Miracle-dz/UniFAR-FTUs huggingface dataset:
A* Search for Soft Constraints Bounded by Tree Decompositions

Some of the most e-cient methods for solving soft con- straints are based on heuristic search using an evaluation function that is mechanically generated from the problem. However, if only a few best so- lutions are needed, signiflcant efiort can be wasted pre-computing heuris- tics that are not used during search. Recently, a scheme for depth-flrst branch-and-bound search has been proposed that avoids the problems of pre-computation by interleaving search with the generation of heuristics using tree decomposition and dynamic programming. In this paper, we extend this idea to A* search, which has the advantage of expanding a minimal number of search nodes to flnd optimal solutions, and allows to generate solutions in best-flrst order. The approach uses tree decomposi- tion and dynamic programming to generate only those heuristics that are speciflcally required to generate a next best solution. The time complex- ity of the approach is thus optimal among all search algorithms having access to the same heuristics, while its space complexity is bounded by structural parameters of the constraint graph (induced width) in the worst case, and is even lower in the average case.
The Heme Transport Capacity of LHR1 Determines the Extent of Virulence in Leishmania amazonensis

Leishmania spp. are trypanosomatid parasites that replicate intracellularly in macrophages, causing serious human morbidity and mortality throughout the world. Trypanosomatid protozoa cannot synthesize heme, so must acquire this essential cofactor from their environment. Earlier studies identified LHR1 as a Leishmania amazonensis transmembrane protein that mediates heme uptake. Null mutants of LHR1 are not viable and single knockout strains have reduced virulence, but very little is known about the properties of LHR1 directly associated with heme transport. Here, we use functional assays in Saccharomyces cerevisiae to show that specific tyrosine residues within the first three predicted transmembrane domains of LHR1 are required for efficient heme uptake. These tyrosines are unique to LHR1, consistent with the low similarity between LHR1 and its corresponding homologs in C. elegans and human. Substitution of these tyrosines in LHR1 resulted in varying degrees of heme transport inhibition, phenotypes that closely mirrored the impaired ability of L. amazonensis to replicate as intracellular amastigotes in macrophages and generate cutaneous lesions in mice. Taken together, our results imply that the mechanism for heme transport by LHR1 is distinctive and may have adapted to secure heme, a limiting cofactor, inside the host. Since LHR1 is significantly divergent from the human heme transporter HRG1, our findings lay the groundwork for selective targeting of LHR1 by small molecule antagonists.
Labelling Graphs with a Condition at Distance 2

Given a simple graph $G = ( V,E )$ and a positive number d, an $L_d ( 2,1 )$-labelling of G is a function $f:V ( G ) \to [ 0,\infty )$ such that whenever $x,y \in V$ are adjacent, $| f ( x ) - f ( .
iGAPSearch: Using phone cameras to search around the world

This paper proposes and implement an iGAPSearch System (GPS Aided Photo Search System) to identify buildings through their photos captured by phone cameras. User need to take a picture of the building he/she wants to know with Android phone and upload the picture to our system. The system returns name and introduction on the buildings. We use SIFT features and the bag of features method to represent and recognize building images. Four distance methods are compared to estimate the distance between query image and those in database, namely L1-norm, L2-norm, KL-divergence and χ2 distance. We calculated ROC curves to compare these methods. Experimental results exhibit that χ2 distance has the best performance. Our system is fast enough for realtime application and its accuracy is satisfactory under the experimental environment.
Feedback-based gameplay metrics: measuring player experience via automatic visual analysis

Using gameplay metrics to articulate player interaction within game systems has received increased interest in game studies. The value of gameplay metrics comes from a desire to empirically validate over a decade of theorization of player experience and knowledge of games as ludic systems. Taking gameplay metrics beyond formalized user testing (i.e. with the aim of improving a product) allows researchers the freedom of examining any commercially available game without the need to have access to the game's source code. This paper offers a new methodology to obtain data on player behavior, achieved through analyzing video and audio streams. Game interface features are being analyzed automatically, which are indicative of player behavior and gameplay events. This paper outlines the development of this methodology and its application to research that seeks to understand the nature of engagement and player motivations.
Introduction to Arithmetic Groups

