nils-herrmann/clean_scirepeval huggingface dataset:
Characterizing gene sets using discriminative random walks with restart on heterogeneous biological networks

Analysis of co-expressed gene sets typically involves testing for enrichment of different annotations or 'properties' such as biological processes, pathways, transcription factor binding sites, etc., one property at a time. This common approach ignores any known relationships among the properties or the genes themselves. It is believed that known biological relationships among genes and their many properties may be exploited to more accurately reveal commonalities of a gene set. Previous work has sought to achieve this by building biological networks that combine multiple types of gene-gene or gene-property relationships, and performing network analysis to identify other genes and properties most relevant to a given gene set. Most existing network-based approaches for recognizing genes or annotations relevant to a given gene set collapse information about different properties to simplify (homogenize) the networks.



We present a network-based method for ranking genes or properties related to a given gene set. Such related genes or properties are identified from among the nodes of a large, heterogeneous network of biological information. Our method involves a random walk with restarts, performed on an initial network with multiple node and edge types that preserve more of the original, specific property information than current methods that operate on homogeneous networks. In this first stage of our algorithm, we find the properties that are the most relevant to the given gene set and extract a subnetwork of the original network, comprising only these relevant properties. We then re-rank genes by their similarity to the given gene set, based on a second random walk with restarts, performed on the above subnetwork. We demonstrate the effectiveness of this algorithm for ranking genes related to Drosophila embryonic development and aggressive responses in the brains of social animals.



DRaWR was implemented as an R package available at veda.cs.illinois.edu/DRaWR.



blatti@illinois.edu



Supplementary data are available at Bioinformatics online.
A Simple and Fast Incremental Randomized Algorithm for Computing Trapezoidal Decompositions and for Triangulating Polygons

Seidel, R., A simple and fast incremental randomized algorithm for computing trapezoidal decompositions and for triangulating polygons, Computational Geometry: Theory and Applications 1 (1991) 51-64. This paper presents a very simple incremental randomized algorithm for computing the trapezoidal decomposition induced by a set S of n line segments in the plane. If S is given as a simple polygonal chain the expected running time of the algorithm is O(n log* n). This leads to a simple algorithm of the same complexity for triangulating polygons. More generally, if S is presented as a plane graph with k connected components, then the expected running time of the algorithm is O(n log* n + k log n). As a by-product our algorithm creates a search structure of expected linear size that allows point location queries in the resulting trapezoidation in logarithmic expected time. The analysis of the expected performance is elementary and straightforward. All expectations are with respect to ‘coinflips’ generated by the algorithm and are not based on assumptions about the geometric distribution of the input.
An Extensible SAT-solver

In this article, we present a small, complete, and efficient SAT-solver in the style of conflict-driven learning, as exemplified by CHAFF. We aim to give sufficient details about implementation to enable the reader to construct his or her own solver in a very short time. This will allow users of SAT-solvers to make domain specific extensions or adaptions of current state-of-the-art SAT-techniques, to meet the needs of a particular application area. The presented solver is designed with this in mind, and includes among other things a mechanism for adding arbitrary boolean constraints. It also supports solving a series of related SAT-problems efficiently by an incremental SAT-interface.
Forecasting stock market movement direction with support vector machine

Support vector machine (SVM) is a very speci1c type of learning algorithms characterized by the capacity control of the decision function, the use of the kernel functions and the sparsity of the solution. In this paper, we investigate the predictability of 1nancial movement direction with SVM by forecasting the weekly movement direction of NIKKEI 225 index. To evaluate the forecasting ability of SVM, we compare its performance with those of Linear Discriminant Analysis, Quadratic Discriminant Analysis and Elman Backpropagation Neural Networks. The experiment results show that SVM outperforms the other classi1cation methods. Further, we propose a combining model by integrating SVM with the other classi1cation methods. The combining model performs best among all the forecasting methods. ? 2004
The Discrete Cosine Transform ( DCT ) : Theory and Application 1

Transform coding constitutes an integral component of contemporary image/video processing applications. Transform coding relies on the premise that pixels in an image exhibit a certain level of correlation with their neighboring pixels. Similarly in a video transmission system, adjacent pixels in consecutive frames 2 show very high correlation. Consequently, these correlations can be exploited to predict the value of a pixel from its respective neighbors. A transformation is, therefore, defined to map this spatial (correlated) data into transformed (uncorrelated) coefficients. Clearly, the transformation should utilize the fact that the information content of an individual pixel is relatively small i.e., to a large extent visual contribution of a
Multi-Objective Resource Allocation for Secure Communication in Cognitive Radio Networks with Wireless Information and Power Transfer

