nils-herrmann/clean_scirepeval huggingface dataset:
A Powerful Generative Model Using Random Weights for the Deep Image Representation

To what extent is the success of deep visualization due to the training? Could we do deep visualization using untrained, random weight networks? To address this issue, we explore new and powerful generative models for three popular deep visualization tasks using untrained, random weight convolutional neural networks. First we invert representations in feature spaces and reconstruct images from white noise inputs. The reconstruction quality is statistically higher than that of the same method applied on well trained networks with the same architecture. Next we synthesize textures using scaled correlations of representations in multiple layers and our results are almost indistinguishable with the original natural texture and the synthesized textures based on the trained network. Third, by recasting the content of an image in the style of various artworks, we create artistic images with high perceptual quality, highly competitive to the prior work of Gatys et al. on pretrained networks. To our knowledge this is the first demonstration of image representations using untrained deep neural networks. Our work provides a new and fascinating tool to study the representation of deep network architecture and sheds light on new understandings on deep visualization.
Bayesian Quadratic Network Game Filters

A repeated network game where agents have quadratic utilities that depend on information externalities-an unknown underlying state-as well as payoff externalities-the actions of all other agents in the network-is considered. Agents play Bayesian Nash Equilibrium strategies with respect to their beliefs on the state of the world and the actions of all other nodes in the network. These beliefs are refined over subsequent stages based on the observed actions of neighboring peers. This paper introduces the Quadratic Network Game (QNG) filter that agents can run locally to update their beliefs, select corresponding optimal actions, and eventually learn a sufficient statistic of the network's state. The QNG filter is demonstrated on a Cournot market competition game and a coordination game to implement navigation of an autonomous team.
An architecture for scalable, universal speech recognition

This thesis describes MultiSphinx, a concurrent architecture for scalable, low-latency automatic speech recognition. We first consider the problem of constructing a universal “core” speech recognizer on top of which domain and task specific adaptation layers can be constructed. We then show that when this problem is restricted to that of expanding the search space from a “core” vocabulary to a superset of this vocabulary across multiple passes of search, it allows us to effectively “factor” a recognizer into components of roughly equal complexity. We present simple but effective algorithms for constructing the reduced vocabulary and associated statistical language model from an existing system. Finally, we describe the MultiSphinx decoder architecture, which allows multiple passes of recognition to operate concurrently and incrementally, either in multiple threads in the same process, or across multiple processes on separate machines, and which allows the best possible partial results, including confidence scores, to be obtained at any time during the recognition process.
On the customer lifetime value: a mathematical perspective

The customer lifetime value (CLV) is an important concept increasingly considered in the field of general marketing and in the management of firms, of organizations to increase the captured profitability. It represents the total value that a customer produces during his or her lifetime, or better represents the measure of the potential profit generating by a customer. The companies use the customer lifetime value to segment customers, analyze probability of churn, allocate resources or formulate strategies and, therefore, they increasingly derive revenue from the creation and from sustenance of long-term relationships with their customers. For this reason, the customer lifetime value is increasingly considered a touchstone for the management of customer relationships. In this article, the authors deepen the concept and use of customer lifetime value and present some mathematical models for its determination. There is manymodels for this purpose but most of them are theoretic, complex and not applicable. Though not exhaustive, the major contribution of this paper is that it provides a general mathematical formulation to estimate the CLV and that it has a context less specific compared to papers, present in literature, on the customer lifetime.
Complimentary Memory Technologies to Reform the Homogeneity , Energy Landscape , and Volatility of Future Memory Hierarchies

With the limits of transistor size rapidly approaching and the memory wall approaching us at unfathomable speeds, a need to revisit our existing memory technologies has arisen. Fortunately, there are new non-volatile memory (NVM) technologies on the horizon showing great promise. In this paper we will examine some of the new NVM technologies being developed. We will present the basic characteristics of these new technologies as well as the properties that make them unique and the possibilities for the future they offer. non-volatile; volatile; memory; memristor; RRAM; MRAM; SRAM; STT-RAM; PCM; PRAM
Digital Dental X-Ray Image Segmentation and Feature Extraction

The process of analysis of such images is important in order to improve quantify medical imaging systems. It is significant to analysis the dental x-ray images we need features of image. In this paper we present a method for segmentation and feature extraction of dental x-ray images. The proposed method has been implemented by using level-set method for segmentation after image enhancement and illustrate contour for teeth to complete the segmentation step. Furthermore, we extracted multiple features of dental x-ray images using texture statistics techniques by gray-level co-occurrence matrix. Extracted data can perform to obtain the teeth measurements for automatic dental systems such human identification or dental diagnosis systems. Preparatory experiments show the significance of the proposed method to extract teeth from an x-ray image.
Fractal Islamic Geometric Patterns Based on Arrangements of { n / 2 } Stars

