machine Learning scheme that exploits the devices’ ability to trade-oﬀ the quality of its sensors versus its power consumption. We introduce a technique that extends Bayesian Network classiﬁers with hardware description nodes that encode the probabilistic relation between sensory features and their degraded versions. We show how this allows to tune the hardware device’s power consumption versus inference accuracy trade-oﬀ space with ﬁne granularity, resulting in operating points that achieve signiﬁcant power savings at almost no accuracy loss. This is empirically shown on various Machine Learning benchmarking datasets.
Regularised maximum-likelihood inference of mixture of experts for regression and clustering

Variable selection is fundamental to high-dimensional statistical modeling, and is challenging in particular in unsupervised mod-eling, including mixture models. We propose a regularised maximum-likelihood inference of the Mixture of Experts model which is able to deal with potentially correlated features and encourages sparse models in a potentially high-dimensional scenarios. We develop a hybrid Expectation-Majorization-Maximization (EM/MM) algorithm for model fitting. Unlike state-of-the art regularised ML inference [1, 2], the proposed modeling doesn't require an approximate of the regularisation. The proposed algorithm allows to automatically obtain sparse solutions without thresholding, and includes coordinate descent updates avoiding matrix inversion. An experimental study shows the capability of the algorithm to retrieve sparse solutions and for model fitting in model-based clustering of regression data.
Prevalence and causes of blindness and vision impairment: magnitude, temporal trends and projections in South and Central Asia

Background To assess prevalence and causes of vision loss in Central and South Asia. Methods A systematic review of medical literature assessed the prevalence of blindness (presenting visual acuity<3/60 in the better eye), moderate and severe vision impairment (MSVI; presenting visual acuity <6/18 but ≥3/60) and mild vision impairment (MVI; presenting visual acuity <6/12 and ≥6/18) in Central and South Asia for 1990, 2010, 2015 and 2020. Results In Central and South Asia combined, age-standardised prevalences of blindness, MSVI and MVI in 2015 were for men and women aged 50+years, 3.72% (80% uncertainty interval (UI): 1.39–6.75) and 4.00% (80% UI: 1.41–7.39), 16.33% (80% UI: 8.55–25.47) and 17.65% (80% UI: 9.00–27.62), 11.70% (80% UI: 4.70–20.32) and 12.25% (80% UI:4.86–21.30), respectively, with a significant decrease in the study period for both gender. In South Asia in 2015, 11.76 million individuals (32.65% of the global blindness figure) were blind and 61.19 million individuals (28.3% of the global total) had MSVI. From 1990 to 2015, cataract (accounting for 36.58% of all cases with blindness in 2015) was the most common cause of blindness, followed by undercorrected refractive error (36.43%), glaucoma (5.81%), age-related macular degeneration (2.44%), corneal diseases (2.43%), diabetic retinopathy (0.16%) and trachoma (0.04%). For MSVI in South Asia 2015, most common causes were undercorrected refractive error (accounting for 66.39% of all cases with MSVI), followed by cataract (23.62%), age-related macular degeneration (1.31%) and glaucoma (1.09%). Conclusions One-third of the global blind resided in South Asia in 2015, although the age-standardised prevalence of blindness and MSVI decreased significantly between 1990 and 2015.
Cystathione β-Synthase Is Increased in Thyroid Malignancies

Background: Cystathione β-synthase (CBS) catalyzes the conversion of homocysteine and cysteine to hydrogen sulfide (H2S) and cystathione, via the trans-sulfuration pathway. CBS protein expression levels are increased in several different human malignancies, with increased protein expression correlating with parameters such as tumor stage, anaplasia, metastases, and chemotherapy resistance. Materials and Methods: This study employed tissue microarrays to examine CBS expression in benign thyroid tissue, thyroid oncocytomas, thyroid follicular adenomas, and in follicular, papillary, anaplastic, and medullary thyroid carcinomas. Results: CBS expression was increased in all thyroid carcinomas types compared to benign thyroid tissue, but not in thyroid follicular adenomas or oncocytomas. A similar pattern was observed for nicotinamide phosphoribosyltransferase (NAMPT) tissue microarray analysis comparing thyroid adenomas and follicular carcinomas. Conclusion: For the first time, we showed that an H2S-syntheszing enzyme plays a role in thyroid malignancies. Additionally, our data suggest that CBS and NAMPT immunohistochemistry may be useful in differentiating follicular adenomas from follicular carcinomas.
‘The disease isn't listening to the drug’: The socio-cultural context of antibiotic use for viral respiratory infections in rural Uganda

