Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of the Frequency Offset Effect on Zadoff–Chu Sequence Timing Performance

Zadoff-Chu (ZC) sequences have been used as synchronization sequences in modern wireless communication systems, replacing the conventional pseudorandom noise sequences due to their perfect autocorrelation properties. We first study the problem of ambiguity between a timing offset and a frequency offset, which arises when a ZC sequence is used as a synchronization signal. We then show how a frequency offset can impair the timing property of a ZC sequence, causing irreducible timing errors. An analytical framework, particularly the timing spectrum, is developed, which fully characterizes a ZC sequence's timing properties and its fundamental limitations as a time synchronization sequence in the presence of a frequency offset between the transmitter and the receiver. This analytical framework provides a powerful analytical tool for timing signal design and performance analysis of ZC sequences.
Disentangling Gaussians

The Gaussian mixture model (GMM) is one of the oldest and most widely-used statistical models. It is comprised of a weighted combination of heterogeneous Gaussian sources. As a simple one–dimensional example, consider measurements of heights of adults in a certain population, where the distribution of heights can be closely approximated as a mixture of two univariate Gaussians, one for males and one for females [3]. Can one recover the parameters of the Gaussians from unlabeled height measurements alone (with no gender information)? This paper focuses on the case where the mixture consists of a small but unknown number of Gaussians that may overlap—the combined density may even have a single peak, as in the height example, and the dimensionality may be high. Much of the previous work on this problem attempts to learn the parameters through clustering, and consequently needs to make a strong separation assumption on the components in the mixture. The primary contribution of our research is to avoid this assumption by instead basing our learning algorithm upon the algebraic structure of the mixture. Our algorithm succeeds even if the components overlap almost entirely, in which case accurate clustering is no longer possible. We give a simple notion of “condition number” of a GMM which characterizes its complexity up to polynomial factors. Generally speaking, the conclusion is that the statistical complexity and computational complexity of this general problem is in every way polynomial except for the dependence on the number of Gaussians, which is necessarily exponential. Statisticians have long known that from random samples from a GMM it is possible to identify the Gaussians in the limit—one can eventually recover to arbitrary precision each subpopulation’s mean, variance, and proportion, given sufficiently many examples [14]. However, their analysis provides no bounds on convergence rate—it might be exponentially slow even for two Gaussians in one dimension. Moreover, heuristics in widespread use, such as the EM algorithm, suf-
Specification framework for engineering adaptive web applications

The growing demand for data-driven Web applications has led to the need for a structured and controlled approach to the engineering of such applications. Both designers and developers need a framework that in all stages of the engineering process allows them to specify the relevant aspects of the application. This paper concentrates on Web applications that automatically generate hypermedia presentations for their output. Typically, these applications retrieve their data from a heterogeneous set of Web data sources, and they respond to a user's request for information by providing the user with a hypermedia presentation for the requested data. Many classes of Web-based information systems are of this nature. Because of this aspect of automated presentation generation, (the support of) the engineering process for these applications is far from trivial. The engineering becomes even more complicated when we include notions of adaptation. Here, we address both adaptation during the presentation generation for the sake of personalization, for example to reflect user preferences or platform used, as well as adaptation inside the generated presentation (generating adaptive hypermedia). The specification framework that we present in this paper can be used in an engineering process of an adaptive Web application. This framework called Hera is related to design methodologies for Web applications, and in particular to RMM (Relationship Management Methodology) for its coverage of aspects of hypermedia design. Characteristically, Hera distinguishes between the logical, functional specification of the data and its actual presentation. By separating (1) the conceptual (or semantical) description of data, (2) the navigational aspects of its hypermedia presentation and (3) the rendering of that presentation, the process of designing the application improves significantly. Using new emerging Web technologies like RDF, XML, and XSLT, we have implemented a prototype to illustrate the use of this specification framework.
A whole brain atlas with sub-parcellation of cortical gyri using resting fMRI

