nils-herrmann/clean_scirepeval huggingface dataset:
A comprehensive introduction to differential geometry

Spivak's Comprehensive introduction takes as its theme the classical roots of contemporary differential geometry. Spivak explains his Main Premise (my term) as follows: "in order for an introduction to differential geometry to expose the geometric aspect of the subject, an historical approach is necessary; there is no point in introducing the curvature tensor without explaining how it was invented and what it has to do with curvature". His second premise concerns the manner in which the historical material should be presented: "it is absurdly inefficient to eschew the modern language of manifolds, bundles, forms, etc., which was developed precisely in order to rigorize the concepts of classical differential geometry". Here, Spivak is addressing "a dilemma which confronts anyone intent on penetrating the mysteries of differential geometry". On the one hand, the subject is an old one, dating, as we know it, from the works of Gauss and Riemann, and possessing a rich classical literature. On the other hand, the rigorous and systematic formulations in current use were established relatively recently, after topological techniques had been sufficiently well developed to provide a base for an abstract global theory; the coordinate-free geometric methods of E. Cartan were also a major source. Furthermore, the viewpoint of global structure theory now dominates the subject, whereas differential geometers were traditionally more concerned with the local study of geometric objects. Thus it is possible and not uncommon for a modern geometric education to leave the subject's classical origins obscure. Such an approach can offer the great advantages of elegance, efficiency, and direct access to the most active areas of modern research. At the same time, it may strike the student as being frustratingly incomplete. As Spivak remarks, "ignorance of the roots of the subject has its price-no one denies that modern formulations are clear, elegant and precise; it's just that it's impossible to comprehend how any one ever thought of them." While Spivak's impulse to mediate between the past and the present is a natural one and is by no means unique, his undertaking is remarkable for its ambitious scope. Acting on its second premise, the Comprehensive introduction opens with an introduction to differentiable manifolds; the remaining four volumes are devoted to a geometric odyssey which starts with Gauss and Riemann, and ends with the Gauss-Bonnet-Chern Theorem and characteristic classes. A formidable assortment of topics is included along the way, in which we may distinguish several major historical themes: In the first place, the origins of fundamental geometric concepts are investigated carefully. As just one example, Riemannian sectional curvature is introduced by a translation and close exposition of the text of Riemann's remarkable paper, Über die Hypothesen, welche der Geometrie zu Grunde
Performance Evaluation of Concurrent Lock-free Data Structures on GPUs

Graphics processing units (GPUs) have emerged as a strong candidate for high-performance computing. While regular data-parallel computations with little or no synchronization are easy to map on the GPU architectures, it is a challenge to scale up computations on dynamically changing pointer-linked data structures. The traditional lock-based implementations are known to offer poor scalability due to high lock contention in the presence of thousands of active threads, which is common in GPU architectures. In this paper, we present a performance evaluation of concurrent lock-free implementations of four popular data structures on GPUs. We implement a set using lock-free linked list, hash table, skip list, and priority queue. On the first three data structures, we evaluate the performance of different mixes of addition, deletion, and search operations. The priority queue is designed to support retrieval and deletion of the minimum element and addition operations to the set. We evaluate the performance of these lock-free data structures on a Tesla C2070 Fermi GPU and compare it with the performance of multi-threaded lock-free implementations for CPU running on a 24-core Intel Xeon server. The linked list, hash table, skip list, and priority queue implementations achieve speedup of up to 7.4, 11.3, 30.7, and 30.8, respectively on the GPU compared to the Xeon server.
Managing Complexity in Aircraft Design Using Design Structure Matrix

Modern aerospace systems have reached a level of complexity that requires systematic methods for their design. The development of products in aircraft industry involves numerous engineers from different disciplines working on independent components. Routine activities consume a significant part of aircraft development time. To be competitive, aircraft industry needs to manage complexity and readjust the ratio of innovative versus routine work. Thus, the main objective is to develop an approach that can manage complexity in engineering design, reduce the design cycle time, and reduce the product development cost. The design structure matrix (DSM) is a simple tool to perform both analysis and management of complex systems. It enables the user to model, visualize, and analyze the dependencies among the functional group of any system and derive suggestions for the improvement or synthesis of a system. This article illustrates with a case study how DSM can be used to manage complexity in aircraft design process. The result of implementing the DSM approach for Light Combat Aircraft (Navy) at Hindustan Aeronautics Limited, India, (in aircraft design) show primary benefits of 75% reduction in routine activities, 33% reduction in design cycle time, 50% reduction in rework cost, and 30% reduction in product and process development cost of aircraft.
DTAM: Dense tracking and mapping in real-time

