Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Sieving, Storage, and Incubation Temperature on the Phospholipid Fatty Acid Profile of a Soil Microbial Community

Disturbances typically associated with the study of soil microbial communities, i.e., sieving, storage, and subsequent incubation at elevated temperatures, were investigated with phospholipid fatty acid (PLFA) analyses. Treatment effects were quantified by statistical analyses of the mole percentage distribution of the individual fatty acids. Changes in the concentrations of individual fatty acids over a 7-week storage period at 4.5°C were generally not statistically significant. Sieving effects (mesh size, 4 or 2 mm) on CO2 evolution and the PLFA profile were monitored over 3 weeks; the physical disturbance had only minor effects, although some damage to fungal hyphae by the first sieving (<4 mm) was suggested by a decrease in the signature fatty acid 18:2 ω6c. Temperature effects were investigated by incubating soil for up to 3 weeks at 4.5, 10, or 25°C. Principal component analyses demonstrated a significant shift in the PLFA composition at 25°C over the first 2 weeks, while changes at the other two temperatures were minor. Several of the changes observed at 25°C could be explained with reference to mechanisms of temperature adaptation or as a response to conditions of stress, including a decrease in the degree of unsaturation, an increased production of cyclopropyl fatty acids, and increased ratios of the branched-chain fatty acids iso-15:0 and iso-17:0 over anteiso-15:0 and anteiso-17:0, respectively. A decrease in the total amount of PLFA was also indicated.
Data conflict resolution among same entities in Web of Data

With the growing amount of published RDF datasets on similar domains, data conflict between similar entities (same-as) is becoming a common problem for Web of Data applications. In this paper we propose an algorithm to detect conflict of same properties values of similar entities and select the most accurate value. The proposed algorithm contains two major steps. The first step filters out low ranked datasets using a link analysis technique. The second step calculates and evaluates the focus level of a dataset in a specific domain. Finally, the value of the top ranked dataset is considered. The proposed algorithm is implemented by Java Programming Language and is evaluated by geographical datasets containing "country" entities.
The Minimum k-Colored Subgraph Problem in Haplotyping and DNA Primer Selection

In this paper we consider the minimum k-colored subgraph problem (MkCSP), which is motivated by maximum parsimony based population haplotyping and minimum primer set selection for DNA amplification by multiplex Polymerase Chain Reaction, two important problems in computational biology. We use several new techniques to obtain improved approximation algorithms for both the general MkCSP and some important special cases. We also establish a novel relation between MkCSP and the densest k-subgraph problem, whose approximability is notoriously hard. This relation gives evidence that some of the approximation factors in this paper might be almost tight. Furthermore, this relation could shed light into a potential proof of polynomial inapproximability for the densest k-subgraph problem (using an inapproximability result that we present for a generalized version of the densest k-subgraph problem).
Entity Analysis with Weak Supervision: Typing, Linking, and Attribute Extraction

Entity Analysis with Weak Supervision: Typing, Linking, and Attribute Extraction Xiao Ling Chair of the Supervisory Committee: Professor Daniel S. Weld Computer Science and Engineering With the advent of the Web, textual information has grown at an explosive rate. To digest this enormous amount of data, an automatic solution, Information Extraction (IE), has become necessary. Information extraction is a task of converting unstructured text strings into structured machine-readable data. The first key step of a general IE pipeline is often to analyze entities mentioned in the text before making holistic conclusions. To fully understand each entity, one needs to detect their mentions, categorize them into semantic types, connect them with their knowledge base entries, and identify their attributes as well as the relationships with others. In this dissertation, we first present the problem of fine-grained entity recognition. Unlike most traditional named entity recognition systems using a small set of entity classes, e.g., person, organization, location or miscellaneous, we define a novel set of over one hundred fine-grained entity types. In order to intelligently understand text and extract a wide range of information, it is useful to more precisely determine the semantic classes of entities mentioned in unstructured text. We formulate the recognition problem as multi-class, multi-label classification, describe an unsupervised method for collecting training data, and present the FIGER implementation. Next, we demonstrate that fine-grained entity types are closely connected with other entity analysis tasks. We describe an entity linking system whose prediction heavily relies on these types and present a simple yet effective implementation, called VINCULUM. An extensive evaluation on nine data sets, comparing VINCULUM with two state-of-the-art systems, elucidates key aspects of the system that include mention extraction, candidate generation, entity type prediction, entity coreference, and coherence. Finally, we describe an approach to acquire commonsense knowledge from a massive amount of text on the Web. In particular, a system called SIZEITALL is developed to extract numerical attribute values for various classes of entities. To resolve the ambiguity from the surface form text, we canonicalize the extractions with respect to WordNet senses and build a knowledge base on physical size for thousands of entity classes. Throughout all three entity analysis tasks, we show the feasibility of building sophisticated IE systems without a significant investment in human effort to create sufficient labeled data.
An information-theoretic meta-theorem on edge-cut bounds