This book provides a gentle introduction to the study of arithmetic subgroups of semisimple Lie groups. This means that the goal is to understand the group SL(n,Z) and certain of its subgroups. Among the major results discussed in the later chapters are the Mostow Rigidity Theorem, the Margulis Superrigidity Theorem, Ratner's Theorems, and the classification of arithmetic subgroups of classical groups. As background for the proofs of these theorems, the book provides primers on lattice subgroups, arithmetic groups, real rank and Q-rank, ergodic theory, unitary representations, amenability, Kazhdan's property (T), and quasi-isometries. Numerous exercises enhance the book's usefulness both as a textbook for a second-year graduate course and for self-study. In addition, notes at the end of each chapter have suggestions for further reading. (Proofs in this book often consider only an illuminating special case.) Readers are expected to have some acquaintance with Lie groups, but appendices briefly review the prerequisite background.
Machine Learning Methods in Systematic Reviews: Identifying Quality Improvement Intervention Evaluations

Background Electronic searches typically yield far more citations than are relevant, and reviewers spend a substantial amount of time screening titles and abstracts to identify potential studies eligible for inclusion in a review. This is of particular relevance in complex research fields such as quality improvement. We tested a semiautomated literature screening process applied to the title and abstract screening stage of systematic reviews. A machine learning approach may allow literature reviewers to screen only a fraction of a search output and to use a predictive model to learn and then emulate the reviewers’ decisions. Once learned, the model can apply the selection process to an essentially unlimited number of citations. Method Two independent literature reviewers screened 1,591 quasi-randomly selected citations in a training dataset used to predict decisions on the remaining citations in a MEDLINE search output of 9,395 citations. We explored different prediction algorithms and tested results against reference samples screened by experts in quality improvement. Qualitative (relevance cutoff determined in ROC curve) and quantitative predictions (probability rank order of citations) were determined. Results The agreement between independent literature reviewers ranged from κ= 0.55 to 0.57. Across two reference samples, the predictive performance of the machine learning approach demonstrated 90.1 percent sensitivity, 43.9 percent specificity, and 32.1 percent PPV. This translates to a reduction of 36.1 percent in citation screening if applied. The predictive performance was affected by reviewer disagreements: a subgroup analysis restricted to citations both reviewers agreed on showed a sensitivity of 98.8 percent (specificity 43.9 percent). Conclusion Machine learning approaches may assist in the title and abstract inclusion screening process in systematic reviews of complex, steadily expanding research fields such as quality improvement. Increased reviewer agreement appeared to be associated with improved predictive performance.
Voltage and reactive power control in MV networks integrating MicroGrids

The main objective of this paper is to describe a strategy to deal with the voltage/reactive power problem for a MV distribution network integrating microgrids. The global problem, concerning all voltage levels, is detailed here and will imply the optimization of operating conditions by using the control capabilities of power electronic interfaces from DG sources, OLTCs and microgrids, through the application of an EPSO optimization algorithm.
An image mining approach for clustering traffic behaviors based on knowledge discovery of image databases

With this rapid increase in generating and collecting images, image mining and knowledge discovery attract so much attention. In this paper, we present a framework for extracting knowledge from sequence of images in the form of natural languages. In another view point, this process summarizes the enormous visual information of sequence of images into some simple and small sentences with the aim of automatic traffic regulations checking. Sequences of images have been manually extracted from a video of moving vehicles in cities. The structure of the system composed of Image Analysis and Knowledge Processing modules. Image Analysis module works on raw images and tries to extract a set of records consisting of spatial-temporal characteristics of moving objects in the scene. These records are passed to knowledge processing phase for further processing, information transformation and summarization. Knowledge processing phase consists of two modules: Trajectory Detection and Fuzzy Rule base. A new similarity measure based on Longest Common Subsequence (LCS) is proposed and used in trajectory detection module. This module tries to form traffic behavioral classes. In fuzzy Rule Base module, traffic regulations have been modeled using fuzzy rules and fuzzy inference used for checking normality/abnormality of the actions in the scene. The output of the system in the form of Natural Languages clearly shows what happened in the scene.
3D-Virtual Reality in Science Education: An Implication for Astronomy Teaching

This work presents a new virtual environment (VE) which employs a dynamic 3-D model of the solar system. It is based on powerful scientific visualization techniques and can be used as an effective aide in astronomy teaching. The learner “enters” a virtual model of the physical world, journeys through it, zooms in or out as he or she wishes, changes his or her view point and perspective, as the virtual world continues to “behave” and operate in its usual manner. The continual motion of the planets generates day and night, seasons, eclipses, and phases—topics that are customarily hard to grasp, especially at young age. The model allows for a powerful learning experience, and facilitates the mental construction of three-dimensional space, where objects are varied and different, but share common features and obey the same physical principles. The new platform helps to overcome the inherent geocentric view and ensures the transition to a scientific, heliocentric view of the solar system.
Improv: Interactive Improvisational Animation and Music

