Miracle-dz/UniFAR-FTUs huggingface dataset:
An IR Approach for Translating New Words from Nonparallel, Comparable Texts

In recent years, there is a phenomenal growth in the amount of online text material available from the greatest information repository known as the World Wide Web. Various traditional information retrieval(IR) techniques combined with natural language processing(NLP) techniques have been re-targeted to enable efficient access of the WWW--search engines, indexing, relevance feedback, query term and keyword weighting, document analysis, document classification, etc. Most of these techniques aim at efficient online search for information already on the Web. Meanwhile, the corpus linguistic community regards the W W W as a vast potential of corpus resources. It is now possible to download a large amount of texts with automatic tools when one needs to compute, for example, a list of synonyms; or download domain-specific monolingual texts by specifying a keyword to the search engine, and then use this text to extract domain-specific terms. It remains to be seen how we can also make use of the multilingual texts as NLP resources. In the years since the appearance of the first papers on using statistical models for bilingual lexicon compilation and machine translation(Brown et al., 1993; Brown et al., 1991; Gale and Church, 1993; Church, 1993; Simard et al., 1992), large amount of human effort and time has been invested in collecting parallel corpora of translated texts. Our goal is to alleviate this effort and enlarge the scope of corpus resources by looking into monolingual, comparable texts. This type of texts are known as nonparallel corpora. Such nonparallel, monolingual texts should be much more prevalent than parallel texts. However, previous a t tempts at using nonparallel corpora for terminology translation were constrained by the inadequate availability of same-domain, comparable texts in electronic form. The type of nonparallel texts obtained from the LDC or university libraries were often restricted, and were usually out-of-date as soon as they became available. For new word translation, the timeliness of corpus resources is a prerequisite, so is the continuous and automatic availability of nonparallel, comparable texts in electronic form. Data collection effort should not inhibit the actual translation effort. Fortunately, nowadays the World Wide Web provides us with a daily increase of fresh, up-to-date multilingual material, together with the archived versions, all easily downloadable by software tools running in the background. It is possible to specify the URL of the online site of a newspaper, and the start and end dates, and automatically download all the daily newspaper materials between those dates. In this paper, we describe a new method which combines IR and NLP techniques to extract new word translation from automatically downloaded English-Chinese nonparallel newspaper texts.
Identification and Characterization of Poorly Differentiated Invasive Carcinomas in a Mouse Model of Pancreatic Neuroendocrine Tumorigenesis

Pancreatic neuroendocrine tumors (PanNETs) are a relatively rare but clinically challenging tumor type. In particular, high grade, poorly-differentiated PanNETs have the worst patient prognosis, and the underlying mechanisms of disease are poorly understood. In this study we have identified and characterized a previously undescribed class of poorly differentiated PanNETs in the RIP1-Tag2 mouse model. We found that while the majority of tumors in the RIP1-Tag2 model are well-differentiated insulinomas, a subset of tumors had lost multiple markers of beta-cell differentiation and were highly invasive, leading us to term them poorly differentiated invasive carcinomas (PDICs). In addition, we found that these tumors exhibited a high mitotic index, resembling poorly differentiated (PD)-PanNETs in human patients. Interestingly, we identified expression of Id1, an inhibitor of DNA binding gene, and a regulator of differentiation, specifically in PDIC tumor cells by histological analysis. The identification of PDICs in this mouse model provides a unique opportunity to study the pathology and molecular characteristics of PD-PanNETs.
Controlled Natural Language Simplifying Language Use Workshop Programme

This standard is the first part of the series of ISO standards that are targeted at controlled natural language (CNL) in written languages. It focuses on the basic concepts and general principles of CNL that apply to languages in general. It will cover properties of CNL and CNL classification scheme. The subsequent parts will, however, focus on the issues specific to particular viewpoint and/or applications, such as particular CNLs, CNL interfaces, implementation of CNLs, and evaluation techniques for CNL.
Specialized DNA Polymerases, Cellular Survival, and the Genesis of Mutations

