Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast Low-Rank Subspace Segmentation

Subspace segmentation is the problem of segmenting (or grouping) a set of n data points into a number of clusters, with each cluster being a (linear) subspace. The recently established algorithms such as Sparse Subspace Clustering (SSC), Low-Rank Representation (LRR) and Low-Rank Subspace Segmentation (LRSS) are effective in terms of segmentation accuracy, but computationally inefficient as they possess a complexity of O(n3), which is too high to afford for the case where n is very large. In this paper we devise a fast subspace segmentation algorithm with complexity of O(n log (n)). This is achieved by firstly using partial Singular Value Decomposition (SVD) to approximate the solution of LRSS, secondly utilizing Locality Sensitive Hashing (LSH) to build a sparse affinity graph that encodes the subspace memberships, and finally adopting a fast Normalized Cut (NCut) algorithm to produce the final segmentation results. Besides of high efficiency, our algorithm also has comparable effectiveness as the original LRSS method.
Does Social Media Affect Consumer Decision-Making?

Does social media affect consumer decision-making? Social media becomes an important communication tool that people use to connect to other people or organization. People use social media to share their experiences, reviews, information, advice, warnings, tips and/or any kind of issues that are interesting to their “connection” or friends. That information is a helpful source, which may influence consumer’s decision-making. Most of studies showed that people use information on social media as the guideline for their future purchase or planning their future trip. Also, social media is used as an advertising for the marketer. Marketers take this advantage and create marketing strategy, which in turn could help them gain more customers. The social media environment is very easy to apply and to reach the reach customer. These benefits give persons convenience to achieve what they are looking for. People tend to believe in what their friends recommend. Facebook, Twitter, or Myspace is the most popular social media site that people share their lifestyle, stories. , or even where they went for vacation. Posting information could lead their friends to do the same thing or use their information to make decisions. The goal of this research report is to ascertain through a review of selected literature on social media its influence on travelers' decision-making for their future vacations.
Network Externalities in Mutual Funds

The literature on mutual fund flows documents surprisingly large return effects given that mutual fund flows are uninformed (i.e., not related to fundamentals). I provide evidence that network externalities generate the necessary amplification mechanism to support these results. Network externalities are generated by mutual funds with common holdings and return-chasing investors. Economically, I show that the fund flow network externality is 32–92% as large as the typical explanatory effects (e.g., lagged flows). Network externalities generate a 1.5% quarterly excess return that reverses in the subsequent year, and are independent of style investing and robust to multiple specifications of holdings similarity.
Wide-band image guided visible-band image enhancement

In this paper, we present a new algorithm which enhances an image quality of the visible-band image in low light conditions by using the wide-band (400–1000 nm) image. In our proposed algorithm, the high signal-to-noise ratio (SNR) capability of the wide-band image is utilized, and the bivariate shrinkage and the multiresolution bilateral filter are incorporated with our joint filtering scheme. Our proposed method employs the cross channel paradigm to further reduce chromatic low frequency noise. Detail transfer is performed by using a mapping function on the wavelet domain. The experimental result with real image will be given in order to illustrate the effectiveness of the proposed algorithm.
A N OBJECT MODEL SUPPORTING AD-HOC APPLICATION PARTITIONING IN HETEROGENEOUS NETWORK ENVIRONMENTS

Application partitioning is an important technique for providing optimized performance of complex, interactive applications in heterogeneous network environments with widely varying capacities of execution and transport resources. The basic idea is to partition an application into a number of component processes that may be allocated to the various available network nodes with the objective to balance the load and optimize the overall performance. Partitions and allocation schemes may possibly be adjusted during runtime to account for changing situations. However, it is in general unclear how to find suitable partition borders: typical object code is essentially unstructured and therefore does not lend itself to an easy (i.e., syntactic) computation of potential component processes. On the other hand, distributed hypermedia systems such as the World-Wide Web provide already a very well structured representation for complex documents, where components can easily be derived using a simple syntactic analysis. This paper introduces the concept of a unified structured representation for applications and data, where syntactic techniques for data analysis can be carr ied over to application partition computation. Furthermore, an execution model is provided that allows for dynamic migration of statefull partition processes, supporting run-time reallocation and mobile objects.
Goal Delegation in Multiagent System