We consider the problem of multiple unicast in wireline networks. Edge-cut based bounds which are simple bounds on the rates achievable by routing flow are not in general, fundamental, i.e. they are not outer bounds on the capacity region. It has been observed that when the problem has some kind of symmetry involved, then flows and edge-cut based bounds are `close', i.e. within a constant or poly-logarithmic factor of each other. In this paper, we make the observation that in these very cases, such edge-cut based bounds are actually `close' to fundamental yielding an approximate characterization of the capacity region for these problems. We demonstrate this in the case of k-unicast in undirected networks, k-pair unicast in directed networks with symmetric demands i.e. for every source communicating to a destination at a certain rate, the destination communicates an independent message back to the source at the same rate, and sum-rate of k-groupcast in directed networks, i.e. a group of nodes, each of which has an independent message for every other node in the group. We place our work in context of existing results to suggest a meta-theorem: if there is inherent symmetry either in the network connectivity or in the traffic pattern, then edge-cut bounds are near-fundamental and flows approximately achieve capacity.
The EUMSSI Project - Event Understanding through Multimodal Social Stream Interpretation

Journalists, as well as users at home, face increasing amounts of data from a large variety of sources, both in professionally curated media archives and in the form of user-generated-content or social media. This provides a great opportunity at the same time as a great challenge to use all of this information, which EUMSSI approaches by providing semantically rich analysis of multimedia content, together with intuitive visual interfaces to explore the data and gain new insights. The goal of the EUMSSI project is to provide a complete system for large-scale multimedia analysis, including a wide range of analysis components working on different media modalities (video, audio, text). Additionally, we have developed two example applications (demonstrators) that build upon this platform with the goal to showcase the platform’s potential, but also to lead towards a commercial exploitation of the project outcomes. The first is a tool to help journalists when writing an article or preparing a report, while the second is aimed at viewers of TV or streaming content, providing background information and entertainment.They are currently running and publicly accessible online, incorporating hundreds of thousands of videos and news articles, as well as almost 10 million tweets.
SAM: A rethinking of prominent convolutional neural network architectures for visual object recognition

Convolutional neural networks play an increasingly important role in computer vision tasks, especially in the field of visual object recognition. Many prominent models, such as Inception, Maxout, ResNet, and NIN, have been proposed to significantly improve recognition performance. Inspired from those models, we propose a novel module called self-adaptive module (SAM). SAM consists of four passes and one selector. Specifically, the four passes include two direct passes with different receptive fields and depths, one residual pass, and one Maxout pass. Actually, the residual pass is used to speed up convergence, while we take advantage of the Maxout pass to enhance approximate capabilities of SAM. The selector is further designed to help choose reasonable output. Basically, SAM is intended to simplify design of any new deep learning architecture, since it no longer requires consideration of how to select receptive fields and depths. Our SAM is tested on the visual object recognition datasets including CIFAR-10, CIFAR-100, MNIST, and SVHN. The experimental results demonstrate that the SAM-Net has superior recognition performances on the four benchmarks, which achieve test errors of 5.76%, 28.56%, 0.31%, and 1.98%, respectively.
Association of GSTM1 Null Allele with Prostate Cancer Risk: Evidence from 36 Case-Control Studies

Background Glutathione S-transferase M1 (GSTM1) is thought to be involved in detoxifying several carcinogens and may play a vital role in tumorigenesis. Numerous studies have evaluated the association between GSTM1 null/present polymorphism and risk of prostate cancer (PCa). However, the results remain inconsistent. To derive a more precise estimation, we performed a meta-analysis. Methodology/Principal Findings A comprehensive search was conducted to identify all eligible case-control studies. We used odds ratios (ORs) with 95% confidence intervals (CIs) to assess the strength of the association. The overall association was significant (OR = 1.28, 95% CI: 1.11–1.48, P = 0.001). Moreover, subgroup analyses showed GSTM1 null genotype significantly associated with PCa risk among Asians (OR = 1.35, 95% CI: 1.03–1.78, P = 0.03) but not among Caucasians (OR = 1.12, 95% CI: 0.96–1.31, P = 0.16). In addition, we did not find that smoking modified the genotype effect on the risk of PCa. Conclusions/Significance The present meta-analysis suggested that GSTM1 null allele was a low-penetrant risk factor for PCa among Asians.
The Clinical and Health Economic Value of Clinical Laboratory Diagnostics

