Miracle-dz/UniFAR-FTUs huggingface dataset:
A one-pass decoder based on polymorphic linguistic context assignment

In this study, we examine how fast decoding of conversational speech with large vocabularies profits from efficient use of linguistic information, i.e. language models and grammars. Based on a re-entrant single pronunciation prefix tree, we use the concept of linguistic context polymorphism to allow an early incorporation of language model information. This approach allows us to use all available language model information in a one-pass decoder, using the same engine to decode with statistical n-gram language models as well as context free grammars or re-scoring of lattices in an efficient way. We compare this approach to our previous decoder, which needed three passes to incorporate all available information. The results on a very large vocabulary task show that the search can be speeded up by almost a factor of three, without introducing additional search errors.
Semi-non-negative matrix factorization using alternating direction method of multipliers for voice conversion

Voice conversion (VC) is being widely researched in the field of speech processing because of increased interest in using such processing in applications such as personalized Text-To-Speech systems. A VC method using Non-negative Matrix Factorization (NMF) has been researched because of its natural sounding voice, however, huge memory usage and high computational times have been reported as problems. We present in this paper a new VC method using Semi-Non-negative Matrix Factorization (Semi-NMF) using the Alternating Direction Method of Multipliers (ADMM) in order to tackle the problems associated with NMF-based VC. Dictionary learning using Semi-NMF can create a compact dictionary, and ADMM enables faster convergence than conventional Semi-NMF. Experimental results show that our proposed method is 76 times faster than conventional NMF, and its conversion quality is almost the same as that of the conventional method.
"Clustering of Dancelets": Towards Video Recommendation Based on Dance Styles

Dance is a special and important type of action, composed of abundant and various action elements. However, the recommendation of dance videos on the web are still not well studied. It is hard to realize it in the way of traditional methods using associated texts or static features of video content. In this paper, we study the problem focusing on extraction and representation of action information in dances. We propose to recommend dance videos based on the automatically discovered ``Dance Styles'', which play a significant role in characterizing different types of dances. To bridge the semantic gap of video content and mid-level concept, style, we take advantage of a mid-level action representation method, and extract representative patches as ``Dancelets'', a sort of intermediation between videos and the concepts. Furthermore, we propose to employ Motion Boundaries as saliency priors and sparsely extract patches containing more representative information to generate a set of dancelet candidates. Dancelets are then discovered by Normalized-cut method, which is superior in grouping visually similar patterns into the same clusters. For the fast and effective recommendation, a random forest-based index is built, and the ranking results are derived according to the matching results in all the leaf notes. Extensive experiments validated on the web dance videos demonstrate the effectiveness of the proposed methods for dance style discovery and video recommendation based on styles.
A Parallel System for Textual Inference

| This paper presents a possible solution for the text inference problem-extracting information unstated in a text, but implied. Text inference is central to natural language applications such as information extraction and dissemination , text understanding, summarization, and translation. Our solution takes advantage of a semantic English dictionary available in electronic form that provides the basis for the development of a large linguistic knowledge base. The inference algorithm consists of a set of highly parallel search methods that when applied to the knowledge base nd contexts in which sentences are interpreted. These contexts reveal information relevant to the text. Implementation, results and parallelism analysis are discussed. T HIS paper addresses the issue of parallelism in a class of problems that is largely unexplored, yet of growing importance. Text inference refers to the problem of extracting information that is not stated directly in a text, but is implied. This may be achieved by reasoning about a text by making logical judgments on the basis of circum-stantial evidence from a large knowledge base that contains knowledge about the world. A related, but much simpler problem is information retrieval where the goal is the recognition of facts, events and properties that are explicitly stated in the text. While current information retrieval systems that process millions of sentences per minute with an accuracy close to that of humans have been built 25], the process of large scale inference has not been automated yet. The major obstacles that need to be resolved are: (1) building knowledge bases large enough to capture world knowledge, (2) nd-ing a knowledge representation scheme good for common sense reasoning, and (3) developing inference methods and control mechanisms able to provide relevant inferences at speeds comparable to humans. In this paper we present a parallel inference system that operates on a very large linguistic knowledge base. The system is scalable both in size and accuracy and is highly parallel. The novelty of this work derives from our use of an extended linguistic knowledge base for English language called WordNet, and an inference algorithm that consists S. Harabagiu is with the and reference IEEECS Log Number D96261. of a set of parallel search procedures over the linguistic semantic network (i.e. the knowledge base). WordNet is being developed at Princeton by a group led by Miller 17]. Text inference is of great importance especially today when there are many newspapers, books and other …
Sequencing-Based Approaches Reveal Low Ambient Temperature-Responsive and Tissue-Specific MicroRNAs in Phalaenopsis Orchid

