nils-herrmann/clean_scirepeval huggingface dataset:
On the Number of Linear Regions of Deep Neural Networks

We study the complexity of functions computable by deep feedforward neural networks with piecewise linear activations in terms of the symmetries and the number of linear regions that they have. Deep networks are able to sequentially map portions of each layer’s input-space to the same output. In this way, deep models compute functions that react equally to complicated patterns of different inputs. The compositional structure of these functions enables them to re-use pieces of computation exponentially often in terms of the network’s depth. This paper investigates the complexity of such compositional maps and contributes new theoretical results regarding the advantage of depth for neural networks with piecewise linear activation functions. In particular, our analysis is not specific to a single family of models, and as an example, we employ it for rectifier and maxout networks. We improve complexity bounds from pre-existing work and investigate the behavior of units in higher layers.
Analysis of various Software Process Models

This paper conducts an analysis of various software development approaches, i.e. sequential, incremental, evolutionary, specialized and agile. An example of each approach is considered – Waterfall model (sequential approach), Incremental Model (incremental approach), Spiral Model (evolutionary approach), Formal Methods Model (specialized approach), Extreme Programming Model (agile approach) and RUP. This paper elaborates these models, as well as, it compares and contrasts between these different models.
A Compact, Low Input Capacitance Neural Recording Amplifier

Conventional capacitively coupled neural recording amplifiers often present a large input load capacitance to the neural signal source and hence take up large circuit area. They suffer due to the unavoidable trade-off between the input capacitance and chip area versus the amplifier gain. In this work, this trade-off is relaxed by replacing the single feedback capacitor with a clamped T-capacitor network. With this simple modification, the proposed amplifier can achieve the same mid-band gain with less input capacitance, resulting in a higher input impedance and a smaller silicon area. Prototype neural recording amplifiers based on this proposal were fabricated in 0.35 μm CMOS, and their performance is reported. The amplifiers occupy smaller area and have lower input loading capacitance compared to conventional neural amplifiers. One of the proposed amplifiers occupies merely 0.056 mm2. It achieves 38.1-dB mid-band gain with 1.6 pF input capacitance, and hence has an effective feedback capacitance of 20 fF. Consuming 6 μW, it has an input referred noise of 13.3 μVrms over 8.5 kHz bandwidth and NEF of 7.87. In-vivo recordings from animal experiments are also demonstrated.
The role of eyebrows in face recognition.

A fundamental challenge in face recognition lies in determining which facial characteristics are important in the identification of faces. Several studies have indicated the significance of certain facial features in this regard, particularly internal ones such as the eyes and mouth. Surprisingly, however, one rather prominent facial feature has received little attention in this domain: the eyebrows. Past work has examined the role of eyebrows in emotional expression and nonverbal communication, as well as in facial aesthetics and sexual dimorphism. However, it has not been made clear whether the eyebrows play an important role in the identification of faces. Here, we report experimental results which suggest that for face recognition the eyebrows may be at least as influential as the eyes. Specifically, we find that the absence of eyebrows in familiar faces leads to a very large and significant disruption in recognition performance. In fact, a significantly greater decrement in face recognition is observed in the absence of eyebrows than in the absence of eyes. These results may have important implications for our understanding of the mechanisms of face recognition in humans as well as for the development of artificial face-recognition systems.
Building the Internet of Things with bluetooth smart

The Internet of Things, or the IoT, is an emerging, disruptive technology that enables physical devices to communicate across disparate networks. IP has been the de facto standard for seamless interconnectivity in the traditional Internet; and piggybacking on the success of IP, 6LoWPAN has been the first standardized technology to realize it for networks of resource-constrained devices. In the recent past Bluetooth Low Energy (BLE) a.k.a Bluetooth Smart a subset of the Bluetooth v4.0 and the latest v4.2 stack, has surfaced as an appealing alternative, with many competing advantages over available low-power communication technologies in the IoT space such as IEEE 802.15.4. However, BLE is a closed standard and lacks open hardware and firmware support, something that hinders innovation and development in this field. In this article, we aim to overcome some of the constraints in BLE’s core building blocks by making three contributions: first, we present the design of a new open hardware platform for BLE; second, we provide a Contiki O.S. port for the new platform; and third, we identify research challenges and opportunities in 6LoWPAN-connected Bluetooth Smart. We believe that the knowledge and insights will facilitate IoT innovations based on this promising technology.
Pseudo-Relevance Feedback Based on Matrix Factorization

