snu-aidas/HVMap-dataset huggingface dataset:
Functorial Boxes in String Diagrams

String diagrams were introduced by Roger Penrose as a handy notation to manipulate morphisms in a monoidal category. In principle, this graphical notation should encompass the various pictorial systems introduced in proof-theory (like Jean-Yves Girard’s proof-nets) and in concurrency theory (like Robin Milner’s bigraphs). This is not the case however, at least because string diagrams do not accomodate boxes — a key ingredient in these pictorial systems. In this short tutorial, based on our accidental rediscovery of an idea by Robin Cockett and Robert Seely, we explain how string diagrams may be extended with a notion of functorial box to depict a functor separating an inside world (its source category) from an outside world (its target category). We expose two elementary applications of the notation: first, we characterize graphically when a faithful balanced monoidal functor F : C −→ D transports a trace operator from the category D to the category C, and we then exploit this to construct well-behaved fixpoint operators in cartesian closed categories generated by models of linear logic; second, we explain how the categorical semantics of linear logic induces that the exponential box of proof-nets decomposes as two enshrined functorial boxes. ∗Invited paper at the conference Computer Science Logic 2006 in Szeged, Hungary. To appear in the proceedings of the conference. c © Springer Verlag. This research was partially supported by the ANR Project INVAL “Invariants algébriques des systèmes informatiques”.
Fascia lliaca block for analgesia after hip arthroplasty: a randomized double-blind, placebo-controlled trial.

BACKGROUND AND OBJECTIVES
Fascia iliaca block (FIB) is often used to treat pain after total hip arthroplasty (THA), despite a lack of randomized trials to evaluate its efficacy for this indication. The objective of this study was to assess the analgesic benefit of FIB after THA. Our primary hypothesis was administration of FIB decreases the intensity of postoperative pain (numeric rating scale [NRS-11] score) compared with sham block (SB) in patients after THA.


METHODS
After institutional review board approval and informed consent, 32 eligible patients having THA were recruited. In the postoperative care unit, although all patients received intravenous morphine sulfate patient-controlled analgesia, patients reporting pain of 3 or greater on the NRS-11 scale were randomized to receive ultrasound-guided fascia iliaca (30 mL 0.5% ropivacaine) or SB (30 mL 0.9% NaCl) using identical technique, below fascia iliaca. The primary outcome was pain intensity (NRS-11) after FIB.


RESULTS
Thirty-two patients (16 in each group) completed the study; all patients received an FIB. There was no difference in pain intensity (NRS-11 = 5.0 ± 0.6 vs 4.7 ± 0.6, respectively) after FIB versus SB or in opioid consumption (8.97 ± 1.6 vs 5.7 ± 1.6 mg morphine, respectively) between the groups at 1 hour. The morphine consumption after 24 hours was similar in both groups (49.0 ± 29.9 vs 50.4 ± 34.5 mg, P = 0.88, respectively).


CONCLUSIONS
The evidence in these data suggests that the difference in average pain intensity after FIB versus SB was not significant (95% confidence interval, -2.2-1.4 NRS units).
Surveys on attitudes towards legalisation of euthanasia: importance of question phrasing.

AIM
To explore whether the phrasing of the questions and the response alternatives would influence the answers to questions about legalisation of euthanasia.


METHODS
Results were compared from two different surveys in populations with similar characteristics. The alternatives "positive", "negative", and "don't know" (first questionnaire) were replaced with an explanatory text, "no legal sanction", four types of legal sanctions, and no possibility to answer "don't know" (second questionnaire). Four undergraduate student groups (engineering, law, medicine, and nursing) answered.


RESULTS
In the first questionnaire (n = 684) 43% accepted euthanasia (range 28-50%), 14% (8-33%) did not, and 43% (39-59%) answered "don't know". Two per cent of the respondents declined to answer. In comparison with previous surveys on attitudes to euthanasia the proportion of "don't know" was large. The results of the second questionnaire (n = 639), showed that 38% favoured "no legal prosecution" (26-50%). However, 62% (50-74%) opted for different kinds of legal sanctions, and two of four groups expressed significantly different views in the two surveys. A proportion of 10% declined to answer the second questionnaire.