ABSTRACT To identify factors precipitating antibiotic misuse and discuss how to promote safe antibiotics use and curb antibiotic resistance. Antibiotic misuse is a significant problem globally, leading to increased antibiotic resistance. Many socio-cultural factors facilitate antibiotic misuse: patient and provider beliefs about antibiotics, inadequate regulation, poor health literacy, inadequate healthcare provider training, and sub-optimal diagnostic capability. This study investigates the influence of such factors on antibiotic use and community health in rural Uganda. Attention was paid to patient-provider dynamics, providers’ concerns, and the role of drug shops in the communities and how these situations exacerbate antibiotic misuse. Using a grounded ethnographic approach, interviews, focus groups, and observations were conducted over six weeks. Five salient themes emerged from data analysis. Based on the study results and a review of past literature on antibiotic resistance, there is need for improved health literacy and education, continued focus on efficiency and affordability in healthcare, and recognition of the role of stewardship and government in providing better healthcare. The problem of antibiotic misuse is multifactorial. Proposed solutions must target multiple contributing factors and must ultimately modify the culture and beliefs surrounding antibiotic use and encourage proper use. Such a multi-pronged approach would be most effective and would decrease rates of antibiotic resistance.
Canonicalization of Threshold Logic Representation and Its Applications

Threshold logic functions gain revived attention due to their connection to neural networks employed in deep learning. Despite prior endeavors in the characterization of threshold logic functions, to the best of our knowledge, the quest for a canonical representation of threshold logic functions in the form of their realizing linear inequalities remains open. In this paper we devise a procedure to canonicalize a threshold logic function such that two threshold logic functions are equivalent if and only if their canonicalized linear inequalities are the same. We further strengthen the canonicity to ensure that symmetric variables of a threshold logic function receive the same weight in the canonicalized linear inequality. The canonicalization procedure invokes $O(m)$ queries to a linear programming (resp. an integer linear programming) solver when a linear inequality solution with fractional (resp. integral) weight and threshold values is to be found, where $m$ is the number of symmetry groups of the given threshold logic function. The guaranteed canonicity allows direct application to the classification of NP (input negation, input permutation) and NPN (input negation, input permutation, output negation) equivalence of threshold logic functions. It may thus enable applications such as equivalence checking, Boolean matching, and library construction for threshold circuit synthesis.
Side-Channel Attack on a Protected RFID Card

Side-channel attack is a known security risk to smart cards, and there have been efforts by smart card manufacturers to incorporate side-channel attack countermeasures. In this paper, we study a widely used smart card that uses the 3DES algorithm. First, a platform is setup to extract the power consumption information from the electromagnetic wave. Based on the findings from the initial analysis, we determine that the card is equipped with a “head and tail protection” mechanism. Second, a chosen-plaintext power analysis with a complexity of 216 is proposed, which is designed to recover the second round key from the power leakage in the third round. Then, a slicing-collision-algebraic attack is presented, which decreases the complexity to 26 rapidly. The experiments show that after collecting 20000 power traces (in approximately 200 s), only $2^{6} \times 8$ key guesses and another 177 searches (about 300 seconds) are sufficient in recovering the 56-bit source keys of DES successfully. In other words, we demonstrate how the security of the 3DES card can be easily compromised, using side-channel attacks. Finally, we recommend that the head and tail protection should extend to the first and last four rounds, at the minimal, in order to be side-channel attack resilience.
A discourse-aware neural network-based text model for document-level text classification

Capturing semantics scattered across entire text is one of the important issues for Natural Language Processing (NLP) tasks. It would be particularly critical with long text embodying a flow of themes. This article proposes a new text modelling method that can handle thematic flows of text with Deep Neural Networks (DNNs) in such a way that discourse information and distributed representations of text are incorporate. Unlike previous DNN-based document models, the proposed model enables discourse-aware analysis of text and composition of sentence-level distributed representations guided by the discourse structure. More specifically, our method identifies Elementary Discourse Units (EDUs) and their discourse relations in a given document by applying Rhetorical Structure Theory (RST)-based discourse analysis. The result is fed into a tree-structured neural network that reflects the discourse information including the structure of the document and the discourse roles and relation types. We evaluate the document model for two document-level text classification tasks, sentiment analysis and sarcasm detection, with comparisons against the reference systems that also utilise discourse information. In addition, we conduct additional experiments to evaluate the impact of neural network types and adopted discourse factors on modelling documents vis-à-vis the two classification tasks. Furthermore, we investigate the effects of various learning methods, input units on the quality of the proposed discourse-aware document model.
CDTW-based classification for Parkinson's Disease diagnosis