In information retrieval, pseudo-relevance feedback (PRF) refers to a strategy for updating the query model using the top retrieved documents. PRF has been proven to be highly effective in improving the retrieval performance. In this paper, we look at the PRF task as a recommendation problem: the goal is to recommend a number of terms for a given query along with weights, such that the final weights of terms in the updated query model better reflect the terms' contributions in the query. To do so, we propose RFMF, a PRF framework based on matrix factorization which is a state-of-the-art technique in collaborative recommender systems. Our purpose is to predict the weight of terms that have not appeared in the query and matrix factorization techniques are used to predict these weights. In RFMF, we first create a matrix whose elements are computed using a weight function that shows how much a term discriminates the query or the top retrieved documents from the collection. Then, we re-estimate the created matrix using a matrix factorization technique. Finally, the query model is updated using the re-estimated matrix. RFMF is a general framework that can be employed with any retrieval model. In this paper, we implement this framework for two widely used document retrieval frameworks: language modeling and the vector space model. Extensive experiments over several TREC collections demonstrate that the RFMF framework significantly outperforms competitive baselines. These results indicate the potential of using other recommendation techniques in this task.
Cognitive synergy: A universal principle for feasible general intelligence

Do there exist general principles, which any system must obey in order to achieve advanced general intelligence using feasible computational resources? Here we propose one candidate: “cognitive synergy,” a principle which suggests that general intelligences must contain different knowledge creation mechanisms corresponding to different sorts of memory (declarative, procedural, sensory/episodic, attentional, intentional); and that these different mechanisms must be interconnected in such a way as to aid each other in overcoming memory-type-specific combinatorial explosions.
Fundamental Green Tradeoffs: Progresses, Challenges, and Impacts on 5G Networks

With years of tremendous traffic and energy consumption growth, green radio has been valued not only for theoretical research interests but also for the operational expenditure reduction and the sustainable development of wireless communications. Fundamental green tradeoffs, served as an important framework for analysis, include four basic relationships: 1) spectrum efficiency versus energy efficiency; 2) deployment efficiency versus energy efficiency; 3) delay versus power; and 4) bandwidth versus power. In this paper, we first provide a comprehensive overview on the extensive on-going research efforts and categorize them based on the fundamental green tradeoffs. We will then focus on research progresses of 4G and 5G communications, such as orthogonal frequency division multiplexing and non-orthogonal aggregation, multiple input multiple output, and heterogeneous networks. We will also discuss potential challenges and impacts of fundamental green tradeoffs, to shed some light on the energy efficient research and design for future wireless networks.
Modelling of capacitor mismatch and non-linearity effects ini charge redistribution SAR ADCs

For embedded high resolution successive approximation ADCs, it is necessary to determine the performance limitation of the CMOS process used for the design. This paper presents a modelling technique for major limitations, i.e. capacitor mismatch and non-linearity effects. The model is besed on Monte Carlo simulations applied to an analytical description of the ADC. Additional effects like charge injection and parasitic capacitance are included. The analytical basis covers different architectures with a fully binary weighted or series-split capacitor array. when comparing our analysis and measurement results to several conventional approaches, a significantly more realistic estimation of the attainable resolution is achieved. The presented results provide guidance in choosing process and circuit structure for the design of SAR ADCs. The model also enbles reliable capacitor sizing early in the design process, i.e. well before actual layout implementation.
Diagonal based feature extraction for handwritten character recognition system using neural network

An off-line handwritten alphabetical character recognition system using multilayer feed forward neural network is described in the paper. A new method, called, diagonal based feature extraction is introduced for extracting the features of the handwritten alphabets. Fifty data sets, each containing 26 alphabets written by various people, are used for training the neural network and twenty different handwritten alphabets characters are used for testing. The proposed recognition system performs quite well yielding higher levels of recognition accuracy compared to the systems employing the conventional horizontal and vertical methods of feature extraction. This system will be suitable for converting handwritten documents into structural text form and recognizing handwritten names.
Estimating fractal dimension