CONCLUSION
An introduction of an explanatory text and a wider range of response alternatives produced differences between the results of the two surveys conducted.
Overhead line fault detection using GSM technology

In this paper, a novel technique for the fault detection, classification & protection of transmission lines is proposed. The proposed system uses different protective equipment's, voltage sense section, microcontroller section, LED display section & GSM (global system for mobile communication) module. The faults like all series (1LO, 2LO, 3LO etc.) & shunt faults (L-G, L-L, L-L-G, L-L-L, L-L-L-G) get detected & classified according to characteristics condition of current & voltage at the occurrence of fault in the three phase overhead lines. The sensed signals are given to microcontroller for detection & classification of faults. Also wireless mobile communication technique i.e. GSM is used simultaneously to send message to responsible person on mobile. Type of fault gets displayed on fault display section. Simultaneously fault gets interrupted using protective devices. (fuse, circuit breakers etc.)
Complete 3D Scene Parsing from an RGBD Image

One major goal of vision is to infer physical models of objects, surfaces, and their layout from sensors. In this paper, we aim to interpret indoor scenes from one RGBD image. Our representation encodes the layout of orthogonal walls and the extent of objects, modeled with CAD-like 3D shapes. We parse both the visible and occluded portions of the scene and all observable objects, producing a complete 3D parse. Such a scene interpretation is useful for robotics and visual reasoning, but difficult to produce due to the well-known challenge of segmentation, the high degree of occlusion, and the diversity of objects in indoor scenes. We take a data-driven approach, generating sets of potential object regions, matching to regions in training images, and transferring and aligning associated 3D models while encouraging fit to observations and spatial consistency. We use support inference to aid interpretation and propose a retrieval scheme that uses convolutional neural networks to classify regions and retrieve objects with similar shapes. We demonstrate the performance of our method on our newly annotated NYUd v2 dataset (Silberman et al., in: Computer vision-ECCV, 2012, pp 746–760, 2012) with detailed 3D shapes.
Reducing Buyer Search Costs : Implications for Electronic Marketplaces

Information systems can serve as intermediaries between the buyers and the sellers in a market, creating an “electronic marketplace” that lowers the buyers' cost to acquire information about seller prices and product offerings. As a result, electronic marketplaces reduce the inefficiencies caused by buyer search costs, in the process reducing the ability of sellers to extract monopolistic profits while increasing the ability of markets to optimally allocate productive resources. This article models the role of buyer search costs in markets with differentiated product offerings. The impact of reducing these search costs is analyzed in the context of an electronic marketplace, and the allocational efficiencies such a reduction can bring to a differentiated market are formalized. The resulting implications for the incentives of buyers, sellers and independent intermediaries to invest in electronic marketplaces are explored. Finally, the possibility to separate price information from product attribute information is introduced, and the implications of designing markets promoting competition along each of these dimensions are discussed. Copyright © 1997 by The Institute of Management Sciences
School-based interventions for aggressive and disruptive behavior: update of a meta-analysis.

BACKGROUND
Research about the effectiveness of school-based psychosocial prevention programs for reducing aggressive and disruptive behavior was synthesized using meta-analysis. This work updated previous work by the authors and further investigated which program and student characteristics were associated with the most positive outcomes.


METHODS
Two hundred forty-nine experimental and quasi-experimental studies of school-based programs with outcomes representing aggressive and/or disruptive behavior were obtained. Effect sizes and study characteristics were coded from these studies and analyzed.