Improv is a system for the creation of real-time behavior-based animated actors. There have been several recent efforts to build network distributed autonomous agents, but in general, these efforts do not focus on the author’s view. To create rich interactive worlds inhabited by believable animated actors, authors need the proper tools. Improv provides tools to create actors that respond to users and to each other in real-time with personalities and moods consistent with the author’s goals and intentions.
Labeling Clusters - Tagging Resources

In order to support the navigation in huge document collections efficiently, tagged hierarchical structures can be used. Often, multiple tags are used to describe resources. For users, it is important to correctly interpret such tag combinations. In this paper, we propose the usage of tag groups for addressing this issue and an algorithm that is able to extract these automatically for text documents. The approach is based on the diversity of content in a document collection and a user’s structuring preference. For evaluation, we use methods from ontology evaluation and show the validity of our approach on a benchmark dataset. 1 Labeling vs. Tagging When searching for information, structured access to data, e.g., as given by web directories or social tagging systems like del.icio. us1 can be very helpful. The goal of our work is to automatically provide such structure for unstructured collections. Here, we consider text documents. One possible direction is to hierarchically cluster documents based on their content and further knowledge like personal clustering preferences [Bade and Nurnberger, 2008]. This hierarchy can then be used to browse the collection. However, the labeling of the individual clusters is crucial. A standard approach is to select a small set of terms that are expected to describe the documents in the cluster well. The idea of cluster labeling is closely related to tagging. In tagging, a small set of terms is assigned to a resource with the goal to describe it. Theoretically, every resource can be described with a different set of terms. However, existing tagging systems have shown that users tend to use similar / equal terms [Golder and Huberman, 2006]. This makes finding relevant or interesting resources more easy for a user. Therefore, a system for automatic tagging should follow this idea. An initial clustering identifies similar documents having common properties. It is natural to tag the documents of a cluster with similar tags. Hence, the cluster label can be used to tag all resources in this cluster. Furthermore, hierarchical relations between the tags can be derived from a cluster hierarchy. Automatic tagging was already proposed in the literature, e.g. by [Mishne, 2006; Begelman et al., 2006]. Summing up, access to the initially unstructured collection can be given either through a labeled cluster hierarchy or through a tag cloud as typical for todays tagging systems. Both representations can be gained through the same http://del.icio. us algorithm. One possible solution is presented in the following, which is based on an initial hierarchical clustering. This paper focuses on the extraction of the tags assuming the cluster hierarchy was already build. Information on the clustering process can be found in [Bade et al., 2007].
Improving machine vision via incorporating expectation-maximization into Deep Spatio-Temporal learning

The Deep Spatio-Temporal Inference Network (DeSTIN) is a deep learning architecture which combines un-supervised learning and Bayesian inference. The original version of DeSTIN incorporates k-means clustering inside each processing node. Here we propose to replace k-means with a more sophisticated algorithm, online EM (Expectation Maximization), and show that this improves DeSTIN's performance on image classification and restoration tasks.
Oppcast: Exploiting Spatial and Channel Diversity for Robust Data Collection in Urban Environments

ZigBee shares the 2.4 GHz ISM band with a number of wireless technologies like WiFi, Bluetooth, and common household appliances like a microwave and a cordless phone to name a few. Due to the large-scale penetration of these technologies in urban environments, ZigBee communication suffers from severe cross-technology interference (CTI). Data collection in the presence of such highly dynamic CTI is quite challenging. Our work first examines the different deployment environments under the influence of planned and unplanned CTI and later proposes Oppcast, a robust and energy- efficient data collection protocol that carefully exploits a combination of spatial and channel diversity to eliminate the need for performing expensive channel estimation in advance. Our extensive evaluation in both a large-scale testbed (Academic Institution) and various urban environments (Carpark, Residential Complex, Shopping Mall and Cafeteria) shows that Oppcast is not only robust to CTI with reliability consistently maintained above 98.55%, but is also up to 2.4 times more energy efficient than the state-of-the-art data collection protocols. The rationale behind Oppcast exhibiting high robustness in highly dynamic environments is a significant increase in the number of communication opportunities it gets by exploiting multiple routes over multiple channels towards the destination.
Data-driven temporal filters obtained via different optimization criteria evaluated on Aurora2 database

In deriving the data-driven temporal filters for speech features, the Linear Discriminant Analysis (LDA) has been shown to be successful in improving the feature robustness [1,2,3]. In our previous works [4,5] it was shown that the criteria of Principal Component Analysis (PCA) and Minimum Classification Error (MCE) can also be used to obtain the data-driven temporal filters in improving the speech recognition performance. In this paper, we proposed to perform Cepstral Normalization before applying these temporal filters, and evaluated the effectiveness of these different data-driven temporal filters on the AURORA2 database. Test results showed very significant improvements for almost all different cases, specially when the training and testing environments are highly mismatched.
On-Chip Delay Measurement Circuit for Reliability Characterization of SRAM