Plant small RNAs (smRNAs) are short, non-coding RNA molecules that mediate RNA silencing and regulate a group of genes involved in plant development and responses to environmental stimuli. Low temperature is necessary to initiate stalk development in the orchid Phalaenopsis aphrodite subsp. formosana. To identify smRNAs in Phalaenopsis responding to low temperatures, a smRNA profiling analysis using high-throughput sequencing technology was performed. Subsequent bioinformatic analysis was applied to categorize the miRNAs identified. A total of 37,533,509 smRNA reads yielded 11,129 independent orchid miRNA sequences, representing 329 known miRNA families identified in other plant species. Because the genomic resources available for Phalaenopsis are limited, a transcriptomic database was established using deep sequencing data sets to identify miRNAs precursors and their target transcripts. Comparing small RNAs and the transcriptomic database, 14 putative miRNA precursors of 10 miRNA families were identified, as were hundreds of putative targets. Comparing sequencing data and smRNA northern hybridization results identified miR156, miR162, miR528 and miR535 as low temperature-induced miRNAs. In addition, tissue-specific expression of these miRNAs was investigated. It was concluded that miR156 and miR172 may be components of a regulatory pathway mediating transition from the vegetative to the reproductive phase in Phalaenopsis. The smRNA and transcriptomic databases could be the foundations for further research aimed at elucidating the control of the flowering time in orchids.
A statistical atlas-based technique for automatic segmentation of the first Heschl's gyrus in human auditory cortex from MR images

We present an automatic method for the segmentation of the first transverse temporal gyrus of Heschl (HG), the morphological marker for primary auditory cortex in humans. The proposed technique utilizes a statistical anatomical atlas of the gyrus, generated from a set of training samples using principal component analysis. The training set consists of MRI data from 12 subjects with the corresponding Heschl's gyri manually labeled in each hemisphere (separate atlases were generated for each hemisphere). We used a leave-one-out approach to automatically segment Heschl's gyri in both hemispheres from the MR image data using generated atlases. We assessed the accuracy of this atlas-based technique by using it to segment the HG region from several test cases and finding the overlap between the segmented and labeled HG regions. Results demonstrated more than 75% and 83% accuracy in the extraction of the HG volumes in the left and right hemispheres, respectively. It is expected that the proposed tool can be adapted to extract other anatomical regions in the brain.
Integrating design and retail in the clothing value chain

Purpose – New product design is an established field in the literature. It is either analysed inside the firm; or when using a value chain perspective it is limited to the interactions between manufacturers and suppliers (in producer‐driven commodity chains). The current research adopts a downstream perspective, analysing the relationships between manufacturers and retailers in relation to the new product design process. Seeks to conduct research in the clothing industry; that has the specificity of being a buyer‐driven commodity chain where fashion makes design a key dimension for the success of a product.Design/methodology/approach – The research was empirical in nature, involving 50 semi‐structured face‐to‐face interviews in France, the USA and the UK at all points along the clothing value chain.Findings – In the clothing industry, the strategy of integrating design and retail has resulted in a more flexible design process and therefore, in an increased product performance. This strategy has been devel.
Speaker-gaze Modulates the Inter-personal Repetition of Hand Gestures

One study found that observers retained more information from hand gestures that speakers gazed at, possibly because speaker-gaze shifted observers' attention covertly. Speaker-gaze may thus modulate the role of gestures in communication. One hypothesized communicative function of gestures, and specifically of the inter-personal repetition of gestures, is to facilitate the process of creating common ground (grounding). Therefore, speaker-gaze may also influence the inter-personal repetition of gestures. In an experimental study, we found that participants were more likely to repeat another speaker's gestures if the original speaker gazed at the gestures. Moreover, speaker-gaze was a better predictor of this repetition than participants' own gaze. This supports the hypothesis that speakers' gaze at their gestures leads to covert attention shifts in observers, causing the gestures to be processed differently. Speaker-gaze could therefore be a valuable cue to the processing and production of gestures by artificial systems that interact with humans.
A Theoretical Analysis of the Investor Protection Regulations Argument for Global Listing of Stocks

Global listing of stocks has become a major topic in international capital markets. Many argue that the U.S. investor protection system discourages foreign firms from listing and raising capital in the U.S. However, there has recently been an increase in the number of foreign firms listing their shares in the U.S. This paper analyzes managers' decision as to which international market to cross-list their firms' shares on. It is shown that when markets differ in their regulatory environment, regulatory strictness may enable managers of highly profitable foreign firms to credibly convey their private information regarding their firms' future prospects. Through listing on the market with strict regulatory environment, those managers deliberately accept additional regulatory exposure, related to investor protection. That increased exposure, however, is more than offset by higher stock prices. The general setting of cross-listing is also shown to be applicable for global public offerings. Furthermore, in contrast to the claim that the strict regulatory environment deters firms from listing on that market, it is shown that a large differential between markets with respect to the regulatory strictness may, in fact, increase the number of firms listing on the market with stricter regulations. The implications of the model are consistent with the empirical evidence regarding global cross-listing.
Relevance Feedback Between Hypertext and Semantic Search