Within traditional Islamic geometric design, there is a small but distinct subset of patterns which incorporate notions of self-similarity. Such patterns typically use exactly two overlapping levels of self-similarity, with each level comprising similar motifs at two different, related scales. Recently I have constructed a large number of fractal Islamic patterns that pursue a different notion of self-similarity, simultaneously incorporating motifs at multiple— often, infinite—scales within a single pattern. I will first highlight the ways in which these patterns are distinct in nature from related work, both traditional and contemporary. Then, I will explain the details of the technique I have devised for using arrangements of {n/2} stars as a basis for discovering and constructing a wide variety of new fractal Islamic patterns.
Fast simulation of mass-spring systems

We describe a scheme for time integration of mass-spring systems that makes use of a solver based on block coordinate descent. This scheme provides a fast solution for classical linear (Hookean) springs. We express the widely used implicit Euler method as an energy minimization problem and introduce spring directions as auxiliary unknown variables. The system is globally linear in the node positions, and the non-linear terms involving the directions are strictly local. Because the global linear system does not depend on run-time state, the matrix can be pre-factored, allowing for very fast iterations. Our method converges to the same final result as would be obtained by solving the standard form of implicit Euler using Newton's method. Although the asymptotic convergence of Newton's method is faster than ours, the initial ratio of work to error reduction with our method is much faster than Newton's. For real-time visual applications, where speed and stability are more important than precision, we obtain visually acceptable results at a total cost per timestep that is only a fraction of that required for a single Newton iteration. When higher accuracy is required, our algorithm can be used to compute a good starting point for subsequent Newton's iteration.
Frames: A Corpus for Adding Memory to Goal-Oriented Dialogue Systems

This paper proposes a new dataset, Frames, composed of 1369 human-human dialogues with an average of 15 turns per dialogue. This corpus contains goal-oriented dialogues between users who are given some constraints to book a trip and assistants who search a database to find appropriate trips. The users exhibit complex decision-making behaviour which involve comparing trips, exploring different options, and selecting among the trips that were discussed during the dialogue. To drive research on dialogue systems towards handling such behaviour, we have annotated and released the dataset and we propose in this paper a task called frame tracking. This task consists of keeping track of different semantic frames throughout each dialogue. We propose a rule-based baseline and analyse the frame tracking task through this baseline.
Defining Creativity: Finding Keywords for Creativity Using Corpus Linguistics Techniques

A computational system that evaluates creativity needs guidance on what creativity actually is. It is by no means straightforward to provide a computer with a formal definition of creativity; no such definition yet exists and viewpoints in creativity literature vary as to what the key components of creativity are considered to be. This work combines several viewpoints for a more general consensus of how we define creativity, using a corpus linguistics approach. 30 academic papers from various academic disciplines were analysed to extract the most frequently used words and their frequencies in the papers. This data was statistically compared with general word usage in written English. The results form a list of words that are significantly more likely to appear when talking about creativity in academic texts. Such words can be considered keywords for creativity, guiding us in uncovering key sub-components of creativity which can be used for computational assessment of creativity.
Idea Generation Techniques among Creative Professionals

The creative process has been a key topic research over the past century, but it wasn't until the last decade that creativity became a hot topic of research in the HCI. It is an important commodity to businesses and individuals alike spawning numerous research studies in business, psychology and design. However, it wasn't until recently that researchers became interested in developing technologies to support creative behavior. This article outlines the role of creativity in design from the designer's perspective, provides a model for the creative process and provides a foundation and direction for future creativity support research by identifying nineteen idea generation techniques utilized by creative professionals.
Human model evaluation in interactive supervised learning

Model evaluation plays a special role in interactive machine learning (IML) systems in which users rely on their assessment of a model's performance in order to determine how to improve it. A better understanding of what model criteria are important to users can therefore inform the design of user interfaces for model evaluation as well as the choice and design of learning algorithms. We present work studying the evaluation practices of end users interactively building supervised learning systems for real-world gesture analysis problems. We examine users' model evaluation criteria, which span conventionally relevant criteria such as accuracy and cost, as well as novel criteria such as unexpectedness. We observed that users employed evaluation techniques---including cross-validation and direct, real-time evaluation---not only to make relevant judgments of algorithms' performance and interactively improve the trained models, but also to learn to provide more effective training data. Furthermore, we observed that evaluation taught users about what types of models were easy or possible to build, and users sometimes used this information to modify the learning problem definition or their plans for using the trained models in practice. We discuss the implications of these findings with regard to the role of generalization accuracy in IML, the design of new algorithms and interfaces, and the scope of potential benefits of incorporating human interaction in the design of supervised learning systems.
A tutorial on the dynamics and control of wind turbines and wind farms