This paper presents a new classification approach for Parkinson’s Disease (PD) diagnosis using Continuous Dynamic Time Warping (CDTW) technique and gait cycles data. These data are the vertical Ground Reaction Forces (vGRFs) recordings collected from eight force sensors placed in each shoe sole worn by each subject. The proposed approach exploits the principle of the repetition of gait cycle patterns to discriminate healthy subjects from PD subjects. The repetition of gait cycles is evaluated using the similarity of the time-series corresponding to stance phases estimated by applying the CDTW technique. The CDTW distances, extracted from gait cycles, are used as inputs of a binary classifier discriminating healthy subjects from PD subjects. Different classification methods are evaluated, including four supervised methods: K-Nearest Neighbours (K-NN), Decision Tree (DT), Random Forest (RF), and Support Vector Machines (SVM), and two unsupervised ones: Gaussian Mixture Model (GMM), and K-means. The proposed approach compares favorably with a classification based on standard features.
Optimized weighted feature voting technique for content-based image retrieval

Abstract. A content-based image retrieval system is proposed using an optimized weighted feature voting technique with multifeatures and multidistance measures. The proposed system consists of two phases: enrollment phase and querying phase. In enrollment phase, six features have been extracted from each enrollment image. These features are color histogram, edge histogram, edge direction histogram, hierarchical annular histogram, Gabor filter, and co-occurrence matrix. The extracted features are weighted, and feature weights are optimized using the four optimization techniques. Particle swarm optimization, ant lion optimizer, bird swarm algorithm, and whale optimization algorithm are applied and compared to decide the suitable technique. In querying phase, the same features are extracted from the query image and comparing these features to the extracted features from the database images through the matching measures. Three distance measures have been used and compared for the matching process: histogram intersection, Euclidean distance, and cosine distance. Based on these distances and the feature weights, the class of the query image is identified using the weighted feature voting mechanism. Finally, the related images are retrieved based on the feature that returned the maximum number of images in the identified class. For the validation and performance evaluations of the proposed system; Wang, Caltech101, and UW datasets are used. The experimental results show that the proposed method achieves improved precision in comparison with existing methods with 93% for Wang database, 92% for UW database, and 94% for Caltech101 database.
A two-level finite element method for the Allen–Cahn equation

ABSTRACT We consider the fully implicit treatment for the nonlinear term of the Allen–Cahn equation. To solve the nonlinear problem efficiently, the two-level scheme is employed. We obtain the discrete energy law of the fully implicit scheme and two-level scheme with finite element method. Also, the convergence of the two-level method is presented. Finally, some numerical experiments are provided to confirm the theoretical analysis.
Improvement of User Review Classification Using Keyword Expansion (S)

Application users can submit reviews for downloaded applications. Recently, developers have received more and more user reviews. However, it is still difficult to extract beneficial comments from a large amount of reviews. Latent Dirichlet Allocation (LDA) is a promising way of topic modeling, which classifies documents according to implicit multiple topics. However, there is a gap between the documents that the developer wants to extract and the document extracted by LDA. In this paper, we propose a method to extract documents of each category, such as requirements descriptions or bug reports, more accurately. Our method first decomposes the topics. Then, the method uses the keyword list which is a set of semantically similar words collected by word2vec, to integrate the decomposed topics. We apply our method to the applications user reviews in Apple Store and demonstrate the validity of it. Our approach can help application developers to extract beneficial information.
Productivity in an Era of Multi-Teaming

When and why do information dashboards enhance team productivity? We explore this question in the context of project teams whose members have to work across multiple teams at the same time. We expected multi-teaming, especially that involving a high variety in team members, would have a negative effect on team performance by impairing a team's transactive memory system (TMS), a critical form of shared cognition allowing members to keep track of who knows what. Further, we expected the use of an information dashboard with member skills could combat the harmful effects of high-variety multi-teaming. To test our hypotheses experimentally, we developed an online game (Knowledge Worker's Dilemma) that simulates the coordination decisions that professionals face in app development teams. Results show multi-teaming hurt team performance by reducing TMS; use of an information dashboard benefited high-variety teams and, surprisingly, hurt the performance of low-variety teams. Potential explanations and implications are discussed.
Identification of AGI1-A, a variant of Acinetobacter genomic island 1 (AGI1), in a French clinical isolate belonging to the Enterobacter cloacae complex