Cell death caused by arrested replication of damaged or structurally altered DNA can be avoided in prokaryotic and eukaryotic cells by multiple DNA polymerases that are specialized to bypass DNA damage. Some of these polymerases perform such translesion DNA synthesis of specific types of damage with high genetic fidelity. However, they exhibit greatly reduced fidelity when they operate on undamaged DNA or on DNA with lesions that are (apparently) not cognate substrates. The low fidelity of some of these specialized polymerases when copying undamaged DNA may be physiologically functional, including generating immunoglobulin diversity.
Tree Augmented Naïve possibilistic network classifier

Tree Augmented Naïve Bayes Network (TAN) classifier has shown excellent performance in Machine Learning and Data Mining in spite of the assumption of one- dependence of attributes. This paper proposes a new approach of classification under the possibilistic network (PN) framework with TAN, named tree augmented naïve possibilistic network classifier (TANPC), which combines the advantages of the PN and TAN. The classifier is built from a training set where instances can be expressed by imperfect attributes and classes. It is able to classify new instances those may have imperfect attributes.
Integrating parametric and non-parametric models for scene labeling

We adopt Convolutional Neural Networks (CNN) as our parametric model to learn discriminative features and classifiers for local patch classification. As visually similar pixels are indistinguishable from local context, we alleviate such ambiguity by introducing a global scene constraint. We estimate the global potential in a non-parametric framework. Furthermore, a large margin based CNN metric learning method is proposed for better global potential estimation. The final pixel class prediction is performed by integrating local and global beliefs. Even without any post-processing, we achieve state-of-the-art performance on SiftFlow and competitive results on Stanford Background benchmark.
Reversible Sessions Using Monitors

Much research has studied foundations for correct and reliable communication-centric systems. A salient approach to correctness uses session types to enforce structured communications; a recent approach to reliability uses reversible actions as a way of reacting to unanticipated events or failures. This note develops a simple observation: the machinery required to define asynchronous semantics and monitoring can also support reversible protocols. We propose a process framework of session communication in which monitors support reversibility. A key novelty in our approach are session types with present and past, which allow us to streamline the semantics of reversible actions.
The International Journal of Logistics Management Emerald Article: Supply Chain Risk Management: Present and Future Scope

Purpose – This paper examines supply chain risk management (SCRM) from a holistic systems thinking perspective by considering the different typologies that have evolved as a result of earlier research. The purpose of this paper is to identify important strategic changes in the field and to outline future requirements and research opportunities in SCRM. Design/methodology/approach – The systematic literature review (SLR) methodology employed by this research was used to evaluate and categorise a literature survey of quality articles published over a period of ten years (2000-2010). Additionally, the findings from the SLR have been strengthened through cross validation against results obtained from an associated text mining activity. Findings – The SLR methodology has provided a rich, unbiased and holistic picture of the advances in the field of SCRM. Consequently, important new research areas have been identified based on a multi-perspective descriptive and thematic data analysis. In addition, the analysis, based on evolved typologies, indicates a growth of SCRM from a nascent to a fairly established activity over the past decade. Practical implications – The systematic approach undertaken for the literature review will provide future researchers and managers with an insightful understanding of the scope of the SCRM field. Also, the literature review provides important clues on new research directions for SCRM through identification of gaps in current knowledge. Originality/value – The holistic approach to SCRM was found to be an important missing link in earlier literature surveys. The outcome of the SLR reported in this paper has provided critical insights into the present and future scope of the SCRM field. The identified research insights, gaps and future directions will encourage new research techniques, with a view to managing the risks in the globalized supply chain environment.
In Silico Genome Mismatch Scanning to Map Breast Cancer Genes in Extended Pedigrees