The organization of an event, like a conference, presents some aspects of distributed problem solving that we think can be tackled in an efficient way through goal delegation. Our application exploits the capabilities of both interface agents and collaborative agents to ease the work of negotiating a list of services, with different priorities and constraints. It shows that agents can approach even complex tasks through goal delegation and offer dynamic and flexible solutions for supply chains, especially to deal with unexpected events and chain
A novel temperature and disturbance insensitive DAC calibration method

This paper presents a new foreground DAC calibration method that is insensitive to temperature fluctuations and on-chip disturbances. In the proposed current cell, the same number of unit transistors is always used, guaranteeing matched response for all current cells. These transistors are divided in two groups: a fixed group and a configurable group. The unit transistors in the configurable group can be interchanged with additional redundant unit transistors, such that the mismatch errors of the configurable group compensate the mismatch errors of the fixed group. Together they generate the needed output current. Thus all current cells feature matched temperature coefficients and dynamic response. For an exemplary 6+6bits segmented current steering DAC, the expected 99% yield INL improves with almost 3 bits while using only 30% additional unit transistors.
Endobronchial ultrasound elastography in the diagnosis of mediastinal and hilar lymph nodes.

OBJECTIVE Endobronchial ultrasound elastography is a new technique for describing the stiffness of tissue during endobronchial ultrasound-guided transbronchial needle aspiration. The aims of this study were to evaluate the utility of endobronchial ultrasound elastography for mediastinal and hilar lymph nodes, and to compare the elastographic patterns of lymph nodes with results from endobronchial ultrasound-guided transbronchial needle aspiration. METHODS Seventy-five lymph nodes were evaluated. A convex probe endobronchial ultrasound was used with a new endoscopic ultrasound processor to assess elastographic patterns that were classified based on color distribution as follows: Type 1, predominantly non-blue (green, yellow and red); Type 2, part blue, part non-blue (green, yellow and red); Type 3, predominantly blue. The elastographic patterns were compared with the final pathologic results from endobronchial ultrasound-guided transbronchial needle aspiration. RESULTS On pathological evaluation of the lymph nodes, 33 were benign and 42 were malignant. The lymph nodes that were classified as Type 1 on endobronchial ultrasound elastography were benign in 24/24 (100%); for Type 2 lymph nodes, 6/14 (46.9%) were benign and 8/14 (57.1%) were malignant; Type 3 lymph nodes were benign in 2/37 (5.4%) and malignant in 35/37 (94.6%). In classifying Type 1 as 'benign' and Type 3 as 'malignant,' the sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy rates were 100, 92.3, 94.6, 100 and 96.7%, respectively. CONCLUSIONS Endobronchial ultrasound elastography of mediastinal and hilar lymph nodes is a noninvasive technique that can be performed reliably and may be helpful in the prediction of nodal metastasis during endobronchial ultrasound-guided transbronchial needle aspiration.
Development and research of models of organization storages based on the software-defined infrastructure

Data centers are widely used for the placement of highly loaded applications and solutions used for the processing of large data sets (BigData). For effective access to services, minimal response time of data storages is required. We explored a new paradigm in software-defined infrastructure distributed cloud systems. Also the design principles and high-level architectural design of the SD infrastructure controller are presented here. Modern applications and services needs constantly transforming infrastructure for ensuring consumer requirements (SLAs) amidst provider constraints (costs). This paper examines infrastructure in the context of platform for new applications and services and discussing some of the fundamental characteristics required of such infrastructure called software-defined infrastructure (SDI). We developed the models of the software-defined infrastructure. We performed an experiment to analyze the productivity of software-defined storage. Our experiment has shown that software-defined storage and scheduling algorithm in software-defined infrastructure placement can gain growth performance compared with the physical storage and virtual machines. This is necessary when storage systems work with high intensities requests.
GPU Activity Prediction using Representation Learning

GPU activity prediction is an important and complex problem. This is due to the high level of contention among thousands of parallel threads. This problem was mostly addressed using heuristics. We propose a representation learning approach to address this problem. We model any performance metric as a temporal function of the executed instructions with the intuition that the flow of instructions can be identified as distinct activities of the code. Our experiments show high accuracy and non-trivial predictive power of representation learning on a benchmark.
Efficient Evaluation of Imprecise Location-Dependent Queries