The new hybrid-BCI-DNI atlas is a high-resolution MPRAGE, single-subject atlas, constructed using both anatomical and functional information to guide the parcellation of the cerebral cortex. Anatomical labeling was performed manually on coronal single-slice images guided by sulcal and gyral landmarks to generate the original (non-hybrid) BCI-DNI atlas. Functional sub-parcellations of the gyral ROIs were then generated from 40 minimally preprocessed resting fMRI datasets from the HCP database. Gyral ROIs were transferred from the BCI-DNI atlas to the 40 subjects using the HCP grayordinate space as a reference. For each subject, each gyral ROI was subdivided using the fMRI data by applying spectral clustering to a similarity matrix computed from the fMRI time-series correlations between each vertex pair. The sub-parcellations were then transferred back to the original cortical mesh to create the subparcellated hBCI-DNI atlas with a total of 67 cortical regions per hemisphere. To assess the stability of the gyral subdivisons, a separate set of 60 HCP datasets were processed as follows: 1) coregistration of the structural scans to the hBCI-DNI atlas; 2) coregistration of the anatomical BCI-DNI atlas without functional subdivisions, followed by sub-parcellation of each subject’s resting fMRI data as described above. We then computed consistency between the anatomically-driven delineation of each gyral subdivision and that obtained per subject using individual fMRI data. The gyral sub-parcellations generated by atlas-based registration show variable but generally good overlap of the confidence intervals with the resting fMRI-based subdivisions. These consistency measures will provide a quantitative measure of reliability of each subdivision to users of the atlas.
Portable Arduino-Based LCR – Meter

An Arduino based portable LCR meter is designed, using the impedance converter AD5933 for measuring the reactance. The system combines an on-board frequency generator with a 12-bit, 1 MSPS, analog-to-digital converter (ADC). The built-in frequency generator allows an external complex impedance to be excited with a known frequency. The response signal from the impedance is sampled by the on-board ADC and a discrete Fourier transform (DFT) is processed by an on-board DSP engine. The DFT algorithm returns a real (R) and imaginary (I) dataword at each output frequency as the real and imaginary register contents, which can be read from the serial I2C interface. An Arduino controller-board maintains control of the AD5933 via the I2C-compliant serial interface protocol. Measuring of high value capacitances and low-value inductances with impedances in the range 1 Ω 10 Ω is enabled using additional circuitry with reference resistor added to the unknown reactive component and correction of the real part and the phase of the measured impedance.
Antagonist Properties of Conus parius Peptides on N-Methyl-D-Aspartate Receptors and Their Effects on CREB Signaling

Three members of a family of small neurotoxic peptides from the venom of Conus parius, conantokins (Con) Pr1, Pr2, and Pr3, function as antagonists of N-methyl-D-aspartate receptors (NMDAR). We report structural characterizations of these synthetic peptides, and also demonstrate their antagonistic properties toward ion flow through NMDAR ion channels in primary neurons. ConPr1 and ConPr2 displayed moderate increases in α-helicity after addition of Mg2+. Native apo-ConPr3 possessed an α-helical conformation, and the helicity increased only slightly on addition of Mg2+. Additionally, these peptides diminished NMDA/Gly-mediated currents and intracellular Ca2+ (iCa2+) influx in mature rat primary hippocampal neurons. Electrophysiological data showed that these peptides displayed slower antagonistic properties toward the NMDAR than conantokins from other species of cone snails, e.g., ConT and ConG. Furthermore, to demonstrate selectivity of the C. parius-derived conantokins towards specific NMDAR subunits, cortical neurons from GluN2A-/- and GluN2B-/- mice were utilized. Robust inhibition of NMDAR-mediated stimulation in GluN2A-/--derived mouse neurons, as compared to those isolated from GluN2B-/--mouse brains, was observed, suggesting a greater selectivity of these antagonists towards the GluN2B subunit. These C. parius conantokins mildly inhibited NMDAR-induced phosphorylation of CREB at Ser133, suggesting that the peptides modulated iCa2+ entry and, thereby, activation of CREB, a transcription factor that is required for maintaining long-term synaptic activity. Our data mechanistically show that while these peptides effectively antagonize NMDAR-directed current and iCa2+ influx, receptor-coupled CREB signaling is maintained. The consequence of sustained CREB signaling is improved neuronal plasticity and survival during neuropathologies.
Nonnucleoside Inhibitor of Measles Virus RNA-Dependent RNA Polymerase Complex Activity