In this paper, we study resource allocation for multiuser multiple-input single-output (MISO) secondary communication systems with multiple system design objectives. We consider cognitive radio (CR) networks where the secondary receivers are able to harvest energy from the radio frequency when they are idle. The secondary system provides simultaneous wireless power and secure information transfer to the secondary receivers. We propose a multi-objective optimization framework for the design of a Pareto optimal resource allocation algorithm based on the weighted Tchebycheff approach. In particular, the algorithm design incorporates three important system design objectives: total transmit power minimization, energy harvesting efficiency maximization, and interference power leakage-totransmit power ratio minimization. The proposed framework takes into account a quality of service (QoS) requirement regarding communication secrecy in the secondary system and the imperfection of the channel state information (CSI) of potential eavesdroppers (idle secondary receivers and primary receivers) at the secondary transmitter. The proposed framework includes total harvested power maximization and interference power leakage minimization as special cases. The adopted multiobjective optimization problem is non-convex and is recast as a convex optimization problem via semidefinite programming (SDP) relaxation. It is shown that the global optimal solution of the original problem can be constructed by exploiting both the primal and the dual optimal solutions of the SDP relaxed problem. Besides, two suboptimal resource allocation schemes for the case when the solution of the dual problem is unavailable for constructing the optimal solution are proposed. Numerical results not only demonstrate the close-to-optimal performance of the proposed suboptimal schemes, but also unveil an interesting trade-off between the considered conflicting system design objectives.
Care-O-bot® 3 - creating a product vision for service robot applications by integrating design and technology

This paper introduces Care-O-bot® 3, a highly integrated and compact service robot with manipulation, navigation and vision capabilities. In particular, Care-O-bot® 3 combines the best of available technology including a 7 DOF light-weight arm, an omnidirectional platform and many high-end sensors along with a sustainable, end user oriented design concept enabling many interaction possibilities.
There is more to context than location

Context is a key issue in interaction between human and computer, describing the surrounding facts that add meaning. In mobile computing research published the parameter location is most often used to approximate context and to implement context-aware applications. We propose that ultra-mobile computing, characterized by devices that are operational and operated while on the move (e.g. PDAs, mobile phones, wearable computers), can significantly benefit from a wider notion of context. To structure the field we introduce a working model for context, discuss mechanisms to acquire context beyond location, and application of context-awareness in ultra-mobile computing. We investigate the utility of sensors for context-awareness and present two prototypical implementations-a light sensitive display and an orientation aware PDA interface. The concept is then extended to a model for sensor fusion to enable more sophisticated context recognition. Based on an implementation of the model an experiment is described and the feasibility of the approach is demonstrated. Further we explore fusion of sensors for acquisition of information on more sophisticated contexts. 1 Introduction Context is " that which surrounds, and gives meaning to something else " =. Various areas of computer science have been investigating this concept over the last 40 years, to relate information processing and communication to aspects of the situations in which such processing occurs. Most notably, context is a key concept in Natural Language Processing and more generally in Human-Computer Interaction. For instance, state of the art graphical user interfaces use context to adapt menus to contexts such as user preference and dialogue status. A new domain, in which context currently receives growing attention, is mobile computing. While a first wave of mobile computing was based on portable general-purpose computers and primarily focussed on location transparency, a second wave is now based on ultra-mobile devices and an interest in relating these to their surrounding situation of usage. Ultra-mobile devices are a new class of small mobile computer, defined as computing devices that are operational and operated while on the move, and characterized by a shift from general-purpose computing to task-specific support. Ultra-mobile devices comprise for instance Personal Digital Assistants (PDAs), mobile phones, and wearable computers. A primary concern of context-awareness in mobile computing is awareness of the physical environment surrounding a user and their ultra-mobile device. In recent work, this concern has been addressed by implementation of location-awareness, for instance based on global positioning, or the use of beacons. Location …
ASR-based corrective feedback on pronunciation: does it really work?