Abstract : This project aims to map breast cancer genes using dense single nucleotide polymorphism arrays in large extended pedigrees. Data has been collected using a 1,000,000 SNP genotype assay for 25 women affected by breast cancer in three high risk Utah pedigrees. Preliminary analysis of control data has been performed and significant progress has been made on the problem of modeling linkage disequilibrium between genetic loci at the density and scale required by this project. Programs to perform the modeling and analysis have been written and tested. Two papers describing the methodological developments have been published and a third describing initial analyses of control data is in preparation. The project is proceeding as planned and we expect to carry out the remainder in good time.
Thiopental dose requirements for induction of anaesthesia and subsequent endotracheal intubation in patients with complete spinal cord injuries

Dose requirements of thiopental depend on patient characteristics and infusion rate. We determined thiopental dose requirements for induction of anaesthesia, and the effects of remifentanil on cardiovascular and bispectral index (BIS) responses to tracheal intubation in spinal cord‐injured (SCI) patients undergoing general anaesthesia.
Viral, Quality, and Junk Videos on YouTube: Separating Content from Noise in an Information-Rich Environment

With the rise of web 2.0 there is an ever-expanding source of interesting media because of the proliferation of usergenerated content. However, mixed in with this is a large amount of noise that creates a proverbial “needle in the haystack” when searching for relevant content. Although there is hope that the rich network of interwoven metadata may contain enough structure to eventually help sift through this noise, currently many sites serve up only the “most popular” things. Identifying only the most popular items can be useful, but doing so fails to take into account the famous “long tail” behavior of the web—the notion that the collective effect of small, niche interests can outweigh the market share of the few blockbuster (i.e. most-popular) items—thus providing only content that has mass appeal and masking the interests of the idiosyncratic many. YouTube, for example, hosts over 40 million videos— enough content to keep one occupied for more than 200 years. Are there intelligent tools to search through this information-rich environment and identify interesting and relevant content? Is there a way to identify emerging trends or “hot topics” in addition to indexing the long tail for content that has real value?
Online Aggregation over Trees

Consider a distributed network with nodes arranged in a tree, and each node having a local value. We consider the problem of aggregating values (e.g., summing values) from all nodes to the requesting nodes in the presence of writes. The goal is to minimize the total number of messages exchanged. The key challenges are to define a notion of "acceptable" aggregate values, and to design algorithms with good performance that are guaranteed to produce such values. We formalize the acceptability of aggregate values in terms of certain consistency guarantees. We propose a lease-based aggregation mechanism, and evaluate algorithms based on this mechanism in terms of consistency and performance. With regard to consistency, we adapt the definitions of strict and causal consistency to apply to the aggregation problem. We show that any lease-based aggregation algorithm provides strict consistency in sequential executions, and causal consistency in concurrent executions. With regard to performance, we propose an online lease-based aggregation algorithm, and show that, for sequential executions, the algorithm is constant competitive against any offline algorithm that provides strict consistency. Our online lease-based aggregation algorithm is presented in the form of a fully distributed protocol, and the aforementioned consistency and performance results are formally established with respect to this protocol.
Optimum Design Method Of Multilayer Elements

A new design method for soft X-ray multilayers is presented. The method utilizes a recurrence formula for the complex amplitude reflectance of a multilayer and its graphical representation in the Gaussian plane. The design procedure gives a clear insight into the evolution of the complex amplitude reflectance of a multilayer at every stage of the deposition as the multilayer builds up. The thickness of each layer is optimized layer by layer so as to attain the highest possible reflectance for a given set of coating materials with the minimum number of layers. Some examples are given to demonstrate the usefulness of the present method in the design of multilayer elements for soft X-rays.
Ubiquitous and Robust Authentication Services for Ad Hoc Wireless Networks