Wind energy is currently the fastest-growing energy source in the world, with a concurrent growth in demand for the expertise of engineers and researchers in the wind energy field. There are still many unsolved challenges in expanding wind power, and there are numerous problems of interest to systems and control researchers. In this paper, we first review the basic structure of wind turbines and then describe wind turbine control systems and control loops. Of great interest are the generator torque and blade pitch control systems, where significant performance improvements are achievable with more advanced systems and control research. We describe recent developments in advanced controllers for wind turbines and wind farms, and we also outline many open problems in the areas of modeling and control of wind turbines.
Liquid cooled permanent-magnet traction motor design considering temporary overloading

This paper focuses on traction motor design in a variable speed drive of an Electric Mini Bus, aiming to deliver high mean mechanical power over the whole speed range, using single gear transmission. Specific design considerations aim to minimize oversizing, by utilizing temporary overloading capability using liquid cooled motor housing. Constant toque - constant power control strategy is adopted by appropriately programming the over-torque and field weakening operation areas of the motor. A good quantitative analysis between over-torque, rated and field weakening operation areas, is given, focusing on efficiency and paying particular attention to iron losses both on stator and rotor. The motor has been designed in order to meet traction application specifications and allow operation in higher specific electric loading to increase power density.
Medusa: Simplified Graph Processing on GPUs

Graphs are common data structures for many applications, and efficient graph processing is a must for application performance. Recently, the graphics processing unit (GPU) has been adopted to accelerate various graph processing algorithms such as BFS and shortest paths. However, it is difficult to write correct and efficient GPU programs and even more difficult for graph processing due to the irregularities of graph structures. To simplify graph processing on GPUs, we propose a programming framework called Medusa which enables developers to leverage the capabilities of GPUs by writing sequential C/C++ code. Medusa offers a small set of user-defined APIs and embraces a runtime system to automatically execute those APIs in parallel on the GPU. We develop a series of graph-centric optimizations based on the architecture features of GPUs for efficiency. Additionally, Medusa is extended to execute on multiple GPUs within a machine. Our experiments show that 1) Medusa greatly simplifies implementation of GPGPU programs for graph processing, with many fewer lines of source code written by developers and 2) the optimization techniques significantly improve the performance of the runtime system, making its performance comparable with or better than manually tuned GPU graph operations.
The risk of misinterpreting genital signs of sexual abuse in cadavers: a case report

The significance of genital findings in a case of suspected child sexual abuse has been widely debated in the past decades, as shown by the different classifications available in literature. In the case of postmortem examination, the search for signs of sexual abuse is considerably more difficult because of the superimposition of postmortem modifications, which may determine tissue modifications that can be mistaken for traumatic lesions. This study aims at reporting a case where presumed findings of the first autopsy were denied by histological analysis; in detail, what looked like a possible bruise of the hymen was correctly recognized as hypostasis (livor) of the hymenal tissue by histological analysis. This case report suggests caution in the analysis and discussion of genital lesions found during postmortem examination since the superimposition of cadaveric modifications may radically modify the morphology of soft tissues.
Viewpoint Selection using Viewpoint Entropy

Computation of good viewpoints is important in several fields: computational geometry, visual servoing, robot motion, graph drawing, etc. In addition, selection of good views is rapidly becoming a key issue in computer graphics due to the new techniques of Image Based Rendering. Although there is no consensus about what a good view means in Computer Graphics, the quality of a viewpoint is intuitively related to how much information it gives us about a scene. In this paper we use the theoretical basis provided by Information Theory to define a new measure, viewpoint entropy, that allows us to compute good viewing positions automatically. We also show how it can be used to select a set of N good views of a scene for scene understanding. Finally, we design an algorithm that uses this measure to explore automatically objects or scenes.
Binary Phase Detector Gain in Bang-Bang Phase-Locked Loops With DCO Jitter