In location-based services, it is common for a user to issue a query based on his/her current position. One such example is "find the available cabs within two miles of my current location". Very often, the query issuers' locations are imprecise due to measurement error, sampling error, or message delay. They may also want to protect their privacy by providing a less precise location. In this paper, we study the efficiency of queries that return probabilistic guarantees for location data with uncertainty. We classify this query into two types, based on whether the data (1) has no uncertainty (e.g., shops and restaurants), or (2) has a controlled degree of uncertainty (e.g., moving vehicles). Based on this classification, we develop three methods to improve the computational and I/O performance. The first method expands the query range based on the query issuer's uncertainty. The second idea exchanges the roles of query and data. The third technique exploits the fact that users may only be interested in answers with probabilities higher than some threshold. Experimental simulation over a realistic dataset reveals that our approaches improve the query performance significantly.
A Meta-Analytic Review of Adult Cognitive-Behavioral Treatment Outcome Across the Anxiety Disorders

The efficacy of cognitive behavioral treatments (CBT) for anxiety in adults has been supported by multiple meta-analyses. However, most have focused on only 1 diagnosis, thereby disallowing diagnostic comparisons. This study examined the efficacy of CBT across the anxiety disorders. One hundred eight trials of CBT for an anxiety disorder met study criteria. Cognitive therapy and exposure therapy alone, in combination, or combined with relaxation training, were efficacious across the anxiety disorders, with no differential efficacy for any treatment components for any specific diagnoses. However, when comparing across diagnoses, outcomes for generalized anxiety disorder and posttraumatic stress disorder were superior to those for social anxiety disorder, but no other differences emerged. CBT effects were superior to those for no-treatment and expectancy control treatments, although tentative evidence suggested equal effects of CBT when compared with relaxation-only treatments.
Contribution of working memory in parity and proportional judgments

This paper presents experimental evidence on the differences in a sentence–picture verification task under additional memory load between parity and proportional quantifiers. We asked subjects to memorize strings of four or six digits, then to decide whether a quantified sentence was true for a given picture, and finally to recall the initially given string of numbers. The results show that: (a) proportional quantifiers are more difficult than parity quantifiers with respect to reaction time and accuracy; (b) maintaining either four or six elements in working memory has the same effect on the processing of parity quantifiers; (c) however, in the case of proportional quantifiers subjects perform better in the verification tasks under the six-digit load condition, and (d) even though the strings of four numbers were better recalled by subjects after judging parity there is no difference between quantifiers in the case of the six-element condition. We briefly outline two alternative explanations for the observed phenomena rooted in the computational model of quantifier verification and the different theories of working memory.
Comparative Study of Deep Learning Software Frameworks

Deep learning methods have resulted in significant performance improvements in several application domains and as such several software frameworks have been developed to facilitate their implementation. This paper presents a comparative study of five deep learning frameworks, namely Caffe, Neon, TensorFlow, Theano, and Torch, on three aspects: extensibility, hardware utilization, and speed. The study is performed on several types of deep learning architectures and we evaluate the performance of the above frameworks when employed on a single machine for both (multi-threaded) CPU and GPU (Nvidia Titan X) settings. The speed performance metrics used here include the gradient computation time, which is important during the training phase of deep networks, and the forward time, which is important from the deployment perspective of trained networks. For convolutional networks, we also report how each of these frameworks support various convolutional algorithms and their corresponding performance. From our experiments, we observe that Theano and Torch are the most easily extensible frameworks. We observe that Torch is best suited for any deep architecture on CPU, followed by Theano. It also achieves the best performance on the GPU for large convolutional and fully connected networks, followed closely by Neon. Theano achieves the best performance on GPU for training and deployment of LSTM networks. Caffe is the easiest for evaluating the performance of standard deep architectures. Finally, TensorFlow is a very flexible framework, similar to Theano, but its performance is currently not competitive compared to the other studied frameworks.
Optimal distribution for four-station TDOA location system