Providing security support for large ad hoc wireless networks is challenging due to their unique characteristics, such as mobility, channel errors, dynamic node joins and leaves, and occasional node break-ins. In this report, we exploit these characteristics and present our design that supports ubiquitous security for mobile nodes, scales to network size, and is robust against adversary break-ins. In our design, we distribute the functionality of conventional security servers, specifically the authentication services, so that each individual node can potentially provide other nodes certification services. Centralized management is minimized and the nodes in the network collaboratively self-secure themselves. We propose a suit of fully distributed and localized protocols that facilitate practical deployment. Our protocols also feature communication efficiency to conserve the wireless channel bandwidth, and independency from both the underlying transport layer protocols and the network layer routing protocols.
Shaping a VLSI wire to minimize Elmore delay

Euler's differential equation of the calculus of variations is used to determine the shape of a VLSI wire that minimizes Elmore delay. The solution is given as a power series whose coefficients are formulas involving the load-end wire width, the load capacitance, the capacitance per unit area, and the capacitance per unit perimeter. In contrast to an optimal-width rectangular wire, the RC Elmore delay of the optimally tapered wire goes to zero as the driver resistance goes to zero. The optimal taper is immune, to first order, to process variations affecting wire width.
Body fat in neonates and young infants: validation of skinfold thickness versus dual-energy X-ray absorptiometry.

BACKGROUND There is an interest in noninvasive measurement of body fat in newborns and infants. Measurement of skinfold thickness (SFT) is a simple clinical method. OBJECTIVE We correlated fat mass (FM) values of neonates and infants predicted from SFT measurements and compared them with FM values measured by dual-energy X-ray absorptiometry (DXA), a validated in vivo method for determining body fat. DESIGN The weight, length, body composition (DXA measurement of FM and percentage of body fat), and SFT of 104 healthy term and preterm infants were measured at 0, 2, and 4 mo of age. RESULTS Mean (+/- SD) FM determined by DXA increased from 440 +/- 220 g at birth to 1310 +/- 450 g at 2 mo of age and to 2170 +/- 605 g at 4 mo of age. The percentage of body fat increased from 13.3% at birth to 24.5% and 31.2% at 2 and 4 mo of age, respectively. An equation was developed to calculate FM (in g) in newborns by using the sum of SFT measurements (in mm) and body length (l; in cm): FM = 68.2 x SigmaSFT((0.0162) x l) - 172.8 (R(2) = 0.948, P < 0.001). CONCLUSIONS With the use of statistical bootstrap analysis, the results provide an in vivo validation of SFT measurements against DXA for newborns and young infants. Body fat measurements by SFT correlate with FM values determined by DXA (R(2) = 0.936). Estimation of nutritional status is possible with errors (SD) of +/- 75, +/- 170, +/- 300, and +/- 380 g for infants with an FM <or= 500, 501-1000, 1001-2000, or > 2000 g, respectively.
Important human factors for systems development success: a user focus

Improving user satisfaction with information systems is an important consideration given the amount of resources organizations invest in systems development. Many factors are likely to play a role in enhancing the satisfaction users feel toward their systems, but probably none is as important as having knowledgeable, well-trained users participate in a meaningful way in the system development process. This study empirically tests the importance of user participation, user experience, user/developer communication, user training, user influence, and user conflict encountered during the system development process. A sample of 228 system users were used for a multivariate regression model testing the importance of these factors. The results indicate that user participation, user training, and user expertise can explain 61% of the variance in user satisfaction with their systems. The other factors showed no statistical significance in this study. Based on the results, managerial recommendations are proposed to people responsible for the systems development process in organizations.
Reversed optimality and predictive ecology: burrowing depth forecasts population change in a bivalve

Optimality reasoning from behavioural ecology can be used as a tool to infer how animals perceive their environment. Using optimality principles in a ‘reversed manner’ may enable ecologists to predict changes in population size before such changes actually happen. Here we show that a behavioural anti-predation trait (burrowing depth) of the marine bivalve Macoma balthica can be used as an indicator of the change in population size over the year to come. The per capita population growth rate between years t and t+1 correlated strongly with the proportion of individuals living in the dangerous top 4 cm layer of the sediment in year t: the more individuals in the top layer, the steeper the population decline. This is consistent with the prediction based on optimal foraging theory that animals with poor prospects should accept greater risks of predation. This study is among the first to document fitness forecasting in animals.
Parallel automata processor

