Miracle-dz/UniFAR-FTUs huggingface dataset:
A Method for Estimating the Spinning-potential Yarn Number for Cotton Spun on the Rotor-spinning System

A test for determining the spinning potential of cotton fibers on the rotor-spinning system is proposed. It is based on the minimum twist required to rotor-spin a fixed yarn size. By using known mathematical relationships inherent in the physics of yarn formation, the minimum twist results are transformed into the theoretically finest yarn count that may be spun commercially. Implications of the effects of some key fiber properties un rotor-spinning performance are clarified.
fMRI SEGMENTATION USING ECHO STATE NEURAL NETWORK

This research work proposes a new intelligent segmentation technique for functional Magnetic Resonance Imaging (fMRI). It has been implemented using an Echostate Neural Network (ESN). Segmentation is an important process that helps in identifying objects of the image. Existing segmentation methods are not able to exactly segment the complicated profile of the fMRI accurately. Segmentation of every pixel in the fMRI correctly helps in proper location of tumor. The presence of noise and artifacts poses a challenging problem in proper segmentation. The proposed ESN is an estimation method with energy minimization. The estimation property helps in better segmentation of the complicated profile of the fMRI. The performance of the new segmentation method is found to be better with higher peak signal to noise ratio (PSNR) of 61 when compared to the PSNR of the existing back-propagation algorithm (BPA) segmentation method which is 57.
Factors Influencing Unsafe Behaviors and Accidents on Construction Sites: A Review

Objective. Construction is a hazardous occupation due to the unique nature of activities involved and the repetitiveness of several field behaviors. The aim of this methodological and theoretical review is to explore the empirical factors influencing unsafe behaviors and accidents on construction sites. Methods. In this work, results and findings from 56 related previous studies were investigated. These studies were categorized based on their design, type, methods of data collection, analytical methods, variables, and key findings. A qualitative content analysis procedure was used to extract variables, themes, and factors. In addition, all studies were reviewed to determine the quality rating and to evaluate the strength of provided evidence. Results. The content analysis identified 8 main categories: (a) society, (b) organization, (c) project management, (d) supervision, (e) contractor, (f) site condition, (g) work group, and (h) individual characteristics. The review highlighted the importance of more distal factors, e.g., society and organization, and project management, that may contribute to reducing the likelihood of unsafe behaviors and accidents through the promotion of site condition and individual features (as proximal factors). Conclusion. Further research is necessary to provide a better understanding of the links between unsafe behavior theories and empirical findings, challenge theoretical assumptions, develop new applied theories, and make stronger recommendations.
Robust exponential Little-Hopfield network storage

The Little-Hopfield network is an auto-associative computational model of neural memory storage and retrieval. This model is known to robustly store collections of randomly generated binary patterns as stable-states of the network dynamics. However, the number of binary memories so storable scales linearly in the number of neurons, and it has been a long-standing open problem whether robust exponential storage of binary patterns was possible in such a network memory model. In this note, we design simple families of Little-Hopfield networks that provably solve this problem affirmatively. As a byproduct, we produce a set of novel (nonlinear) binary codes with an efficient, highly parallelizable denoising mechanism.1
Improved Statistical Machine Translation Using Monolingually-Derived Paraphrases

Untranslated words still constitute a major problem for Statistical Machine Translation (SMT), and current SMT systems are limited by the quantity of parallel training texts. Augmenting the training data with paraphrases generated by pivoting through other languages alleviates this problem, especially for the so-called "low density" languages. But pivoting requires additional parallel texts. We address this problem by deriving paraphrases monolingually, using distributional semantic similarity measures, thus providing access to larger training resources, such as comparable and unrelated monolingual corpora. We present what is to our knowledge the first successful integration of a collocational approach to untranslated words with an end-to-end, state of the art SMT system demonstrating significant translation improvements in a low-resource setting.
Light Field Image Coding Using High-Order Intrablock Prediction

This paper proposes a two-stage high-order intrablock prediction method for light field image coding. This method exploits the spatial redundancy in lenslet light field images by predicting each image block, through a geometric transformation applied to a region of the causal encoded area. Light field images comprise an array of microimages that are related by complex geometric transformations that cannot be efficiently compensated by state-of-the-art image coding techniques, which are usually based on low-order translational prediction models. The two-stage nature of the proposed method allows us to choose the order of the prediction model most suitable for each block, ranging from pure translations to projective or bilinear transformations, optimized according to an appropriate rate-distortion criterion. The proposed higher order intrablock prediction approach was integrated into a high efficiency video coding (HEVC) codec and evaluated for both unfocused and focused light field camera models, using different resolutions and microlens arrays. Experimental results show consistent bitrate savings, which can go up to 12.62%, when compared to a lower order intrablock prediction solution and 49.82% when compared to HEVC still picture coding.
Towards Training Set Reduction for Bug Triage