ABSTRACT Paramyxoviruses comprise several major human pathogens. Although a live-attenuated vaccine protects against measles virus (MV), a member of the paramyxovirus family, the virus remains a principal cause of worldwide mortality and accounts for approximately 21 million cases and 300,000 to 400,000 deaths annually. The development of novel antivirals that allow improved case management of severe measles and silence viral outbreaks is thus highly desirable. We have previously described the development of novel MV fusion inhibitors. The potential for preexisting or emerging resistance in the field constitutes the rationale for the identification of additional MV inhibitors with a diverse target spectrum. Here, we report the development and implementation of a cell-based assay for high-throughput screening of MV antivirals, which has yielded several hit candidates. Following confirmation by secondary assays and chemical synthesis, the most potent hit was found to act as a target-specific inhibitor of MV replication with desirable drug-like properties. The compound proved highly active against multiple primary isolates of diverse MV genotypes currently circulating worldwide, showing active concentrations of 35 to 145 nM. Significantly, it does not interfere with viral entry and lacks cross-resistance with the MV fusion inhibitor class. Mechanistic characterization on a subinfection level revealed that the compound represents a first-in-class nonnucleoside inhibitor of MV RNA-dependent RNA polymerase complex activity. Singly or in combination with the fusion inhibitors, this novel compound class has high developmental potential as a potent therapeutic against MV and will likely further the mechanistic characterization of the viral polymerase complex.
Sentiment in foreign exchange markets: Hidden fundamentals by the back door or just noise?

Foreign exchange markets have to deal next to hard facts with lots of expectations and emo-tions. One of the major puzzles in international finance remains the â€œexchange rate discon-nect puzzleâ€ . Analyzing sentiment in foreign exchange markets, it appears in fact that senti-ment contains some forward looking information. Particularly due to the unknown economic relevance of sentiment in foreign exchange markets so far, we first analyze the relationship between fundamentals and sentiment in order to reveal underlying forces of the latter; sec-ond we accomplish our analysis by concentrating on popular expectation concepts and con-sidering threshold effects. Third, we evaluate sentiment by testing on accuracy and on for-ward looking elements of subsequent exchange rate returns
Design of a chaotic neural network by using chaotic nodes and NDRAM network

Recent developments in nonlinear dynamics and the theory of chaos have shown deterministic chaotic property of EEGs. Such evidences made the researchers try to take advantage of the chaotic behavior in artificial neural networks. According to the natural selection theory a good problem-solver should have two main properties: The ability of emerging various solutions for problem and existence of a rule (or intelligence) to guide this evolution and variety to become close to the goal. In this paper we used a chaotic node with logistic map to make the ability of emerging various solutions. In order to intelligently control the evolution of chaotic nodes we designed a rule by using NDRAM. The performance of proposed chaotic neural network is about 80% whereas the performance of NDRAM is about 40% in the same condition.
ENCODE: A Sourcebook of Epigenomes and Chromatin Language

Until recently, since the Human Genome Project, the general view has been that the majority of the human genome is composed of junk DNA and has little or no selective advantage to the organism. Now we know that this conclusion is an oversimplification. In April 2003, the National Human Genome Research Institute (NHGRI) launched an international research consortium called Encyclopedia of DNA Elements (ENCODE) to uncover non-coding functional elements in the human genome. The result of this project has identified a set of new DNA regulatory elements, based on novel relationships among chromatin accessibility, histone modifications, nucleosome positioning, DNA methylation, transcription, and the occupancy of sequence-specific factors. The project gives us new insights into the organization and regulation of the human genome and epigenome. Here, we sought to summarize particular aspects of the ENCODE project and highlight the features and data that have recently been released. At the end of this review, we have summarized a case study we conducted using the ENCODE epigenome data.
A 10 b 1 MS / sto-10 MS / s 0 . 11 um CMOS SAR ADC for Analog TV Applications