Finite State Machines (FSM) are widely used computation models for many application domains. These embarrassingly sequential applications with irregular memory access patterns perform poorly on conventional von-Neumann architectures. The Micron Automata Processor (AP) is an in-situ memory-based computational architecture that accelerates non-deterministic finite automata (NFA) processing in hardware. However, each FSM on the AP is processed sequentially, limiting potential speedups. In this paper, we explore the FSM parallelization problem in the context of the AP. Extending classical parallelization techniques to NFAs executing on AP is non-trivial because of high state-transition tracking overheads and exponential computation complexity. We present the associated challenges and propose solutions that leverage both the unique properties of the NFAs (connected components, input symbol ranges, convergence, common parent states) and unique features in the AP (support for simultaneous transitions, low-overhead flow switching, state vector cache) to realize parallel NFA execution on the AP. We evaluate our techniques against several important benchmarks including NFAs used for network intrusion detection, malware detection, text processing, protein motif searching, DNA sequencing, and data analytics. Our proposed parallelization scheme demonstrates significant speedup (25.5× on average) compared to sequential execution on AP. Prior work has already shown that sequential execution on AP is at least an order of magnitude better than GPUs, multi-core processors and Xeon Phi accelerator.
THE "CAM-BRAIN" PROJECT The Evolutionary Engineering of a Billion Neuron Artificial Brain which Grows/Evolves at Electronic Speeds in a Cellular Automata Machine Part 1 : Fundamentals

This 2 part paper reports on the "CAM-Brain Project", which is an 8 year research project, beginning in 1993, at ATR labs in Kyoto, Japan, which intends to build/grow/evolve an artificial brain containing billions of artificial neurons. The essential idea behind CAM-Brain is to use cellular automata [Codd 1968] based neural networks which grow/evolve at electronic speeds inside cellular automata machines (CAMs) [Toffoli & Margolus 1987]. State of the art in these machines is MIT's "CAM8" ($40,000), which can update 200 million cellular automata (CA) cells per second [Toffoli & Margolus 1990]. Since the states of the CA cells can be stored cheaply in gigabytes of RAM, and given the updating speed, it becomes realistic to evolve millions of artificial neural circuits, thus revolutionizing the field of neural networks, and creating a new field, called "Brain Building".
Applied Soil Ecology

Removal of an invasive shrub (Chinese privet: Ligustrum sinense Lour) reduces exotic earthworm abundance and promotes recovery of native North American earthworms a b s t r a c t This study investigated the possibility of a facilitative relationship between Chinese privet (Ligustrum sinense) and exotic earthworms, in the southeastern region of the USA. Earthworms and selected soil properties were sampled five years after experimental removal of privet from flood plain forests of the Georgia Piedmont region. The earthworm communities and soil properties were compared between sites with privet, privet removal sites, and reference sites where privet had never established. Results showed that introduced European earthworms (Aporrectodea caliginosa, Lumbricus rubellus, and Octolasion tyr-taeum) were more prevalent under privet cover, and privet removal reduced their relative abundance (from >90% to ∼70%) in the community. Conversely, the relative abundance of native species (Diplocardia michaelsenii) increased fourfold with privet removal and was highest in reference sites. Soils under privet were characterized by significantly higher pH relative to reference plots and privet removal facilitated a significant reduction in pH. These results suggest that privet-mediated effects on soil pH may confer a competitive advantage to European lumbricid earthworms. Furthermore, removal of the invasive shrub appears to reverse the changes in soil pH, and may allow for recovery of native earthworm fauna. Published by Elsevier B.V.