Miracle-dz/UniFAR-FTUs huggingface dataset:
A 12.5-ENOB 10-kS/s Redundant SAR ADC in 65-nm CMOS

This brief describes a 14-b 10-kS/s successive approximation register analog-to-digital converter (ADC) for biomedical applications. In order to achieve enhanced linearity, a uniform-geometry nonbinary-weighted capacitive digital-to-analog converter is implemented. In addition, a secondary-bit approach to dynamically shift decision levels for error correction is employed. To reduce the power consumption, the ADC also features a power-optimized comparator with bias control. Prototyped in a 65-nm CMOS process, the ADC consumes 1.98 μW and provides an effective number of bit (ENOB) of 12.5 bat 0.8 V while occupying an active area of 0.28 mm2.
Recursive NMF: Efficient label tree learning for large multi-class problems

Many object recognition or concept identification tasks require accurate detection of large number of classes. These applications present enormous challenges to traditional classification methods, which are mostly designed for solving problems with small number of classes. In this paper, we develop a method called recursive non-negative matrix factorization (RNMF) for building a hierarchical label tree over set of classes. The internal nodes of the tree employ linear classifiers to propagate a data instance to its corresponding leaf node, where one or more one class support vector machine (SVM) classifiers is applied to accurately predict its class. Our experiment results show that the proposed method achieves significant gain in test efficiency and comparable accuracy to some of the more expensive label tree learning methods.
Effect of Nanotube Film Thickness on the Performance of Nanotube-Silicon Hybrid Solar Cells

The results of measurements on solar cells made from randomly aligned thin films of single walled carbon nanotubes (SWCNTs) on n-type monocrystalline silicon are presented. The films are made by vacuum filtration from aqueous TritonX-100 suspensions of large diameter arc-discharge SWCNTs. The dependence of the solar cell performance on the thickness of the SWCNT film is shown in detail, as is the variation in performance due to doping of the SWCNT film with SOCl2.
Organic photovoltaic cells with all inkjet printed layers and freedom of form

Large volume production of organic photovoltaics by roll-to-roll compatible techniques is a field of intensive research. Inkjet printing is a well-known deposition technique in the graphical and textile industry, and has several advantages for the production of OPV as it is contactless and has economic materials use. More importantly, cells and modules can be directly patterned during R2R production and by digital fabrication of OPV altering the cell or module design does not require changes of hardware. This makes inkjet printing suitable for OPV with unconventional shapes, but also allows for customizable large scale production. Therefore, inkjet printing offers the flexibility required at this stage of technological and market development of OPV. We have been able, for the first time, to create fully inkjet printed OPVs with a performance of more than 75% of its reference prepared by spin coating and evaporation. Large areas were printed in single passes with an industrial printer head using non-halogenated solvents only. An inverted OPV stack of 6 layers was printed using 4 types of inks. ITO was replaced by an inkjet printed Ag current collecting grid combined with highly conducting PEDOT:PSS. In this contribution we will discuss the additive effect of printing multiple layers on the OPV performance. Furthermore, the performance of cells of different shapes and sizes (up to 6.5 cm2) will be discussed. This work confirms the potential of inkjet printing for OPV as well as printed electronics in general.
Detecting Threat E-mails using Bayesian Approach

Fraud and terrorism have a close connect in terms of the processes that enables and promote them. In the era of Internet, its various services that include Web, e-mail, social networks, blogs, instant messaging, chats, etc. are used in terrorism not only for communication but also for i) creation of ideology, ii) resource gathering, iii) recruitment, indoctrination and training, iv) creation of terror network, and v) information gathering. A major challenge for law enforcement and intelligence agencies is efficient and accurate gathering of relevant and growing volume of crime data. This paper reports on use of established Na\"ive Bayesian filter for classification of threat e-mails. Efficiency in filtering threat e-mail by use of three different Na\"ive Bayesian filter approaches i.e. single keywords, weighted multiple keywords and weighted multiple keywords with keyword context matching are evaluated on a threat e-mail corpus created by extracting data from sources that are very close to terrorism.
Modeling Expressive Wrinkles of Face For Animation