At least four stations are needed in time difference of arrival (TDOA) position system locating the moving target in 3D space. Positioning precision is influenced by the distribution of location stations importantly. Firstly a positioning precision model is established and the factors affecting the positioning precision are analyzed in line-of-sight (LOS) environments by studying the principle of TDOA location system. Then they are simulated for eight different models that the 3D curves of position errors and geometric dilution of precision (GDOP) contour maps of four-station. Detailed data showed coverage value of every distribution of location stations. Finally, in this paper, we demonstrated the optimal distribution principle of four-station in 3D space for the moving target based on the links between location precision and the distribution of location stations.
Decentralized real-time monitoring of network-wide aggregates

The traditional monitoring paradigm of network and systems management, characterized by a central entity polling individual devices, is not adequate for today's large-scale networked systems whose states and configurations are highly dynamic. We outline principles for monitoring such new systems and stress the need for protocols that continuously monitor network-wide aggregates. To keep the overhead at acceptable levels, such protocols must be tunable, e.g., allow controlling the trade-off between accuracy and overhead. We describe and compare two of our efforts in developing protocols for decentralized monitoring of aggregates; one is based on spanning trees, the other on gossiping.
DistiLD Database: diseases and traits in linkage disequilibrium blocks

Genome-wide association studies (GWAS) have identified thousands of single nucleotide polymorphisms (SNPs) associated with the risk of hundreds of diseases. However, there is currently no database that enables non-specialists to answer the following simple questions: which SNPs associated with diseases are in linkage disequilibrium (LD) with a gene of interest? Which chromosomal regions have been associated with a given disease, and which are the potentially causal genes in each region? To answer these questions, we use data from the HapMap Project to partition each chromosome into so-called LD blocks, so that SNPs in LD with each other are preferentially in the same block, whereas SNPs not in LD are in different blocks. By projecting SNPs and genes onto LD blocks, the DistiLD database aims to increase usage of existing GWAS results by making it easy to query and visualize disease-associated SNPs and genes in their chromosomal context. The database is available at http://distild.jensenlab. org/.
Method and apparatus for the selective scoreboarding of computation results

Statically scheduled machines do have a disadvantage when dealing with dynamic events, such as cache hit or miss detection. Early VLIW machines were designed without caches, to achieve predictability in memory access. However, such designs suffer in memory performance. To achieve high performance, VLIW architectures must have adequate support for using caches. A simple VLIW design might use an architecture based on a stall-on-miss design, whereby the entire processor is stalled when a cache miss occurs. This design point is the most natural design option for the static nature of VLIW architectures.
Frequent-Pattern Based Facet Extraction from Graph Data

Graph is a general data model which is applicable to complex data structures. Hence, there are lots of data which can be expressed using graph data models. For example, Web, social networking services, bibliographic database, and Linked Open Data. Since such graph data can be heterogeneous, users are imposed a huge burden on searching over the graph data to find desired sub graphs. Faceted search is a promising approach to reduce the burden of searching graph data. Applying faceted search for graph data requires to determine objects (target sub graphs) and facets. To achieve this, in this paper, we propose a framework for faceted search over graph data. The framework is organized into two phases, namely, extraction phase and search phase. The main objective of this paper is to develop the extraction phase which has two main tasks, one is to extract target sub graphs, and the other is to extract facets. This paper applies frequent sub graph mining techniques to extract target sub graphs and facets. The proposed framework is experimentally evaluated using publicly available graph datasets, namely, citation network data and review network data, which show the proposed framework works as expected.
Implant zones of the jaws: implant location and related success rate.

The article demonstrates the factors of importance in the early and late failures of dental implants based on literature review. An implant location is one of many factors that can influence a success or failure of dental implants. The author identifies and describe four alveolar jaw regions--functional implant zones--with unique characteristics of anatomy, blood supply, pattern of bone resorption, bone quality and quantity, need for bone grafting and other supplemental surgical procedures, and a location-related implant success rate. The article discusses predisposing factors that can lead to early implant failures in different jaw zones. An implant location is investigated as one of these factors. A prior history of trauma to premaxillary region is described in the context of implant success in anterior maxilla. This zone is being referred by the author as the "traumatic zone. " The challenges of mandibular posterior implant reconstruction are presented in the context of blood supply to the mandible. A deficiency of vascularization in this region, especially in elderly and edentulous patients, lead the author to refer to this zone as the "ischemic zone. " The concept of relative ischemia of the posterior mandible that can develop with age and tooth loss is discussed. A thorough understanding of specifics of each functional implant zone should help to improve successes and prevent failures of dental implants.