Bang-bang phase-locked loops are hard nonlinear systems due to the nonlinearity introduced by the binary phase detector (BPD). In the presence of jitter, the nonlinear loop is typically analyzed by linearizing the BPD and applying linear transfer functions in the analysis. In contrast to a linear phase detector, the linearized gain of a BPD depends on the rms jitter and the type of jitter (either nonaccumulative or accumulative). Previous works considered the case of nonaccumulative reference clock jitter and showed that the BPD gain is inversely proportional to the rms jitter when the latter is small or large. In this brief, we consider the case of accumulative digitally-controlled oscillator (DCO) jitter and derive an asymptotic closed-form expression for the BPD gain, which becomes exact in the limit of small and large jitter. Contrary to the reference clock jitter case, the BPD gain is constant for small DCO jitter and is inversely proportional to the square of jitter for large DCO jitter; in the latter case, the timing jitter has a normal Laplace distribution.
Liquid: A detection-resistant covert timing channel based on IPD shaping

A DETECTION RESISTANT COVERT TIMING CHANNEL BASED ON IPD SHAPING ROBERT J. WALLS, M.S. The University of Texas at Arlington, 2009 Supervising Professor: Matthew Wright Covert timing channels provide a way to surreptitiously leak information from an entity in a higher-security level to an entity in a lower level. The difficulty of detecting or eliminating such channels makes them a desirable choice for adversaries that value stealth over throughput. When one considers the possibility of such channels transmitting information across network boundaries, the threat becomes even more acute. A promising technique for detecting covert timing channels focuses on using entropy-based tests. This method is able to reliably detect known covert timing channels by using a combination of entropy and conditional entropy to detect anomalies in shape and regularity, respectively. This dual approach is intended to make entropy-based detection robust against both current and future channels. In this work, we show that entropy-based detection can be defeated by a channel that intelligently manipulates the metrics used for detection. Specifically, we propose a new covert channel that uses a portion of the inter-packet delays in a compromised stream to smooth out the distortions detected by the entropy test. Our experimental results suggest that this channel can successfully evade entropy-based detection and other known tests while maintaining reasonable throughput. Furthermore,
Comprehensive Survey on T-SDN: Software-Defined Networking for Transport Networks

Paradoxically, with an ever-increasing traffic demand, today transport-network operators experience a progressive erosion of their margins. The alarms of change are set, and software define networking (SDN) is coming to the rescue with the promise of reducing capital expenditures and operational expenses. Driven by economic needs and network innovation facilities, today transport SDN (T-SDN) is a reality. It gained big momentum in the last years, however, in the networking industry, the transport network will be perhaps the last segment to embrace SDN, mainly due to the heterogeneous nature and complexity of the optical equipment composing it. This survey guides the reader through a fascinating technological adventure that provides an organic analysis of the T-SDN development and evolution considering contributions from: academic research, standardization bodies, industrial development, open source projects, and alliances among them. After creating a comprehensive picture of T-SDN, we provide an analysis of many open issues that are expected to need significant future work, and give our vision in this path toward a fully programmable and dynamic transport network.
Faster Dynamic Programming for Markov Decision Processes

OF THESIS FASTER DYNAMIC PROGRAMMING FOR MARKOV DECISION PROCESSES Markov decision processes (MDPs) are a general framework used by Artificial Intelligence (AI) researchers to model decision theoretic planning problems. Solving real world MDPs has been a major and challenging research topic in the AI literature. This paper discusses two main groups of approaches in solving MDPs. The first group of approaches combines the strategies of heuristic search and dynamic programming to expedite the convergence process. The second makes use of graphical structures in MDPs to decrease the effort of classic dynamic programming algorithms. Two new algorithms proposed by the author, MBLAO* and TVI, are described here.
Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images

Linear registration and motion correction are important components of structural and functional brain image analysis. Most modern methods optimize some intensity-based cost function to determine the best registration. To date, little attention has been focused on the optimization method itself, even though the success of most registration methods hinges on the quality of this optimization. This paper examines the optimization process in detail and demonstrates that the commonly used multiresolution local optimization methods can, and do, get trapped in local minima. To address this problem, two approaches are taken: (1) to apodize the cost function and (2) to employ a novel hybrid global-local optimization method. This new optimization method is specifically designed for registering whole brain images. It substantially reduces the likelihood of producing misregistrations due to being trapped by local minima. The increased robustness of the method, compared to other commonly used methods, is demonstrated by a consistency test. In addition, the accuracy of the registration is demonstrated by a series of experiments with motion correction. These motion correction experiments also investigate how the results are affected by different cost functions and interpolation methods.