Vivid facial expressions contribute greatly to the visual realism of 3D face models. However, it is often tedious and expensive to model facial details which are authentic and real-time. In this paper, a simple and functional approach for modeling dynamic wrinkles of face is introduced. Started with structured facial mesh, we divide face region into some wrinkle subregions, and create some wrinkle lines with our algorithm in these subregions. Then, some key nodes are labeled on the facial mesh, these key nodes affect wrinkle lines if wrinkle lines locate the subregions that abut on the key nodes. With our algorithm, motion of wrinkle lines is produced by movement of key nodes. Consequently, wrinkle lines drive facial mesh to model expressive wrinkles. In this way, we create dynamic and functional expressive wrinkles of individuals available to date and demonstrate their use in facial animation.
Minimizing incomplete automata

We develop a O(m log n)-time and O(k + n + m)-space algorithm for minimizing incomplete deterministic automata, where n is the number of states, m the number of edges, and k the size of the alphabet. Minimization reduces to the partial Functional coarsest partition problem. Our algorithm is a slight variant of Hopcroft's algorithm for minimizing deterministic complete automata.
An Investigation of Deep Neural Network Architectures for Language Recognition in Indian Languages

In this paper, deep neural networks are investigated for language identiﬁcation in Indian languages. Deep neural networks (DNN) have been recently proposed for this task. However many architectural choices and training aspects that have been made while building such systems have not been studied care-fully. We perform several experiments on a dataset consisting of 12 Indian languages with a total training data of about 120 hours in evaluating the effect of such choices. While DNN based approach is inherently a frame based one, we propose an attention mechanism based DNN architecture for utterance level classiﬁcation there by efﬁciently making use of the context. Evaluation of models were performed on 30 hours of testing data with 2.5 hours for each language. In our results, we ﬁnd that deeper architectures outperform shal-lower counterparts. Also, DNN with attention mechanism out-performs the regular DNN models indicating the effectiveness of attention mechanism.
Deep Temporal Logistic Bag-of-features for Forecasting High Frequency Limit Order Book Time Series

Forecasting time series has several applications in various domains. The vast amount of data that are available nowadays provide the opportunity to use powerful deep learning approaches, but at the same time pose significant challenges of high-dimensionality, velocity and variety. In this paper, a novel logistic formulation of the well-known Bag-of-Features model is proposed to tackle these challenges. The proposed method is combined with deep convolutional feature extractors and is capable of accurately modeling the temporal behavior of time series, forming powerful forecasting models that can be trained in an end-to-end fashion. The proposed method was extensively evaluated using a large-scale financial time series dataset, that consists of more than 4 million limit orders, outperforming other competitive methods.
Evaluation of modified semisolid Rappaport Vassiliadis medium in comparison with conventional media in the isolation of Salmonella species from different samples

Objectives The goal of this study is to assess the performance of the modified semisolid Rappaport Vassiliadis media (MSRV) for detecting Salmonella spp. in comparison with conventional media especially xylose lysine desoxycholate agar (XLD agar) and Salmonella Shigella (SS) agar. Results The analysis showed that the overall Salmonella prevalence was 10% (18/180). Of the 18 Salmonella-positive samples, 16 (89%) were isolated from patients suffering from gastroenteritis, while one Salmonella strain (5.5%) was detected in the 45 stool samples of food handlers, and one Salmonella strain (5.5%) isolated from the 45 food samples. SS culture results comparable to serotyping resulted in sensitivity, specificity, positive predictive, negative predictive, accuracy values of 83.3, 98.2, 83.3, 98.2, and 96.7%, respectively, while XLD generated 83.3% sensitivity, 97.5% specificity, 79% positive predictive value, 98.1% negative predictive value, 96.1% accuracy. Statistically, the MSRV method resulted in higher values of sensitivity (94.4%), specificity (99.4%), positive predictive value (94.4%), and negative predictive value (99.4%), and accuracy (98.9%) than those obtained by the other two culture methods. Conclusions The current study showed that the overall diagnostic performance of MSRV for the detection of Salmonella spp. , including sensitivity, specificity, and accuracy, was significantly (P<0.05) greater than that of SS and XLD culturing methods. The incidence of Salmonella in diarrheal patients was significantly (P<0.05) higher than in food handlers and food samples. The occurrence of Salmonella was not significantly (P<0.05) related to the age and the sex of diarrheal patients. Salmonella typhimurium and Salmonella lamberhurst were the most prevalent serotypes in the tested samples.
Three-dimensional Hess screen test with binocular dual search coils in a three-field magnetic system.