RESULTS
Positive overall intervention effects were found on aggressive and disruptive behavior and other relevant outcomes. The most common and most effective approaches were universal programs and targeted programs for selected/indicated children. The mean effect sizes for these types of programs represent a decrease in aggressive/disruptive behavior that is likely to be of practical significance to schools. Multicomponent comprehensive programs did not show significant effects and those for special schools or classrooms were marginal. Different treatment modalities (e.g., behavioral, cognitive, social skills) produced largely similar effects. Effects were larger for better-implemented programs and those involving students at higher risk for aggressive behavior.


CONCLUSIONS
Schools seeking prevention programs may choose from a range of effective programs with some confidence that whatever they pick will be effective. Without the researcher involvement that characterizes the great majority of programs in this meta-analysis, schools might be well-advised to give priority to those that will be easiest to implement well in their settings.
Zero-Knowledge Proof of Decryption for FHE Ciphertexts

Zero-knowledge proofs of knowledge and fully-homomorphic encryption are two areas that have seen considerable advances in recent years, and these two techniques are used in conjunction in the context of verifiable decryption. Existing solutions for verifiable decryption are aimed at the batch setting, however there are many applications in which there will only be one ciphertext that requires a proof of decryption. The purpose of this paper is to provide a zero-knowledge proof of correct decryption on an FHE ciphertext, which for instance could hold the result of a cryptographic election. We give two main contributions. Firstly, we present a bootstrapping-like protocol to switch from one FHE scheme to another. The first scheme has efficient homomorphic capabilities; the second admits a simple zero-knowledge protocol. To illustrate this, we use the Brakerski et al. (ITCS, 2012) scheme for the former, and Gentry’s original scheme (STOC, 2009) for the latter. Secondly, we present a simple one-shot zero-knowledge protocol for verifiable decryption using Gentry’s original FHE scheme.
Role of social media in online travel information search

Social media are playing an increasingly important role as information sources for travelers. The goal of this study is to investigate the extent to which social media appear in search engine results in the context of travel-related searches. The study employed a research design that simulates a traveler’s use of a search engine for travel planning by using a set of pre-defined keywords in combination with nine U.S. tourist destination names. The analysis of the search results showed that social media constitute a substantial part of the search results, indicating that search engines likely direct travelers to social media sites. This study confirms the growing importance of social media in the online tourism domain. It also provides evidence for challenges faced by traditional providers of travel-related information. Implications for tourism marketers in terms of online marketing strategies are discussed. 2009 Elsevier Ltd. All rights reserved.
Multi-modal Semantic Place Classification

The ability to represent knowledge about space and its position therein is crucial for a mobile robot. To this end, topological and semantic descriptions are gaining popularity for augmenting purely metric space representations. In this paper we present a multi-modal place classification system that allows a mobile robot to identify places and recognize semantic categories in an indoor environment. The system effectively utilizes information from different robotic sensors by fusing multiple visual cues and laser range data. This is achieved using a high-level cue integration scheme based on a Support Vector Machine (SVM) that learns how to optimally combine and weight each cue. Our multi-modal place classification approach can be used to obtain a real-time semantic space labeling system which integrates information over time and space. We perform an extensive experimental evaluation of the method for two different platforms and environments, on a realistic off-line database and in a live experiment on an autonomous robot. The results clearly demonstrate the effecThe International Journal of Robotics Research Vol. 00, No. 00, Xxxxxxxx 2009, pp. 000–000 DOI: 10.1177/0278364909356483 c The Author(s), 2009. Reprints and permissions: http://www.sagepub.co.uk/journalsPermissions.nav Figures 1–15, 17, 18 appear in color online: http://ijr.sagepub.com tiveness of our cue integration scheme and its value for robust place classification under varying conditions. KEY WORDS—recognition, sensor fusion, localization, multi-modal place classification, sensor and cue integration, semantic annotation of space
A Survey on Automatically Mining Facets for Web Queries