This work proposes a 10b 1MS/s-to-10MS/s 0.11um CMOS SAR ADC optimizing power consumption and chip area for analog TV (NTSC/PAL) applications. The proposed DAC employs a 2-step (6b-4b) split-capacitor array with the VCMbased switching method for high power efficiency and small chip area. Additionally, a range-scaling technique is employed for a rail-to-rail input signal swing. The comparator accuracy is improved by offset cancellation techniques in the first-stage preamp. The prototype ADC in a 0.11um CMOS technology demonstrates the measured DNL and INL within 1.07LSB and 1.66LSB, respectively. The ADC shows a maximum SNDR of 54.4dB and a maximum SFDR of 69.8dB at 10MS/s, respectively. The ADC with an active die area of 0.25mm consumes 2.3mW at a 1.2V and 10MS/s. Keywordsanalog-to-digital converter (ADC); range-scaling; split-capacitor array; VCM-based switching; analog TV
Content based 3D model retrieval: A survey

In recent decades, Computer aided retrieval, analysis of text, image data and graphical data technology are developing rapidly. However, the development in the field of 3D model retrieval is still in its infant stage. Thus, the related technologies, such as preprocess, normalization, extracting descriptor, index, and similarity measure still get a long way to explore. In this paper, we review recent various methods of content based 3D model retrieval, and propose a taxonomy for these methods. We also evaluate several requirements for 3D shape representation: (1) model file requirements, (2) necessity of pose normalization, (3) robustness, (4) sensitivity, (5) efficiency, (6) multiresolution support, (7) ability to perform partial matching, and (8) discrimination abilities. Finally, we discuss the advantages and disadvantages of various kinds of methods, and identify several issues in the research area of content based description and retrieval of 3D model.
Structure Functions and Minimal Path Sets

In this paper, we give and discuss a general multilinear expression of the structure function of an arbitrary semicoherent system in terms of its minimal path and cut sets. We also examine the link between the number of minimal path and cut sets consisting of one or two components and the concept of structure signature of the system.
Path tracking control of trailer-like mobile robot

The authors design a path tracking controller for a trailer-like mobile robot which has the following properties: (1) the stability of the control can be evaluated analytically; (2) the trailer-like mobile robot tracks the desired path even when it moves backward; and (3) the control varies according to the environment and/or the velocity of the robot without violating the stability of the control. <<ETX>>
Optimal Placement of Stream Processing Operators in the Fog

Elastic data stream processing enables applications to query and analyze streams of real time data. This is commonly facilitated by processing the flow of the data streams using a collection of stream processing operators which are placed in the cloud. However, the cloud follows a centralized approach which is prone to high latency delay. For avoiding this delay, we leverage on the fog computing paradigm which extends the cloud to the edge of the network.In order to design a stream processing solution for the fog, we first formulate an optimization problem for the placement of stream processing operators, which is tailored to fog computing environments. Then, we build a plugin (for stream processing frameworks) which solves the optimization problem periodically in order to support the dynamic resources of the fog. We evaluate this approach by performing experiments on an OpenStack testbed. The results show that our plugin reduces the response time and the cost by 31.5% and 8.8% respectively, compared to optimizing the placement of operators only upon initialization.
Evaluation of PCR for Diagnosis of Indian Kala-Azar and Assessment of Cure