Relevance feedback is one method for creating a ‘virtuous cy cle’ as put by Baeza-Yates between semantics and search. Previ ous approaches to search have generally considered the Sema ntic Web and hypertext Web search to be entirely disparate, index i g and searching over different domains. While relevance feed back have traditionally improved information retrieval perfor mance, relevance feedback is normally used to improve rankings of a sin gle data-set. Our novel approach is to use relevance feedback fr om hypertext Web search to improve the retrieval of Semantic Web d ata. We also inspect whether relevance feedback from Semantic We b data can improve hypertext Web search results. In both cases , n evaluation based on certain kinds of informational queries (ab tract concepts, people, and places) selected from a query log and h uman judges show that relevance feedback works: relevance feedb ack from hypertext Web search can improve the retrieval of Seman tic Web data, and vice versa. We evaluate our work over a wide rang e of algorithms, and show it improves baseline performance on these queries for deployed systems as well, such as the Semantic Se ar h engine FALCON-S and the commercial Web search engine Yahoo! search.
Automatic information extraction from the Web

The Web is a valuable repository of information. However, its size and its lack of structure difficult the search and extraction of knowledge. In this paper, we propose an automatic and autonomous methodology to retrieve and represent information from the Web in a standard way for a desired domain. It is based on the intensive use of a publicly available search engine and the analysis of a large quantity of web resources for detecting the information relevance. Polysemy detection and aliases/synonyms discovering of the evaluated domain are also considered. Results can be very useful for easing the access of the user to the Web resources or allowing computer processing of the data.
Atomic force microscopy using voice coil actuators for vibration isolation

This paper presents a vibration isolation system integrated with the internal actuators of an atomic force microscope (AFM) to vertically move the probe. For the motion, voice coil actuators (Lorentz actuators) are guided by low-stiffness flexures. Due to the low stiffness, the vibrations from the floor to the probe are decoupled at high frequencies. To reject the residual vibrations, the AFM probe tracks the AFM sample by using a displacement sensor. By mechanical and control design specifically for Lorentz actuators, the vertical motion has a control bandwidth that is 24 times higher than the first mechanical resonance to reject vibrations. As a demonstration of the vibration isolation performance, pits and tracks of a CD-ROM are successfully imaged without an external vibration isolator.
Equivalent Characterization of Invariant Subspaces of H 2 and Applications to Optimal Sensitivity Problem

This paper gives some equivalent characterizations for invariant subspaces of H, when the underlying structure is specified by the so-called pseudorational transfer functions. This plays a fundamental role in computing the optimal sensitivity for a certain important class of infinite-dimensional systems, including delay systems. A closed formula, easier to compute than the well-known Zhou-Khargonekar formula, is given for optimal sensitivity for such systems. An example is given to illustrate the result.
S Particle Flurries Synoptic 3d Pulsatile Flow Visualization Visualization in Biomedical Research

opsis of all flow features simultaneously. Good examples of 2D synoptic visualizations are weather maps from both TV news reports and online Web pages. The human visual system is adept at finding patterns within larger contexts, and we hypothesize that synoptic visualization methods will help users find unexpected features more quickly and thus speed the understanding of complex 3D time-varying flows. Particle Flurries (PF) is our effort toward a synoptic visualization of complex pulsatile 3D flow. Our group’s ongoing study of the correlation between arterial blood flow and lesions as well as our research into the mechanics, dynamics, and evolution of animal flight (described in the “Visualization in Biomedical Research” sidebar) motivated this work. Our approach was inspired by videos of particles animating through a vessel. The video’s flat view raised questions about the precise behavior of the complex 3D flow, such as how particles move in the third dimension and what their movement is relative to the vessel wall. An immersive viewing environment promised to more effectively display complex 3D structures, thus our primary challenge was to design an effective 3D visualization based on the motivational 2D visualization style. PF tries to satisfy four goals:
Chronic ethanol exposure enhances the aggressiveness of breast cancer: the role of p38γ