Fractals arise from a variety of sources and have been observed in nature and on computer screens. One of the exceptional characteristics of fractals is that they can be described by a noninteger dimension. The geometry of fractals and the mathematics of fractal dimension have provided useful tools for a variety of scientific disciplines, among which is chaos. Chaotic dynamical systems exhibit trajectories in their phase space that converge to a strange attractor. The fractal dimension of this attractor counts the effective number of degrees of freedom in the dynamical system and thus quantifies its complexity. In recent years, numerical methods have been developed for estimating the dimension directly from the observed behavior of the physical system. The purpose of this paper is to survey briefly the kinds of fractals that appear in scientific research, to discuss the application of fractals to nonlinear dynamical systems, and finally to review more comprehensively the state of the art in numerical methods for estimating the fractal dimension of a strange attractor. Confusion is a word we have invented for an order which is not understood.-Henry Miller, "Interlude," Tropic of Capricorn Numerical coincidence is a common path to intellectual perdition in our quest for meaning. We delight in catalogs of disparate items united by the same number, and often feel in our gut that some unity must underlie it all.
Cloud radio access network (C-RAN): a primer

In the era of mobile Internet, mobile operators are facing pressure on ever-increasing capital expenditures and operating expenses with much less growth of income.  is expected to be a candidate of next generation access network techniques that can solve operators' puzzle. In this article, on the basis of a general survey of C-RAN, we present a novel logical structure of C-RAN that consists of a physical plane, a control plane, and a service plane. Compared to traditional architecture, the proposed C-RAN architecture emphasizes the notion of service cloud, service-oriented resource scheduling and management, thus it facilitates the utilization of new communication and computer techniques. With the extensive computation resource offered by the cloud platform, a coordinated user scheduling algorithm and parallel optimum precoding scheme are proposed, which can achieve better performance. The proposed scheme opens another door to design new algorithms matching well with C-RAN architecture, instead of only migrating existing algorithms from traditional architecture to C-RAN.
Time-series prediction using a local linear wavelet neural network

A local linear wavelet neural network (LLWNN) is presented in this paper. The difference of the network with conventional wavelet neural network (WNN) is that the connection weights between the hidden layer and output layer of conventional WNN are replaced by a local linear model. A hybrid training algorithm of particle swarm optimization (PSO) with diversity learning and gradient descent method is introduced for training the LLWNN. Simulation results for the prediction of time-series show the feasibility and effectiveness of the proposed method.
An audio-visual corpus for speech perception and automatic speech recognition.

An audio-visual corpus has been collected to support the use of common material in speech perception and automatic speech recognition studies. The corpus consists of high-quality audio and video recordings of 1000 sentences spoken by each of 34 talkers. Sentences are simple, syntactically identical phrases such as "place green at B 4 now". Intelligibility tests using the audio signals suggest that the material is easily identifiable in quiet and low levels of stationary noise. The annotated corpus is available on the web for research use.
Information Theoretically Aided Reinforcement Learning for Embodied Agents

Reinforcement learning for embodied agents is a challenging problem. The accumulated reward to be optimized is often a very rugged function, and gradient methods are impaired by many local optimizers. We demonstrate, in an experimental setting, that incorporating an intrinsic reward can smoothen the optimization landscape while preserving the global optimizers of interest. We show that policy gradient optimization for locomotion in a complex morphology is significantly improved when supplementing the extrinsic reward by an intrinsic reward defined in terms of the mutual information of time consecutive sensor readings.
AUTOMATED 3 D OBJECT RECONSTRUCTION VIA MULTI-IMAGE CLOSE-RANGE PHOTOGRAMMETRY

Three important stages within automated 3D object reconstruction via multi-image convergent photogrammetry are image pre-processing, interest point detection for feature-based matching and triangular mesh generation. This paper investigates approaches to each of these. The Wallis filter is initially examined as a candidate image pre-processor to enhance the performance of the FAST interest point operator. The FAST algorithm is then evaluated as a potential means to enhance the speed, robustness and accuracy of interest point detection for subsequent feature-based matching. Finally, the Poisson Surface Reconstruction algorithm for wireframe mesh generation of objects with potentially complex 3D surface geometry is evaluated. The outcomes of the investigation indicate that the Wallis filter, FAST interest operator and Poisson Surface Reconstruction algorithms present distinct benefits in the context of automated image-based object reconstruction. The reported investigation has advanced the development of an automatic procedure for high-accuracy point cloud generation in multi-image networks, where robust orientation and 3D point determination has enabled surface measurement and visualization to be implemented within a single software system.
Mencius: Building Efficient Replicated State Machine for WANs