Objectives To characterize the structure of a variant of Acinetobacter genomic island 1 (AGI1) in the Enterobacter cloacae clinical isolate EclC2185 responsible for an 8 year outbreak in Dijon University Hospital. Methods The genome was sequenced (Miseq) and de novo assembled (Velvet). PCR and Sanger sequencing were performed to determine the sequence of the genomic island. Results The new variant of AGI1 named AGI1-A (41.7 kb), located at the 3' end of the chromosomal trmE, was detected in an E. cloacae complex isolate identified as Enterobacter hormaechei subsp. oharae. The backbone of AGI1-A lacked A008, part of A025, A026 and the resolvase gene. Its MDR region (19.7 kb) contained two integrons followed by a hybrid transposon Tn502/Tn5053. The former integron was a typical In4-type class 1 integron carrying aadA1 and the latter integron carrying dfrA1 did not belong to a class described to date. The nucleotide sequence intI-dfrA1 was surrounded by a 78 bp imperfect repeat sequence in inverse orientation. An external circular form of AGI1-A was detected, suggesting potential mobility. AGI1-A was also detected in 15 isolates from the outbreak selected at random. They belonged to the ST114 high-risk clone. Conclusions AGI1-A, a variant of AGI1 described in Acinetobacter baumannii, is the first genomic resistance island belonging to the Salmonella genomic island/Proteus genomic island/Acinetobacter genomic island family detected in E. cloacae. One might, therefore, fear for interspecies dissemination of genomic islands from this family.
A Gray-Box Method for Stability and Controller Parameter Estimation in HVDC-Connected Wind Farms Based on Nonparametric Impedance

Estimation of critical control parameters is a desirable tool feature for stability analysis and impedance shaping of high voltage dc (HVdc) connected wind farms. Accurate estimation of such parameters would be enabled by access to detailed models, which is not always the case in real wind farms. Industrial secrecy is one of the main factors hindering the access to such models. This paper proposes a gray-box method that, with basic assumptions about the control structure of the wind energy conversion system (WECS), can estimate the parameters of its controllers. The method is based on the measurements of frequency domain equivalent impedance combined with nonparametric impedance identification used in the solution of an inverse problem. The method makes possible to specify which part of the equivalent WECS impedance has a major impact on the stability of the system and according to this, reshape the impedance to enforce stability. Once the critical controller bandwidth is identified with this method, an instability mitigation technique is proposed based on reshaping the impedance by retuning the critical controllers of the interconnected converters. In order to avoid interaction between the HVdc rectifier and the WECS inverter, the controllers of both converters need to be retuned in such a way that the q-axis impedance magnitude of the HVdc system is kept lower than the q-axis impedance magnitude of the wind farm at the frequency of the phase-locked loop bandwidth. The results show that the method ensures the stability of the system by retuning only the critical controller parameters.
A Sub-1 μs Fast-Response Buck Converter With Adaptive and Frequency-Locked Controlled Techniques

This letter proposes a new architecture of a simple and sub-1μs fast-response voltage-mode buck converter using adaptive on-time and frequency-locked controlled techniques. The adaptive on-time-controller with frequency-locked loop is employed to keep the switching frequency (fsw) constant at 1 MHz to achieve fixed frequency. Therefore, the switching losses, and frequency variations can be reduced to improve the converter efficiency. In addition, the proposed buck converter does not include slope compensation circuit, which will simplify the system design and shorten the transient response of the converter without adding extra acceleration circuits. Moreover, the transient response of the converter can be accelerated by choosing the appropriate equivalent-series-resistance (ESR) value for the load capacitance. The chip is fabricated in TSMC 0.35 μm 2P4M process. The experimental results show that the maximum efficiency is 90.04% when the load current is 140 mA and output voltage is 1.8 V. The load transient response time is about 0.96 and 0.94 μs when the load current changes from 100 to 500 mA and from 500 to 100 mA, respectively.
On constructing 2-3 trees

We consider the task of constructing 2-3 trees. Given a sequence of elements we seek to build a 2-3 tree–in linear time–that contains the elements in symmetric order. We discuss three approaches: top-down, bottom-up, and incremental. The incremental approach is more flexible than the other two in that it allows us to interleave the construction work with other operations, for example, queries.