PURPOSE To establish an objective Hess screen test that allows a simultaneous and binocular analysis of all three axes of eye rotation. METHODS In orthotropic and strabismic human subjects, both eyes were recorded with dual scleral search coils in a three-field magnetic system. Before mounting the search coil annuli on the eyes, the voltage offsets of each channel and the relative magnitudes of the three magnetic fields were determined. For calibration, subjects were only required to fix monocularly on a single reference target. During fixation of targets on the Hess screen by the uncovered eye, the three-dimensional eye position of both the occluded and the viewing eye was simultaneously measured. RESULTS For clinical interpretation, an easy to understand graphical description of the three-dimensional Hess screen test was developed. Positions of orthotropic and strabismic eyes tended to follow Listing's law, which in both eyes allowed the determination of the primary position, that is, the position of gaze from which pure horizontal and pure vertical movements do not lead to an ocular rotation about the line-of-sight. To a first approximation, the location of primary position is a result of the summation of the individual rotation axes of the six extraocular muscles and thus can be used to infer which muscle is paretic. CONCLUSIONS The three-dimensional Hess screen test with binocular dual search coils in a three-field magnetic system is an objective method to assess the ocular alignment in three dimensions with high precision. From these recordings, the clinician can relate deviations of primary position to specific eye muscle palsies.
Relative importance of viruses and bacteria in the etiology of pediatric diarrhea in Taiwan.

Of 80 children with acute gastroenteritis in Taiwan, 75 were tested for evidence of infection with a reovirus-like agent (RVLA) by electron microscopic examination of stools and/or by assays of complement-fixing and immunofluorescent antibody responses to the serologically related Nebraska calf diarrhea virus; 42 (56%) were positive. Of children whose stools contained the agent, 90% showed a fourfold or greater rise in titer of immunofluorescent antibodies, and 85% developed a fourfold or greater rise in titer of complement-fixing antibodies to the agent. Strains of Escherichia coli producing only heat-labile toxin (LT) were isolated from 7% of 57 ill children who had not received previous antibiotics and from 4% of 131 healthy control children (P = 0.49). Strains of E. coli producing only heat-stable toxin (ST) were isolated from five (9%) of 57 age-matched children in the group of 131 healthy controls tested. Invasive E. coli were not found. In addition, three of six children with bacterial and/ or serological evidence of an infection with LT-producing E. coli and four of five children infected with ST-producing E. coli had infections with RVLA. RVLA appeared to be the major cause of diarrheal illness in Taiwan during the summer of 1975, whereas toxigenic E. coli appeared to be responsible for a smaller proportion of pediatric enteritis.
Modeling, Computing and Classifying Topographic Area Features based on topologically non-structured Line Input Data

In this paper the conversion of a spaghetti into a topologically structured topographic base map is discussed. The first step, called structuring, comprises node computation, handling overshoots, undershoots and overlapping line segments. Node computation is a computational geometry problem driven by tolerances and a weighting scheme. The next step is placing additional lines by hand to close certain areas. Finally, area features are classified using a rule-based system. Specifically the rule-based classification of the areas has been implemented, tested and compared to human classification. In these experiments automatic classification leads to a speed-up of a factor 2 while maintaining a similar classification performance when compared with manual classification.
HTLV‐1 infection in blood donors from the Western Brazilian Amazon region: Seroprevalence and molecular study of viral isolates