Received Jan 4, 2017 Revised Jun 2, 2017 Accepted Jun 26, 2017 In this paper, a detailed survey on different facet mining techniques, their advantages and disadvantages is carried out. Facets are any word or phrase which summarize an important aspect about the web query. Researchers proposed different efficient techniques which improves the user’s web query search experiences magnificently. Users are happy when they find the relevant information to their query in the top results. The objectives of their research are: (1) To present automated solution to derive the query facets by analyzing the text query; (2) To create taxonomy of query refinement strategies for efficient results; and (3) To personalize search according to user interest. Keyword:
Visualizing Topic Models

Managing large collections of documents is an important problem for many areas of science, industry, and culture. Probabilistic topic modeling offers a promising solution. Topic modeling is an unsupervised machine learning method that learns the underlying themes in a large collection of otherwise unorganized documents. This discovered structure summarizes and organizes the documents. However, topic models are high-level statistical tools—a user must scrutinize numerical distributions to understand and explore their results. In this paper, we present a method for visualizing topic models. Our method creates a navigator of the documents, allowing users to explore the hidden structure that a topic model discovers. These browsing interfaces reveal meaningful patterns in a collection, helping end-users explore and understand its contents in new ways. We provide open source software of our method. Understanding and navigating large collections of documents has become an important activity in many spheres. However, many document collections are not coherently organized and organizing them by hand is impractical. We need automated ways to discover and visualize the structure of a collection in order to more easily explore its contents. Probabilistic topic modeling is a set of machine learning tools that may provide a solution (Blei and Lafferty 2009). Topic modeling algorithms discover a hidden thematic structure in a collection of documents; they find salient themes and represent each document as a combination of themes. However, topic models are high-level statistical tools. A user must scrutinize numerical distributions to understand and explore their results; the raw output of the model is not enough to create an easily explored corpus. We propose a method for using a fitted topic model to organize, summarize, visualize, and interact with a corpus. With our method, users can explore the corpus, moving between high level discovered summaries (the “topics”) and the documents themselves, as Figure 1 illustrates. Our design is centered around the idea that the model both summarizes and organizes the collection. Our method translates these representations into a visual system for exploring a collection, but visualizing this structure is not enough. The discovered structure induces relationships—between topics and articles, and between articles and articles—which lead to interactions in the visualization. Copyright c © 2012, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Thus, we have three main goals in designing the visualization: summarize the corpus for the user; reveal the relationships between the content and summaries; and, reveal the relationships across content. We aim to present these in a ways that are accessible and useful to a spectrum of users, not just machine learning experts.
Support vector machine approach for protein subcellular localization prediction

MOTIVATION
Subcellular localization is a key functional characteristic of proteins. A fully automatic and reliable prediction system for protein subcellular localization is needed, especially for the analysis of large-scale genome sequences.


RESULTS
In this paper, Support Vector Machine has been introduced to predict the subcellular localization of proteins from their amino acid compositions. The total prediction accuracies reach 91.4% for three subcellular locations in prokaryotic organisms and 79.4% for four locations in eukaryotic organisms. Predictions by our approach are robust to errors in the protein N-terminal sequences. This new approach provides superior prediction performance compared with existing algorithms based on amino acid composition and can be a complementary method to other existing methods based on sorting signals.


AVAILABILITY
A web server implementing the prediction method is available at http://www.bioinfo.tsinghua.edu.cn/SubLoc/.


SUPPLEMENTARY INFORMATION
Supplementary material is available at http://www.bioinfo.tsinghua.edu.cn/SubLoc/.
Heroin epidemics, treatment and ODE modelling.