The ultimate goal of diagnostic testing is to guide disease management in order to improve patient outcomes and patient well-being. Patient populations are rarely homogenous and accurate diagnostic tests can dissect the patient population and identify those patients with similar symptoms but very different underlying pathophysiology that will respond differently to different treatments. This stratification of patients can direct patients to appropriate treatment and is likely to result in clinical benefits for patients and economic benefits for the healthcare system. In this article we look at the clinical and economic benefits afforded by clinical laboratory diagnostics in three disease areas that represent substantial clinical and healthcare burdens to society; heart failure, Alzheimer’s disease and asthma.
Iterative joint integrated particle filter data association for multitarget tracking in clutter

In tracking closely located multiple targets, the traditional optimal multitarget data association approach such as joint integrated probabilistic data association (JIPDA) faces exponential complexity caused by combinatorial increasing of the number of possible measurement-to-track allocations, which severely limits its applicability. This paper presents an iterative implementation of the Joint Integrated Particle Filter (JIPF) which is particle filter basedl multitarget tracker with the capability of false track discrimination (FTD), and provides the trade off between the performance and computation resources, termed by iterative JIPF (iJIPF). The iJIPF is capable of approximating the single target tracking IPF to multitarget tracking JIPF by traversing the data association tree from the level 0 to full level within finite number of iterations. The assertions are validated by the simulations.
Experiments with Multi-component Rating Collaborative Filtering for Improved Recommendation

Collaborative filtering is a method to mine the expressed preferences of a group of users about a set of items to filter out uninteresting items for each individual user. They have been used in recommender systems to make personalized recommendation of items that could be interesting to the users. Collaborative filtering algorithms have traditionally been designed to work with user ratings with only one component. However, recently there has been a growing interest in using ratings on multiple aspects of items[2]. Yahoo Movies has started collecting ratings from users on different aspects of the movie such as Story, Acting, Visuals, Direction etc. In this work an approach to carry out collaborative filtering using multi-component ratings is demonstrated. In order to do this, the dependency structure among the components is discovered and incorporated into a mixture model. This algorithm is evaluated using Yahoo Movies! dataset and it is found that use of multiple components leads to improvement in recommendations over using only one component when very little training data is available. However, it is observed that when we have enough training data available we do not gain much from using additional components of the ratings. We also find agreement between the discovered dependency structure and theories proposed in psychometric literature regarding Halo effect with multicomponent ratings.
TCP Vegas-like Expert-Controlled Multicast Congestion Control Algorithm for Wireless Networks

In order to improve the performance of existing multicast congestion control algorithm applied to wireless networks, in this paper, a TCP Vegas-like expert-controlled multicast congestion control mechanism (VEMCC) is proposed, which uses a recently proposed TCP Vegas throughput model. In VEMCC, the network congestion state is detected according to network relative queuing delay and packet loss; the packet loss reason is distinguished between wireless link errors and network congestion; and the current state of the network is judged accurately; then different multicast source control strategies are taken to adjust the sending rate. The simulation results show that, VEMCC algorithm enhances the capability to distinguish between network congestion and random errors, and improves the network throughput. VEMCC can be applied to wireless networks well, and achieve some extent on TCP-friendliness over low packet error rate wireless link.
Recalling of Images using Hopfield Neural Network Model

In the present paper, an effort has been made for storing and recalling images with Hopfield Neural Network Model of auto-associative memory. Images are stored by calculating a corresponding weight matrix. Thereafter, starting from an arbitrary configuration, the memory will settle on exactly that stored image, which is nearest to the starting configuration in terms of Hamming distance. Thus given an incomplete or corrupted version of a stored image, the network is able to recall the corresponding original image. The storing of the objects has been performed according to the Hopfield algorithm explained below. Once the net has completely learnt this set of input patterns, a set of testing patterns containing degraded images will be given to the net. Then the Hopfield net will tend to recall the closest matching pattern for the given degraded image. The simulated results show that Hopfield model is the best for storing and recalling images.
Unified Saliency Detection Model Using Color and Texture Features