This paper represents a framework for on-chip delay measurement, which will be helpful in measuring the impact of device level variability on memory access time. Commercial frameworks for simulating circuit degradation due to device aging effects are not available. 1 KB SRAM is used as test setup for which on-chip read access time measurement is performed. On-chip delay measurement is performed using proposed novel architecture of 2-stage multi-resolution based vernier time-to-digital converter (TDC). TDC is designed for 2 ps resolution. Actual architecture of TDC is 5 bit, however entire range can be shifted with minimum steps of 40 ps to achieve desirable range. A novel design of programmable linear delay element (PLDE) is also presented in this paper, which is used as a key component of the TDC architecture. Two types of PLDEs are designed to provide delay with step size 2 ps and 40 ps. Use of PLDE in TDC architecture provides the facility of adjustment which makes it more robust to process and temperature variations. Overall design is laid out in 65 nm CMOS process and evaluated using post layout simulation.
UNIX Writing Tools Primer for CS Graduate Students: Ispell,

As graduate students in computer science at Ohio University, you will be expected to write reports for classes, conference papers, journal papers, theses or dissertations. It is expected that such documents will be well written and carefully prepared. Writing well is a difficult task, even for those of us with many years of experience. It can be even more difficult to prepare professional looking documents because technical writing in computer science (and other disciplines) often involves extensive references, complicated technical diagrams, and complicated formulae. Fortunately, there are a number of relatively standard (and free! ) software tools that can help produce professional looking documents. In this primer, a brief introduction is given to each of these software tools. While some of these tools can be used in a stand alone fashion, this document is primarily concerned with the use of these tools for the creation of professionally prepared reports. Because writing well is difficult, you may want to invest in a good dictionary and other books about writing before you start. There are many excellent books on writing, grammar, and how to avoid common mistakes. My favorites are Nicholas Higham’s Handbook of Writing for the Mathematical Sciences [3] and Brusaw et al. Handbook of Technical Writing [1]. You may also wish to use some of the classics, such Strunk and White’s The Elements of Style [4]. In addition, there are a number of expository articles about writing that you may want to read. A somewhat recent article by Hemaspaandra and Selman [2] provides excellent advice on writing and producing large technical documents. Their article provides a number of additional references on writing that may be useful to some of you. This document is a primer on six common software tools for producing technical documents: ispell, LTEX, bibtex, xfig, gnuplot, and xv. All of these tools are relatively complex, and hence this primer cannot possibly cover all the many ways of using them. For additional help, you will almost certainly want to purchase a user’s guide, such as the classic book LTEX: A Documentation Preparation System User’s Guide and Reference Manual by Leslie Lamport [5].
The Oxford handbook of deaf studies, language, and education

Oxford Handbooks offer authoritative and up-to-date reviews of original research in a particular subject area. Specially commissioned chapters from leading figures in the discipline give critical examinations of the progress and direction of debates, as well as a foundation for future research. Oxford Handbooks provide scholars and graduate students with compelling new perspectives upon a wide range of subjects in the humanities, social sciences, and sciences. The adage "Those who do not learn from history are doomed to repeat it" is a powerful one for parents, teachers, and other professionals involved with or interested in deaf individuals or the Deaf community. Myths grown from ignorance have long dogged the field, and faulty assumptions and overgeneralizations have persisted despite contrary evidence. A study of the history of deaf education reveals patterns that have affected educational policy and legislation for deaf people around the world; these patterns are related to several themes critical to the chapters of this volume. One such theme is the importance of parental involvement in raising and educating deaf children. Another relates to how Deaf people have taken an increasingly greater role in influencing their own futures and places in society. In published histories, we see the longstanding conflicts through the centuries that pertain to sign language and spoken communication philosophies, as well as the contributions of the individuals who advocated alternative strategies for teaching deaf children. More recently, investigators have recognized the need for a diverse approach to language and language learning. Advances in technology, cognitive science, linguistics, and the social sciences have alternately led and followed changes in theory and practice, resulting in a changing landscape for deaf and hard-of-hearing individuals and those connected to them. This second volume of the The Oxford Handbook of Deaf Studies, Language, and Education picks up where that first landmark volume left off, describing those advances and offering readers the opportunity to understand the current status of research in the field while recognizing the opportunities and challenges that lie ahead. In Volume 2, an international group of contributing experts provide state-of-the-art summaries intended for students, practitioners, and researchers. Not only does it describe where we are, it helps to chart courses for the future.