To determine the seroprevalence of HTLV‐1 in Brazil, and to review the virus molecular epidemiology in this Amazon population (Rio Branco‐Acre), 219 blood donors were screened for HTLV‐1. Only one case of infection (0.46% seroprevalence) was detected during July 2004 screening at the Acre Hospital Foundation (FUNDACRE). Neighbor‐joining and Maximum Likelihood phylogenetic analyses of two (n = 2) complete LTR region sequences were performed with the PAUP* software. Since the HTLV‐1 envelope surface (gp46) and transmembrane (gp21) glycoproteins are important for virus fitness, three envelope glycoproteins sequences (n = 3) were analyzed using the Prosite tool to determinate potential protein sites. Phylogenetic analysis demonstrated that the new isolate described in this study, and the unpublished LTR strain described in a previous report belong to the Transcontinental subgroup of the Cosmopolitan subtype, inside the Latin American cluster. A similar result was obtained when submitting, to the Automated Genotyping System, three LTR partial sequences from a previous study of the seroprevalence of HTLV‐1 in the same Amazon population. In all analyzed env sequences, the potential protein site was found: two PKC phosphorylation sites at amino acid (aa) positions 310–312 and 342–344, one CK2 phosphorylation site at 194–197aa, three N‐glycosylation sites at 222–225aa, 244–247aa and 272–275aa, and a single N‐myristylation site at 327–338aa. In conclusion, potential protein sites described in HTLV‐1 gp46 and gp21 confirm the presence of conserved sites in the HTLV‐1 envelope proteins, likewise phylogenetic analysis suggests a possible recent introduction of the virus into North Brazil. J. Med. Virol. 80:1966–1971, 2008. © 2008 Wiley‐Liss, Inc.
Maternal Hypercholesterolemia Enhances Atherogenesis in Normocholesterolemic Rabbits, Which Is Inhibited by Antioxidant or Lipid-Lowering Intervention During Pregnancy: An Experimental Model of Atherogenic Mechanisms in Human Fetuses

Maternal hypercholesterolemia during pregnancy is associated with a marked increase in aortic fatty streak formation in human fetuses and faster progression of atherosclerosis during normocholesterolemic childhood. However, the mechanisms responsible are unknown, and the contribution of genetic differences is difficult to assess in humans. The goal of this study was to determine whether maternal hypercholesterolemia per se may cause enhanced fatty streak formation in offspring and whether interventions during pregnancy can reduce it. During pregnancy, 1 group of New Zealand White rabbits was fed control chow and 8 groups were fed hypercholesterolemic diets Chol 1 (yielding plasma cholesterol of 153 mg/dL) or Chol 2 (yielding 359 mg/dL) without or with cholestyramine, vitamin E, or both. Offspring (n=15 to 25 per group) were killed at birth. Maternal hypercholesterolemia enhanced mean lesion size in the aorta of their offspring at birth from 44±18×103 &mgr;m2 per section in controls to 85±26×103 in Chol 1 and 156±49×103 in Chol 2 groups (P <0.0001 for both). Cholestyramine or vitamin E treatment of mothers significantly reduced atherosclerosis at birth by up to 39% compared with controls on the same diet. Oxidized fatty acids and malondialdehyde in aortic atherosclerotic lesions and plasma were similarly affected by diets and treatment as atherosclerosis. Our results establish the causal role of hypercholesterolemia and peroxidation in fetal atherogenesis and demonstrate that both lipid-lowering and antioxidant interventions during pregnancy can reduce it. If it can be established that interventions in mothers also affect progression of lesions after birth, this may indicate a novel approach for the prevention of atherosclerosis.
Semantic Indexing and Typed Hyperlinking

In this paper, we describe linguistically sophisticated tools for the automatic annotation and navigation of on-line documents. Creation of these tools relies on research into finite-state technologies for the design and development of lexicallyintensive semantic indexing, shallow semantic understanding, and content abstraction techniques for texts. These tools utilize robust language processing techniques to generate multi-purpose data structures called LEXICAL WEBS, used in the system TEXTRACT, an automated semantic indexing program designed to parse, index, and hyperlink electronic documents.
Exploration of Antidiabetic Activity of Stephania japonica Leaf Extract in Alloxan-Induced Swiss Albino Diabetic Mice