Bug triage is an important step in the process of bug fixing. The goal of bug triage is to assign a new-coming bug to the correct potential developer. The existing bug triage approaches are based on machine learning algorithms, which build classifiers from the training sets of bug reports. In practice, these approaches suffer from the large-scale and low-quality training sets. In this paper, we propose the training set reduction with both feature selection and instance selection techniques for bug triage. We combine feature selection with instance selection to improve the accuracy of bug triage. The feature selection algorithm ¦Ö2-test, instance selection algorithm Iterative Case Filter, and their combinations are studied in this paper. We evaluate the training set reduction on the bug data of Eclipse. For the training set, 70% words and 50% bug reports are removed after the training set reduction. The experimental results show that the new and small training sets can provide better accuracy than the original one.
Testing decomposition of component specifications based on a rule for formal verification

This paper proposes a general technique for testing decomposition of component specifications based on rules for formal verification. Component specifications are expressed as pairs of two models: a context assumption and a component guarantee. Thereby they capture the contract-like nature between the component to be developed and the context in which it is supposed to work. The paper provides empirical evidence that A/G rules developed for formal methods are highly relevant as patterns for testing decomposition of component specifications. More explicitly, that testing the validity of decomposition based on A/G rules is valid, and moreover, that testing based on A/G rules is more efficient that a conventional approach for the same kind of test-quality.
Journal of Economic Behavior & Organization Information Processing and Decision-making: Evidence from the Brain Sciences and Implications for Economics

This article assesses the potential benefits of including findings from neurobiology in economic decision-making models. First, we emphasize that the evidence supports both 'expected utility-like' theory and 'Bayesian-like' information acquisition theory. Second, we explain that inferences and representations are subject to physiological limitations that affect decision making. We report in particular two 'mechanical' models developed in neu-roscience to represent neural data and choices. We then propose two economic models that incorporate physiological limitations into an expected utility framework. Interestingly, these two models provide foundations for those developed in neuroscience (which emerge endogenously in our framework) and provide further predictions that can be tested in principle. This allows us to discuss the benefits of bringing together evidence from neuroscience and economic modeling.
Relevance Feedback based on Constrained Clustering: FDU at TREC 09

Abstract : We introduce our participation of the TREC Relevance Feedback(RF) TRACK in 2009. The RF09 TRACK is focused on the explicit relevant feedback, where a few relevant and irrelevant documents are available to each query. Our system is implemented under the framework of probabilistic language model. We apply the constrained clustering on the top returned documents and extract the expanded words to reform the query. We also extract the named entities from the explicit relevant documents to expand the query. The experiment was conducted on the ClueWeb09 TREC Category B, which is a new and huge test collection for the TREC TRACKs. The evaluation result shows the performance of the constrained clustering.
A low latency generic accuracy configurable adder

High performance approximate adders typically comprise of multiple smaller sub-adders, carry prediction units and error correction units. In this paper, we present a low-latency generic accuracy configurable adder to support variable approximation modes. It provides a higher number of potential configurations compared to state-of-the-art, thus enabling a high degree of design flexibility and trade-off between performance and output quality. An error correction unit is integrated to provide accurate results for cases where high accuracy is required. Furthermore, an associated scheme for error probability estimation allows convenient comparison of different approximate adder configurations without requiring the need to numerically simulate the adder. Our experimental results validate the developed error model and also the lower latency of our generic accuracy configurable adder over state-of-the-art approximate adders. For functional verification and prototyping, we have used a Xilinx Virtex-6 FPGA. Our adder model and synthesizable RTL are made open-source.
Induction of c-Cbl contributes to anti-cancer effects of HDAC inhibitor in lung cancer

Here we found loss of c-Cbl, an E3 ligase, expression in non-small cell lung cancer (NSCLC) compared with its adjacent normal tissue in patient specimens. HDAC inhibition by WJ or knockdown of HDAC 1, HDAC2, HDAC3 or HDAC6 all induced c-Cbl. Ectopic expression of c-Cbl induced decreased EGFR, inhibited growth in NSCLC cells. Knockdown of EGFR inhibited NSCLC growth. Mutation of EGFR at Y1045 decreased WJ-induced growth inhibition as well as in vivo anti-cancer effect and EGFR degradation mediated by WJ. Time-lapse confocal analysis showed co-localization of c-Cbl and EGFR after WJ treatment. Furthermore, WJ inhibited lung tumor growth through c-Cbl induction in orthotopic and tail vein injected models. C-Cbl up-regulation induced by HDACi is a potential strategy for NSCLC treatment.
Threats and surprises behind IPv6 extension headers