ABSTRACT This study was done to evaluate PCR with Ld1 primers for the diagnosis of Indian visceral leishmaniasis (VL) and to assess its role in prediction of the disease outcome. The PCR assay was performed with DNA isolated from the peripheral blood of parasitologically confirmed cases of VL before the initiation of treatment, just after the end of treatment, and at 3 and 6 months of follow-up. The pretreatment PCR result was positive for 100 of 101 patients (sensitivity, 99%; confidence interval [CI], 94 to 100%). None of the 150 negative controls tested were PCR positive (specificity, 100%; CI, 96.8 to 100%). Of 60 patients who were treated at our center, 51 (85%; CI, 73 to 93%) became negative immediately after treatment and continued to be negative at 3 and 6 months of follow-up. At the 3-month follow-up, two of the remaining nine patients were PCR positive, making 58 (96.7%; CI, 87 to 100%) patients PCR negative. At the 6-month follow-up, all patients became PCR negative. One patient who was PCR negative immediately after the end of treatment relapsed 11 months later. This limited prospective study with VL patients suggests that the PCR assay is a highly sensitive and specific (99% and 100%, respectively) tool for the diagnosis of VL. In the majority of patients, it can identify a successful disease outcome; however, its translation into the field setting remains a major challenge.
CLIF–SOFA score and SIRS are independent prognostic factors in patients with hepatic encephalopathy due to alcoholic liver cirrhosis

AbstractHepatic encephalopathy (HE) is a complication associated with worst prognosis in decompensated liver cirrhosis (LC) patients. Previous studies have identified prognostic factors for HE, and recent studies reported an association between systemic inflammatory response syndrome (SIRS) and liver disease. This study aimed to identify prognostic factors for 30-day mortality in alcoholic LC patients with HE who visited the emergency department (ED). This was a retrospective study of alcoholic LC patients with HE from January 1, 2010, to April 30, 2015. The baseline characteristics, complications of portal hypertension, laboratory values, Child–Pugh class, Model for End-stage Liver Disease (MELD) score, chronic liver failure-sequential organ failure assessment (CLIF–SOFA) score, and SIRS criteria were assessed. The presence of 2 or more SIRS criteria was considered SIRS. The primary outcomes were 30-day mortality and prognostic factors for patients with HE visiting the ED.In total, 105 patients who met the inclusion criteria were analyzed. Overall, the 30-day mortality rate was 6.7% (7 patients). Significant variables were hepatorenal syndrome, international normalized ratio, white blood cell count, total bilirubin level, MELD score CLIF–SOFA score, and SIRS in univariate analysis. CLIF–SOFA score and SIRS were the significant factors in the multivariate analysis (hazard ratio 5.56, 15.98; 95% confidence interval 1.18–26.18, 1.58–161.37; P = 0.03, P = 0.02). The mortality rates differed according to the CLIF–SOFA score (P < 0.01). The CLIF–SOFA score and SIRS in alcoholic LC patients with HE visiting the ED are independent predictors of 30-day mortality.
Usability benchmarking case study: media downloads via mobile phones in the US

In spring 2004 Usable Products Company set to study the shopping, purchase, and assignment process for mobile media (wallpaper, ring tones and games for mobile phones) in the US market. A large scale (200 initial interviews followed by an additional 60) study covering 5 major US carriers and 13 handsets was conducted between 2004 and 2005 to benchmark the usability of mobile media purchase process. In this paper, we describe the goals, procedures, timeline, and results of this project with particular focus on what changes were necessary to move from a formative to a normative usability testing process.
Credit Card Fraud Detection Using Meta-Learning: Issues and Initial Results 1

We describe initial experiments using meta-learning techniques to learn models of fraudulent credit card transactions. Our experiments reported here are the first step towards a better understanding of the advantages and limitations of current meta-learning strategies on real-world data. We argue that, for the fraud detection domain, fraud catching rate (True Positive rate) and false alarm rate (False Positive rate) are better metrics than the overall accuracy when evaluating the learned fraud classifiers. We show that given a skewed distribution in the original data, artificially more balanced training data leads to better classifiers. We demonstrate how meta-learning can be used to combine different classifiers and maintain, and in some cases, improve the performance of the best classifier.