Saliency detection attracted attention of many researchers and had become a very active area of research. Recently, many saliency detection models have been proposed and achieved excellent performance in various fields. However, most of these models only consider low-level features. This paper proposes a novel saliency detection model using both color and texture features and incorporating higher-level priors. The SLIC superpixel algorithm is applied to form an over-segmentation of the image. Color saliency map and texture saliency map are calculated based on the region contrast method and adaptive weight. Higher-level priors including location prior and color prior are incorporated into the model to achieve a better performance and full resolution saliency map is obtained by using the up-sampling method. Experimental results on three datasets demonstrate that the proposed saliency detection model outperforms the state-of-the-art models.
Algorithms column: An overview of the recent progress on matrix multiplication by Virginia Vassilevska Williams

The exponent ω of matrix multiplication is the infimum over all real numbers c such that for all ε > 0 there is an algorithm that multiplies n - n matrices using at most O(nc+ε) arithmetic operations over an arbitrary field. A trivial lower bound on ω is 2, and the best known upper bound until recently was ω < 2.376 achieved by Coppersmith and Winograd in 1987. There were two independent improvements on ω, one by Stothers in 2010 who showed that ω < 2.374, and one by myself that ultimately resulted in ω < 2.373. Here I discuss the road to these improvements and conclude with some open questions.
Exploring real-time video interactivity with Scratch

Real-time video interactivity is becoming increasingly popular in today's world with the advent of better and more affordable video input devices. With the recent release of the Microsoft Kinect followed by an official Kinect SDK, there has been an explosion of activity around utilizing this now easily-accessible video sensor data. Many creative uses have surfaced including object recognition, gesture recognition, and more. The audience capable of taking full advantage of these video technologies continues to be a technical crowd, likely with a background in computer science. But what if such video technology were made accessible to a much more diverse crowd? This thesis presents a set of computer vision tools for exploration of the real-time video interactivity space in the context of Scratch (scratch.mit.edu), a graphical block-based programming language accessible to all ages. To decide what functionality to provide to Scratch users, various computer vision algorithms are tested, including object detection, object recognition, face recognition and optical flow. Ultimately, an optical flow implementation is realized and its generative abilities are observed through testing with different user groups.
Quantum kinematics on q-deformed quantum spaces II, Wave functions on position and momentum space

The aim of Part II of this paper is to try to describe wave functions on q-deformed versions of position and momentum space. This task is done within the framework developed in Part I of the paper. In order to make Part II self-contained the most important results of Part I are reviewed. Then it is shown that q-deformed exponentials and q-deformed delta functions play the role of momentum and position eigenfunctions, respectively. Their completeness and orthonormality relations are derived. For both bases of eigenfunctions matrix elements of position and momentum operators are calculated. A q-deformed version of the spectral decomposition of multiplication operators is discussed and q-analogs of Heaviside functions are proposed. Interpreting the results from the point of view provided by the concept of quasipoints gives the formalism a physical meaning. The definition of expectation values and the calculation of probability densities are explained in detail. Finally, it is outlined how the considerations so far carry over to antisymmetrized spaces.
3D hair model from small set of images

We present a system for creating hair model that matches a user's hairstyle. The model consists of guide hair strands and is ready to be used in a real-time hair simulator. Our goal differs from most previous work which aims to create realistic high resolution hair for off-line applications or create mesh of exterior of hair volume for image manipulation. Our primary aim is for user to be able to put his/her hairstyle into game or other real-time applications. By taking photos in 8 views of the user's head using a smart phone camera and segmenting images with some easy to use tools, the player will obtain his/her own hair model in NVIDIA's HairWorks, which is a hair simulator used in many games. We show a number of results demonstrating the capabilities of our system in this paper.
Evaluation of the Role of LRRK2 Gene in Parkinson’s Disease in an East Indian Cohort

Leucine rich repeat kinase 2 (LRRK2) gene defects cause Parkinson's disease (PD). Recently, LRRK2 has also been shown by genome wide association (GWA) studies to be a susceptibility gene for the disease. In India mutations in LRRK2 is a rare cause of PD. We, therefore, genotyped 64 SNPs across LRRK2 in 161 control samples and finally studied 6 haplotype tagging SNPs for association-based study on 300 cases and 446 ethnically matched controls to explore the potential role of LRRK2 as a susceptibility gene in PD for East Indians. We did not find any significant allele/ genotype or haplotype associations with PD suggesting that common genetic variants within LRRK2 play limited role in modulating PD among East Indians. In addition, we also screened for the common mutations (viz. p.R1441C, p.R1441G, p.R1441H, p.Y1699C, p.G2019S), and a risk variant common among Asians (p.G2385R) but did not observe any of the above mentioned variants in our cohort. Our study, therefore, strongly suggests that LRRK2 has minimal role as a candidate and susceptibility gene in PD pathogenesis among East Indians.