Both epidemiological and experimental studies suggest that ethanol may enhance aggressiveness of breast cancer. We have previously demonstrated that short term exposure to ethanol (12–48 hours) increased migration/invasion in breast cancer cells overexpressing ErbB2, but not in breast cancer cells with low expression of ErbB2, such as MCF7, BT20 and T47D breast cancer cells. In this study, we showed that chronic ethanol exposure transformed breast cancer cells that were not responsive to short term ethanol treatment to a more aggressive phenotype. Chronic ethanol exposure (10 days - 2 months) at 100 (22 mM) or 200 mg/dl (44 mM) caused the scattering of MCF7, BT20 and T47D cell colonies in a 3-dimension culture system. Chronic ethanol exposure also increased colony formation in an anchorage-independent condition and stimulated cell invasion/migration. Chronic ethanol exposure increased cancer stem-like cell (CSC) population by more than 20 folds. Breast cancer cells exposed to ethanol in vitro displayed a much higher growth rate and metastasis in mice. Ethanol selectively activated p38γ MAPK and RhoC but not p38α/β in a concentration-dependent manner. SP-MCF7 cells, a derivative of MCF7 cells which compose mainly CSC expressed high levels of phosphorylated p38γ MAPK. Knocking-down p38γ MAPK blocked ethanol-induced RhoC activation, cell scattering, invasion/migration and ethanol-increased CSC population. Furthermore, knocking-down p38γ MAPK mitigated ethanol-induced tumor growth and metastasis in mice. These results suggest that chronic ethanol exposure can enhance the aggressiveness of breast cancer by activating p38γ MAPK/RhoC pathway.
Visually-Verifiable Textual Entailment: A Challenge Task for Combining Language and Vision

We propose visually-verifiable textual entailment as a challenge task for the emerging field of combining language and vision. This task is a variant of the wellstudied NLP task of recognizing textual entailment (Dagan et al., 2006) where every entailment judgment can be made purely by reasoning with visual knowledge. We believe that this task will spur innovation in the language and vision field while simultaneously producing inference algorithms that can be used in NLP.
Do Firms in Countries with Poor Protection of Investor Rights Hold More Cash?

Managers make different decisions in countries with poor protection of investor rights and poor financial development. One possible explanation is that shareholder-wealth maximizing managers face different tradeoffs in such countries (the tradeoff theory). Alternatively, firms in such countries are less likely to be managed for the benefit of shareholders because the poor protection of investor rights makes it easier for management and controlling shareholders to appropriate corporate resources for their own benefit (the agency costs theory). Holdings of liquid assets by firms across countries are consistent with Keynes' transaction and precautionary demand for money theories. Firms in countries with greater GDP per capita hold more cash as predicted. Controlling for economic development, firms in countries with more risk and with poor protection of investor rights hold more cash. The tradeoff theory and the agency costs theory can both explain holdings of liquid assets across countries. However, the fact that a dollar of cash is worth less than $0.65 to the minority shareholders of firms in such countries but worth approximately $1 in countries with good protection of investor rights and high financial development is only consistent with the agency costs theory.
Empirical Study on the Similarity and Difference between VCS-DSN and BTS-DSN

Most of the developers are collaborating freely as the team works through the Internet, for developing open source software projects without access restriction, playing different tasks including communications and coordination, making various social collaboration in the open source software projects (e.g., Bug/issue report, discussion, code revisions, etc.). All these activities can be recorded into software repositories like GitHub, and used to generate an implicit developer social network (DSN). In this paper, we conduct our empirical study using 50 open source software projects collected from GitHub and construct VCS-DSN and BTS-DSN to investigate the same collaboration between developers in the real world, and to find similarities and differences between them, in addition to the degree of similarity and diversity.
An Efficient Hardware Implementation of HOG Feature Extraction for Human Detection

In intelligent transportation systems, human detection is an important issue and has been widely used in many applications. Histograms of oriented gradients (HOG) are proven to be able to significantly outperform existing feature sets for human detection. In this paper, we present a low-cost high-speed hardware implementation for HOG feature extraction. The simulation shows that the proposed circuit can achieve 167 MHz with 153-K gate counts by using Taiwan Semiconductor Manufacturing Company 0.13-μm technology. Compared with the previous hardware architectures for HOG feature extraction, our circuit requires fewer hardware costs and achieves faster working speed.
Procrustean decomposition for orthogonal cascade detection

In this paper, our goal is to speed up a standard sliding window detector while maintaining detection accuracies. We do this by decomposing its weight template into multiple component detectors with no redundancy. Each component detector captures partial discriminative appearance, and they can be used in a cascade detection pipeline to aggressively reduce the size of search space. We formulate the decomposition problem as an orthogonal procrustes problem. We further approximate the component detector in the first cascade layer as separable 2D filters (i.e. a product between two vector filters). The running time of such component detector is thus reduced from quadratic to linear with the dimension lengths of the detector template. We conduct extensive experiments using our approach on two well-known object detection datasets: INRIA pedestrian detection dataset and PASCAL VOC 2007 detection dataset. We used HOG features and CNN features in our experiments. Our approach is almost "free lunch": without manipulating the feature representations, it makes the detection process several times faster.