We present a protocol for general state machine replication – a method that provides strong consistency – that has high performance in a wide-area network. In particular, our protocol Mencius has high throughput under high client load and low latency under low client load even under changing wide-area network environment and client load. We develop our protocol as a derivation from the well-known protocol Paxos. Such a development can be changed or further refined to take advantage of specific network or application requirements.
Security of IoT systems: Design challenges and opportunities

Computer-aided design (CAD), in its quest to facilitate new design revolutions, is again on the brink of changing its scope. Following both historical and recent technological and application trends, one can identify several emerging research and development directions in which CAD approaches and techniques may have major impacts. Among them, due to the potential to fundamentally alter everyday life as well as how science and engineering systems are designed and operated, the Internet of Things (IoT) stands out. IoT also poses an extraordinary system replete with conceptual and technical challenges. For instance, greatly reduced quantitative bounds on acceptable area and energy metrics require qualitative breakthroughs in design and optimization techniques.
 Most likely the most demanding of requirements for the widespread realization of many IoT visions is security. IoT security has an exceptionally wide scope in at least four dimensions. In terms of security scope it includes rarely addressed tasks such as trusted sensing, computation, communication, privacy, and digital forgetting. It also asks for new and better techniques for the protection of hardware, software, and data that considers the possibility of physical access to IoT devices. Sensors and actuators are common components of IoT devices and pose several unique security challenges including the integrity of physical signals and actuating events. Finally, during processing of collected data, one can envision many semantic attacks.
 Our strategic objective is to provide an impetus for the development of IoT CAD security techniques. We start by presenting a brief survey of IoT challenges and opportunities with an emphasis on security issues. Next, we discuss the potential of hardware-based IoT security approaches. Finally, we conclude with several case studies that advocate the use of stable PUFs and digital PPUFs for several IoT security protocols.
Automatic Liver Segmentation Using an Adversarial Image-to-Image Network

Automatic liver segmentation in 3D medical images is essential in many clinical applications, such as pathological diagnosis of hepatic diseases, surgical planning, and postoperative assessment. However, it is still a very challenging task due to the complex background, fuzzy boundary, and various appearance of liver. In this paper, we propose an automatic and efficient algorithm to segment liver from 3D CT volumes. A deep image-to-image network (DI2IN) is first deployed to generate the liver segmentation, employing a convolutional encoder-decoder architecture combined with multi-level feature concatenation and deep supervision. Then an adversarial network is utilized during training process to discriminate the output of DI2IN from ground truth, which further boosts the performance of DI2IN. The proposed method is trained on an annotated dataset of 1000 CT volumes with various different scanning protocols (e.g., contrast and non-contrast, various resolution and position) and large variations in populations (e.g., ages and pathology). Our approach outperforms the state-of-the-art solutions in terms of segmentation accuracy and computing efficiency.
Redirecting Walking and Driving for Natural Navigation in Immersive Virtual Environments

Walking is the most natural form of locomotion for humans, and real walking interfaces have demonstrated their benefits for several navigation tasks. With recently proposed redirection techniques it becomes possible to overcome space limitations as imposed by tracking sensors or laboratory setups, and, theoretically, it is now possible to walk through arbitrarily large virtual environments. However, walking as sole locomotion technique has drawbacks, in particular, for long distances, such that even in the real world we tend to support walking with passive or active transportation for longer-distance travel. In this article we show that concepts from the field of redirected walking can be applied to movements with transportation devices. We conducted psychophysical experiments to determine perceptual detection thresholds for redirected driving, and set these in relation to results from redirected walking. We show that redirected walking-and-driving approaches can easily be realized in immersive virtual reality laboratories, e. g., with electric wheelchairs, and show that such systems can combine advantages of real walking in confined spaces with benefits of using vehiclebased self-motion for longer-distance travel.
Online community search using thread structure

Online communities are valuable information sources where knowledge is accumulated by interactions between people. Search services provided by online community sites such as forums are often, however, quite poor. To address this, we investigate retrieval techniques that exploit the hierarchical thread structures in community sites. Since these structures are sometimes not explicit or accurately annotated, we use structure discovery techniques. We then make use of thread structures in retrieval experiments. Our results show that using thread structures that have been accurately annotated can lead to significant improvements in retrieval performance compared to strong baselines.
Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence

In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication networks.