The concept of Extension Headers, newly introduced with IPv6, is elusive and enables new types of threats in the Internet. Simply dropping all traffic containing any Extension Header — a current practice by operators-seemingly is an effective solution, but at the cost of possibly dropping legitimate traffic as well. To determine whether threats indeed occur, and evaluate the actual nature of the traffic, measurement solutions need to be adapted. By implementing these specific parsing capabilities in flow exporters and performing measurements on two different production networks, we show it is feasible to quantify the metrics directly related to these threats, and thus allow for monitoring and detection. Analysing the traffic that is hidden behind Extension Headers, we find mostly benign traffic that directly affects end-user QoE: simply dropping all traffic containing Extension Headers is thus a bad practice with more consequences than operators might be aware of.
Optimal demodulation of wavelength shifts in a fiber Bragg grating sensor using an adaptive two wave mixing photorefractive interferometer

Recent work by our research group on the dynamic demodulation of strain-induced wavelength shifts in fiber Bragg grating (FBG) sensors show that these sensors are suitable for the detection of high frequency ultrasonic waves produced by impact loading. A FBG sensor is incorporated into an optical detection system that uses a broadband tunable laser source in the C-band, a two wave-mixing photorefractive interferometer, and a high-speed photodetector. When an ultrasonic wave interacts with the FBG sensor, the wavelength of the reflected light in the fiber is dynamically shifted due to strain-induced perturbation of the index of refraction and/or the period of the grating in the fiber. The wavelength shift is converted into an intensity change by splitting the light into signal and pump beams and interfering the beams in an InP:Fe photorefractive crystal (PRC). The resulting intensity change is measured by a photodetector. The two-wave mixing (TWM) photorefractive interferometer allows for several FBG sensors to be wavelength multiplexed in one PRC and it also actively compensates for low frequency signal drifts associated with unwanted room vibrations and temperature excursions. In this work, we present preliminary experimental results on the detection of impact signals using a low power (1 mW) TWM PRC based demodulation system. The response time of the PRC is optimized by focusing the signal and pump beams into the crystal allowing for adaptivity of the demodulation system to quasi-static strains or temperature drifts. The TWM intensity gain of the system is optimized for efficient wavelength demodulation through resonant enhancement of the space charge electric field formed in the PRC. The low power demodulation system would facilitate significant reduction in the overall cost of the system.
Unsupervised acoustic and language model training with small amounts of labelled data

We measure the effects of a weak language model, estimated from as little as 100k words of text, on unsupervised acoustic model training and then explore the best method of using word confidences to estimate n-gram counts for unsupervised language model training. Even with 100k words of text and 10 hours of training data, unsupervised acoustic modeling is robust, with 50% of the gain recovered when compared to supervised training. For language model training, multiplying the word confidences together to get a weighted count produces the best reduction in WER by 2% over the baseline language model and 0.5% absolute over using unweighted transcripts. Oracle experiments show that a larger gain is possible, but better confidence estimation techniques are needed to identify correct n-grams.
Toward a quantum process algebra

Quantum computations operate in the quantum world. For their results to be useful in any way, there is an intrinsic necessity of cooperation and communication controlled by the classical world. As a consequence, full formal descriptions of algorithms making use of quantum principles must take into account both quantum and classical computing components and assemble them so that they communicate and cooperate. This paper aims at defining a high level language allowing the description of classical and quantum programming, and their cooperation. Since process algebras provide a framework to model cooperating computations and have well defined semantics, they have been chosen as a basis for this language. Starting with a classical process algebra, this paper explains how to transform it for including quantum computation. The result is a quantum process algebra with its operational semantics, which can be used to fully describe quantum algorithms in their classical context.
School Competition and the Performance of Public School Students

In this paper, I discuss the effect charter and private schools have on public school exam performance through increased competition in districts. Most specifications produce positive and significant effects of charter and private schools when using ordinary least squares. The nature of the charter school movement combined with location decisions of private schools, however, indicates that endogeneity is likely an issue. Via a fixed effects model, the results generally suggest a positive effect of charter schools in urban locations, and a negative effect in suburban locations. Private schools have negligible effects on public school exam performance. Via a Tobit model and control function approach, I find positive impacts from alternative schools across Massachusetts schools.
Cell type dependence and variability in the short‐term plasticity of EPSCs in identified mouse hippocampal interneurones