The UN [United Nations Office on Drugs and Crime (UNODC): World Drug Report, 2005, vol. 1: Analysis. UNODC, 2005.], EU [European Monitoring Centre for Drugs and Drug Addiction (EMCDDA): Annual Report, 2005.http://annualreport.emcdda.eu.int/en/home-en.html.] and WHO [World Health Organisation (WHO): Biregional Strategy for Harm Reduction, 2005-2009. HIV and Injecting Drug Use. WHO, 2005.] have consistently highlighted in recent years the ongoing and persistent nature of opiate and particularly heroin use on a global scale. While this is a global phenomenon, authors have emphasised the significant impact such an epidemic has on individual lives and on society. National prevalence studies have indicated the scale of the problem, but the drug-using career, typically consisting of initiation, habitual use, a treatment-relapse cycle and eventual recovery, is not well understood. This paper presents one of the first ODE models of opiate addiction, based on the principles of mathematical epidemiology. The aim of this model is to identify parameters of interest for further study, with a view to informing and assisting policy-makers in targeting prevention and treatment resources for maximum effectiveness. An epidemic threshold value, R(0), is proposed for the drug-using career. Sensitivity analysis is performed on R(0) and it is then used to examine the stability of the system. A condition under which a backward bifurcation may exist is found, as are conditions that permit the existence of one or more endemic equilibria. A key result arising from this model is that prevention is indeed better than cure.
Sentiment classification of Internet restaurant reviews written in Cantonese

Cantonese is an important dialect in some regions of Southern China. Local online users often represent their opinions and experiences on the web with written Cantonese. Although the information in those reviews is valuable to potential consumers and sellers, the huge amount of web reviews make it difficult to give an unbiased evaluation to a product and the Cantonese reviews are unintelligible for Mandarin Chinese speakers. In this paper, standard machine learning techniques naive Bayes and SVM are incorporated into the domain of online Cantonese-written restaurant reviews to automatically classify user reviews as positive or negative. The effects of feature presentations and feature sizes on classification performance are discussed. We find that accuracy is influenced by interaction between the classification models and the feature options. The naive Bayes classifier achieves as well as or better accuracy than SVM. Character-based bigrams are proved better features than unigrams and trigrams in capturing Cantonese sentiment orientation. 2010 Elsevier Ltd. All rights reserved.
A Computational Model of Graded Cueing: Robots Encouraging Behavior Change

Our work is motivated by the evidence that children with ASD are often behind in their development of imitative behavior, and that practicing through repeated interactions with a therapist can improve imitation abilities. Our model approximates those effects with a socially assistive robot in order to use technology to broaden access to ASD therapy. Our model of graded cueing informs how specific of feedback each participant needs based on their performance in previous rounds of imitation. What is Graded Cueing? Using Probabilistic Models to Make Up for Limited Data
5.10 A 1.4V 10.5MHz swing-boosted differential relaxation oscillator with 162.1dBc/Hz FOM and 9.86psrms period jitter in 0.18µm CMOS

Relaxation oscillators have a profound scope as on-chip reference clock sources or sensor front-ends in comparison to ring oscillators due to their superior frequency stability, control linearity, and wide tuning range. However, despite a better fundamental limit predicted in theory, the phase noise performance of relaxation oscillators trails behind that of ring oscillators. Furthermore, there is a huge gap between the maximum achievable 1/f2 phase noise FOM (169dBc/Hz) [1] and those achieved by recently proposed low-power relaxation oscillator implementations [1-4].
Fast Text Searching Allowing Errors

T h e string-matching problem is a very c o m m o n problem. We are searching for a string P = PtP2. . "Pro i n s i d e a la rge t ex t f i le T = t l t2. . . t . , b o t h sequences of characters from a f i n i t e character set Z. T h e characters may be English characters in a text file, DNA base pairs, lines of source code, angles between edges in polygons, machines or machine parts in a production schedule, music notes and tempo in a musical score, and so fo r th . We w a n t to f i n d a l l occurrences of P i n T; n a m e l y , we are searching for the set of starting posit ions F = {i[1 --i--n m + 1 s u c h t h a t titi+ l " " t i + m 1 = P } " T h e two most famous algorithms for this problem are t h e B o y e r M o o r e algorithm [3] and t h e K n u t h Morris Pratt algorithm [10]. There are many extensions to t h i s problem; for example, we may be looking for a set of patterns, a pattern w i t h "wi ld cards," or a regular expression. String-matching tools are included in every reasonable text editor, word processor, and many other applications.
Binary codes capable of correcting deletions, insertions and reversals