We studied a group of immigrants who were following regular, teacher-fronted Dutch classes, and who were assigned to three groups using either a) Dutch CAPT, an ASR-based Computer Assisted Pronunciation Training (CAPT) system that provides feedback on a number of Dutch speech sounds that are problematic for L2 learners b) a CAPT system without feedback c) no CAPT system. Participants were tested before and after the training. The results show that the ASR-based feedback was effective in correcting the errors addressed in the training.
A wireless low-power pulse oximetry system for patient telemonitoring

In this paper we present the realization of a wireless low power pulse oximetry telemonitoring system capable to measure and transmit patient's arterial blood-oxygen saturation (SpO2) level and heart rate (HR). The use of the proposed system is suitable for continuous long-time patient monitoring, as a part of a diagnostic procedure. The patient can achieve medical assistance of a chronic condition, or can be supervised during recovery from an acute event or surgical procedure. We use commercially available devices, low power microcontrollers and RF transceivers that perform the measurements (SpO2 and HR) and transmit them to the patient monitoring device. The monitoring device, in form of a PDA that running a personal SpO2 monitor application, receives the SpO2 level and HR, activates the alarms when the monitored parameters exceed the preset limits, and communicates periodically to the telemonitoring server by using WiFi or GSM/GPRS connection.
A machine learning approach for viral genome classification

Advances in cloning and sequencing technology are yielding a massive number of viral genomes. The classification and annotation of these genomes constitute important assets in the discovery of genomic variability, taxonomic characteristics and disease mechanisms. Existing classification methods are often designed for specific well-studied family of viruses. Thus, the viral comparative genomic studies could benefit from more generic, fast and accurate tools for classifying and typing newly sequenced strains of diverse virus families. Here, we introduce a virus classification platform, CASTOR, based on machine learning methods. CASTOR is inspired by a well-known technique in molecular biology: restriction fragment length polymorphism (RFLP). It simulates, in silico, the restriction digestion of genomic material by different enzymes into fragments. It uses two metrics to construct feature vectors for machine learning algorithms in the classification step. We benchmark CASTOR for the classification of distinct datasets of human papillomaviruses (HPV), hepatitis B viruses (HBV) and human immunodeficiency viruses type 1 (HIV-1). Results reveal true positive rates of 99%, 99% and 98% for HPV Alpha species, HBV genotyping and HIV-1 M subtyping, respectively. Furthermore, CASTOR shows a competitive performance compared to well-known HIV-1 specific classifiers (REGA and COMET) on whole genomes and pol fragments. The performance of CASTOR, its genericity and robustness could permit to perform novel and accurate large scale virus studies. The CASTOR web platform provides an open access, collaborative and reproducible machine learning classifiers. CASTOR can be accessed at http://castor.bioinfo.uqam.ca .
Automatic Word Sense Discrimination

This paper presents context-group discrimination, a disambiguation algorithm based on clustering. Senses are interpreted as groups (or clusters) of similar contexts of the ambiguous word. Words, contexts, and senses are represented in Word Space, a high-dimensional, real-valued space in which closeness corresponds to semantic similarity. Similarity in Word Space is based on second-order co-occurrence: two tokens (or contexts) of the ambiguous word are assigned to the same sense cluster if the words they co-occur with in turn occur with similar words in a training corpus. The algorithm is automatic and unsupervised in both training and application: senses are induced from a corpus without labeled training instances or other external knowledge sources. The paper demonstrates good performance of context-group discrimination for a sample of natural and artificial ambiguous words.
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

We present a novel and practical deep fully convolutional neural network architecture for semantic pixel-wise segmentation termed SegNet. This core trainable segmentation engine consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer. The architecture of the encoder network is topologically identical to the 13 convolutional layers in the VGG16 network  . The role of the decoder network is to map the low resolution encoder feature maps to full input resolution feature maps for pixel-wise classification. The novelty of SegNet lies is in the manner in which the decoder upsamples its lower resolution input feature map(s). Specifically, the decoder uses pooling indices computed in the max-pooling step of the corresponding encoder to perform non-linear upsampling. This eliminates the need for learning to upsample. The upsampled maps are sparse and are then convolved with trainable filters to produce dense feature maps. We compare our proposed architecture with the widely adopted FCN [2] and also with the well known DeepLab-LargeFOV  , DeconvNet  architectures. This comparison reveals the memory versus accuracy trade-off involved in achieving good segmentation performance. SegNet was primarily motivated by scene understanding applications. Hence, it is designed to be efficient both in terms of memory and computational time during inference. It is also significantly smaller in the number of trainable parameters than other competing architectures and can be trained end-to-end using stochastic gradient descent. We also performed a controlled benchmark of SegNet and other architectures on both road scenes and SUN RGB-D indoor scene segmentation tasks. These quantitative assessments show that SegNet provides good performance with competitive inference time and most efficient inference memory-wise as compared to other architectures. We also provide a Caffe implementation of SegNet and a web demo at
Supporting data aspects in pig latin