Synapses exhibit different short‐term plasticity patterns and this behaviour influences information processing in neuronal networks. We tested how the short‐term plasticity of excitatory postsynaptic currents (EPSCs) depends on the postsynaptic cell type, identified by axonal arborizations and molecular markers in the hippocampal CA1 area. Three distinct types of short‐term synaptic behaviour (facilitating, depressing and combined facilitating‐depressing) were defined by fitting a dynamic neurotransmission model to the data. Approximately 75 % of the oriens‐lacunosum‐moleculare (O‐LM) interneurones received facilitating EPSCs, but in three of 12 O‐LM cells EPSCs also showed significant depression. Over 90 % of the O‐LM cells were immunopositive for somatostatin and mGluR1α and all tested cells were decorated by strongly mGluR7a positive axon terminals. Responses in eight of 12 basket cells were described well with a model involving only depression, but the other cells displayed combined facilitating‐depressing EPSCs. No apparent difference was found between the plasticity of EPSCs in cholecystokinin‐ or parvalbumin‐containing basket cells. In oriens‐bistratified cells (O‐Bi), two of nine cells showed facilitating EPSCs, another two depressing, and the remaining five cells combined facilitating‐depressing EPSCs. Seven of 10 cells tested for somatostatin were immunopositive, but mGluR1α was detectable only in two of 11 tested cells. Furthermore, most O‐Bi cells projected to the CA3 area and the subiculum, as well as outside the hippocampal formation. Postsynaptic responses to action potentials recorded in vivo from a CA1 place cell were modelled, and revealed great differences between and within cell types. Our results demonstrate that the short‐term plasticity of EPSCs is cell type dependent, but with significant heterogeneity within all three interneurone populations.
Endosperm breakdown in Arabidopsis requires heterodimers of the basic helix-loop-helix proteins ZHOUPI and INDUCER OF CBP EXPRESSION 1

In Arabidopsis seeds, embryo growth is coordinated with endosperm breakdown. Mutants in the endosperm-specific gene ZHOUPI (ZOU), which encodes a unique basic helix-loop-helix (bHLH) transcription factor, have an abnormal endosperm that persists throughout seed development, significantly impeding embryo growth. Here we show that loss of function of the bHLH-encoding gene INDUCER OF CBP EXPRESSION 1 (ICE1) causes an identical endosperm persistence phenotype. We show that ZOU and ICE1 are co-expressed in the endosperm and interact in yeast via their bHLH domains. We show both genetically and in a heterologous plant system that, despite the fact that both ZOU and ICE1 can form homodimers in yeast, their role in endosperm breakdown requires their heterodimerization. Consistent with this conclusion, we confirm that ZOU and ICE1 regulate the expression of common target genes in the developing endosperm. Finally, we show that heterodimerization of ZOU and ICE1 is likely to be necessary for their binding to specific targets, rather than for their nuclear localization in the endosperm. By comparing our results with paradigms of bHLH function and evolution in animal systems we propose that the ZOU/ICE1 complex might have ancient origins, acquiring novel megagametophyte-specific functions in heterosporous land plants that were conserved in the angiosperm endosperm.
Novel single photon emitters based on color centers in diamond

Single photon emitters are core components of quantum technologies. In this work new family of emitters based on chromium impurities in diamond is presented. The emitters can be fabricated by ion implantation of chromium into bulk diamond or incorporated during the growth of diamond nanocrystals. The remarkable photo‐physical properties of these emitters include room temperature operation, narrow photoluminescence (PL) in the near infra red and ultra bright, fully polarized photon emission with count rate of 3.2×106 counts/s. These photo physical properties make the Cr centers ideal for quantum information processing, metrology, and cellular bio markers applications.
Chromosomal amplification of leucine-rich repeat kinase-2 (LRRK2) is required for oncogenic MET signaling in papillary renal and thyroid carcinomas

The receptor tyrosine kinase MET is frequently amplified in human tumors, resulting in high cell surface densities and constitutive activation even in the absence of growth factor stimulation by its endogenous ligand, hepatocyte growth factor (HGF). We sought to identify mechanisms of signaling crosstalk that promote MET activation by searching for kinases that are coordinately dysregulated with wild-type MET in human tumors. Our bioinformatic analysis identified leucine-rich repeat kinase-2 (LRRK2), which is amplified and overexpressed in papillary renal and thyroid carcinomas. Down-regulation of LRRK2 in cultured tumor cells compromises MET activation and selectively reduces downstream MET signaling to mTOR and STAT3. Loss of these critical mitogenic pathways induces cell cycle arrest and cell death due to loss of ATP production, indicating that MET and LRRK2 cooperate to promote efficient tumor cell growth and survival in these cancers.