Aims: Presently the medicinal world is rapidly turning more on the therapeutic health benefits of natural product and medicinal plants in the management of major crucial disease and their complications. Medicinal plant, Stephania japonica has been studied for exploring antidiabetic potentiality as an alternative source of medicine against the global threat of Diabetes mellitus (DM). Methods: The extraction of S. japonica leaf was carried out by acetone and ethanol. Phytochemical screening and quantitative analysis of S. japonica leaf extracts were evaluated through chemically forming characterized color formation and calibration method respectively, by using standard reference substances (ascorbic acid, gallic acid, and quercetin) to assess the probable compounds present in the extract. Anti-diabetic potentiality of highest phytochemicals containing two extracts were investigated in in vitro as a ⍺-amylase inhibitors and in vivo through alloxan-induced Swiss albino diabetes mice model. Results: Alkaloids, carbohydrates, steroids, flavonoids, resins, saponins, tannins and coumarins were present in the leaf extracts. The estimated amount of total phenolics, flavonoids, flavonols and proanthrocyanidins contents of acetone and ethanol extract were 92.12±0.64 and 56.54±1.05 mg of gallic acid equivalent (GAE)/gm of dry extract, 66.02±1.42 and 46.17±0.54 mg of catechin equivalent (CAE)/gm of dry extract, 7.05±0.108 and 5.26±0.083 mg of quercetin equivalent (QUE)/gm of dry extract, 35.19±0.67 and 9.55±1.11 mg CAE/gm of dry extract, respectively. In 3, 5-dinitrosalicylic acid method, acetone and ethanol extract showed α-amylase inhibition of 51.02% and 46.62%, respectively at the concentration of 1000 µg/mL whereas in starch iodine color assay, acetone and ethanol extract showed inhibition of 57.32% and 52.12%, respectively at the concentration of 800 µg/mL. In contrast, both of the leaf extracts significantly (p<0.05 to p<0.001) improved the lipid profile parameters, blood glucose level and serum hepatic marker proteins in alloxan-induced diabetic mice. Conclusion: The present study strongly concluded that S. japonica leaf extracts process potent antidiabetic potentiality that might be significance for the management of diabetes and its complications.
LOTUS-DB: an integrative and interactive database for Nelumbo nucifera study

Besides its important significance in plant taxonomy and phylogeny, sacred lotus (Nelumbo nucifera Gaertn.) might also hold the key to the secrets of aging, which attracts crescent attentions from researchers all over the world. The genetic or molecular studies on this species depend on its genome information. In 2013, two publications reported the sequencing of its full genome, based on which we constructed a database named as LOTUS-DB. It will provide comprehensive information on the annotation, gene function and expression for the sacred lotus. The information will facilitate users to efficiently query and browse genes, graphically visualize genome and download a variety of complex data information on genome DNA, coding sequence (CDS), transcripts or peptide sequences, promoters and markers. It will accelerate researches on gene cloning, functional identification of sacred lotus, and hence promote the studies on this species and plant genomics as well. Database URL: http://lotus-db.wbgcas. cn.
Superdirective Beamforming Based on the Krylov Matrix

Superdirective beamforming has attracted a significant amount of research interest in speech and audio applications, since it can maximize the directivity factor (DF) given an array geometry and, therefore, is efficient in dealing with signal acquisition in diffuse-like noise environments. However, this beamformer is very sensitive to sensor self-noise and mismatch among sensors, which considerably restricts its use in practical systems. This paper develops an approach to superdirective beamforming based on the Krylov matrix. We show that the columns of a proposed Krylov matrix, which span a chosen dimension of the whole space, are interesting beamformers; consequently, all different linear combinations of those columns lead to beamformers that have good properties. In particular, we develop the Krylov maximum white noise gain and Krylov maximum DF beamformers, which are obtained by maximizing the WNG and the DF, respectively. By properly choosing the dimension of the Krylov subspace, the developed beamformers that can make a compromise between reasonable values of the DF and white noise amplification. We also extend the basic idea to the design of the Krylov maximum front-to-back ratio, parametric superdirective, and parametric supercardioid beamformers.