In this paper we apply the aspect-oriented programming (AOP) paradigm to Pig Latin, a dataflow language for cloud computing, used primarily for the analysis of massive data sets. Missing from Pig Latin is support for cross-cutting data concerns. Data, like code, has cross-cutting concerns such as versioning, privacy, and reliability. AOP techniques can be used to weave metadata around Pig data. The metadata imbues the data with additional semantics that must be observed in the evaluation of Pig Latin programs. In this paper we show how to modify Pig Latin to process data woven together with metadata. The data weaver is a layer that maps a Pig Latin program to an augmented Pig Latin program using Pig Latin templates or patterns. We also show how to model additional levels of advice, i.e., meta-metadata.
Ranking in context using vector spaces

This paper presents a principled approach to the problem of retrieval in context. The notion of basis of a vector space was introduced previously to describe context. Any basis vectors represents a distinct piece of context, such as time, space, or word meaning. A vector is generated by a basis just as an informative object or an information need is generated in a context. As a consequence a different basis generates a different vector as a different context would generate different information needs or informative objects. Also the Vector Space Model (VSM) describes information needs and informative objects as query vectors and document vectors, respectively. However the VSM assumes that there is a unique basis, which is the set of versors and always generates the same vector provided the same coefficients. Thus a drawback of the VSM is that the vectors are insensitive to context, i.e. they are generated and ranked in the same way independently of the context in which information need and informative objects are. This paper also proposes a function to rank documents in context. Since a basis spans a subspace, which includes all the vectors of object being in the same context, the ranking function is a distance measure between the document vector and the subspace. Even though ranking is still based on an inner product between two vectors, the basic difference is that projection and distance depend on the basis, i.e. on the pieces of context and then ultimately on context. Since an informative object can be produced by different contexts, different bases can arise and then ranking can change. Mathematically, object vector generation is given by x = p1b1 + · · · p k b k = x = B · p, where B is a n × k (k ≤ n) complex matrix and p is a k × 1 real vector. The b's are independent vectors and as such form a basis B of a subspace L(B) of C n. The basis generates all the vectors in L(B) and every vector in it describes an informative object produced within the context described by B. It should be clear that every vector x in L(B) is entirely contained in the subspace spanned by B. Any other vector of the vector space may not be entirely contained in the subspace L(B) and may be more or less close to it. A vector y …
Control of a UAV and a UGV cooperating to manipulate an object

This paper focuses on the control of a system composed of an Unmanned Aerial Vehicle (UAV) and an Unmanned Ground Vehicle (UGV) which cooperate to manipulate an object. The two units are subject to actuator saturations and cooperate to move the object to a desired pose, characterized by its position and inclination. The paper proposes a control strategy where the ground vehicle is tasked to deploy the object to a certain position, whereas the aerial vehicle adjusts its inclination. The ground vehicle is governed by a saturated proportional-derivative control law. The aerial vehicle is regulated by means of a cascade control specifically designed for this problem that is able to exploit the mechanical interconnection. The stability of the overall system is proved through Input-to-State Stability and Small Gain theorem arguments. To solve the problem of constraints satisfaction, a nonlinear Reference Governor scheme is implemented. Numerical simulations are provided to demonstrate the effectiveness of the proposed method.
A UML profile for goal-oriented and use case-driven representation of NFRs and FRs

In order for a software system to be of value, it should meet both functional requirements (FRs) and non-functional requirements (NFRs). Concerning FRs, UML has been used as the de facto object-oriented analysis and design notation. Concerning NFRs, the NFR framework extends, and complements, UML, by treating NFRs as potentially conflicting or synergistic softgoals to be achieved, thereby allowing for the consideration of alternatives and analysis of trade-offs among the alternatives. Albeit the complementary nature of the two, UML and the NFR framework offer two different notations -syntactically, semantically and visually, which makes it difficult to produce requirements models that integrate both FRs and NFRs. In this paper, we propose an integrated modeling language by extending UML with the NFR framework using the standard extension mechanism called UML profile where we define a metamodel to represent the concepts in the NFR framework and identify the extension points for integrating the two notations. We also show how CASE tools may use this profile in building an integrated requirements model based on a model of the well known London ambulance case study.
An ultra-low-power power management IC for energy-scavenged Wireless Sensor Nodes

A power interface IC is designed and demonstrated to convert and manage power for a wireless tire pressure sensor node. The IC includes two switched-capacitor DC-DC converters to supply power to the various components of the sensor at their appropriate voltages. The design of the two integrated converters is discussed, including the optimization of capacitors and power transistors. The losses due to parasitic capacitances are analyzed. Two gate drive techniques are used to drive the gates of the floating triple-well transistors. A synchronous rectifier efficiently harvests energy from an electromagnetic shaker and control circuitry regulates the output voltage while minimizing power consumption. The two converters achieve efficiencies of approximately 84% while the synchronous rectifier achieves an efficiency of 88%.