DTAM is a system for real-time camera tracking and reconstruction which relies not on feature extraction but dense, every pixel methods. As a single hand-held RGB camera flies over a static scene, we estimate detailed textured depth maps at selected keyframes to produce a surface patchwork with millions of vertices. We use the hundreds of images available in a video stream to improve the quality of a simple photometric data term, and minimise a global spatially regularised energy functional in a novel non-convex optimisation framework. Interleaved, we track the camera's 6DOF motion precisely by frame-rate whole image alignment against the entire dense model. Our algorithms are highly parallelisable throughout and DTAM achieves real-time performance using current commodity GPU hardware. We demonstrate that a dense model permits superior tracking performance under rapid motion compared to a state of the art method using features; and also show the additional usefulness of the dense model for real-time scene interaction in a physics-enhanced augmented reality application.
Normalizing SMS: are Two Metaphors Better than One ?

Electronic written texts used in computermediated interactions (e-mails, blogs, chats, etc) present major deviations from the norm of the language. This paper presents an comparative study of systems aiming at normalizing the orthography of French SMS messages: after discussing the linguistic peculiarities of these messages, and possible approaches to their automatic normalization, we present, evaluate and contrast two systems, one drawing inspiration from the Machine Translation task; the other using techniques that are commonly used in automatic speech recognition devices. Combining both approaches, our best normalization system achieves about 11% Word Error Rate on a test set of about 3000 unseen messages.
ELIZA - a computer program for the study of natural language communication between man and machine

ELIZA is a program operating within the MAC time-sharing system at MIT which makes certain kinds of natural language conversation between man and computer possible. Input sentences are analyzed on the basis of decomposition rules which are triggered by key words appearing in the input text. Responses are generated by reassembly rules associated with selected decomposition rules. The fundamental technical problems with which ELIZA is concerned are: (1) the identification of key words, (2) the discovery of minimal context, (3) the choice of appropriate transformations, (4) generation of responses in the absence of key words, and (5) the provision of an editing capability for ELIZA "scripts". A discussion of some psychological issues relevant to the ELIZA approach as well as of future developments concludes the paper.
On Removing Routing Protocol from Future Wireless Networks: A Real-time Deep Learning Approach for Intelligent Traffic Control

Recently, deep learning has appeared as a breakthrough machine learning technique for various areas in computer science as well as other disciplines. However, the application of deep learning for network traffic control in wireless/heterogeneous networks is a relatively new area. With the evolution of wireless networks, efficient network traffic control such as routing methodology in the wireless backbone network appears as a key challenge. This is because the conventional routing protocols do not learn from their previous experiences regarding network abnormalities such as congestion and so forth. Therefore, an intelligent network traffic control method is essential to avoid this problem. In this article, we address this issue and propose a new, real-time deep learning based intelligent network traffic control method, exploiting deep Convolutional Neural Networks (deep CNNs) with uniquely characterized inputs and outputs to represent the considered Wireless Mesh Network (WMN) backbone. Simulation results demonstrate that our proposal achieves significantly lower average delay and packet loss rate compared to those observed with the existing routing methods. We particularly focus on our proposed method's independence from existing routing protocols, which makes it a potential candidate to remove routing protocol(s) from future wired/ wireless networks.
The neurobiology of addiction: implications for voluntary control of behavior.

There continues to be a debate on whether addiction is best understood as a brain disease or a moral condition. This debate, which may influence both the stigma attached to addiction and access to treatment, is often motivated by the question of whether and to what extent we can justly hold addicted individuals responsible for their actions. In fact, there is substantial evidence for a disease model, but the disease model per se does not resolve the question of voluntary control. Recent research at the intersection of neuroscience and psychology suggests that addicted individuals have substantial impairments in cognitive control of behavior, but this "loss of control" is not complete or simple. Possible mechanisms and implications are briefly reviewed.
Signal Processing and Machine Learning with Differential Privacy: Algorithms and Challenges for Continuous Data

Private companies, government entities, and institutions such as hospitals routinely gather vast amounts of digitized personal information about the individuals who are their customers, clients, or patients. Much of this information is private or sensitive, and a key technological challenge for the future is how to design systems and processing techniques for drawing inferences from this large-scale data while maintaining the privacy and security of the data and individual identities. Individuals are often willing to share data, especially for purposes such as public health, but they expect that their identity or the fact of their participation will not be disclosed. In recent years, there have been a number of privacy models and privacy-preserving data analysis algorithms to answer these challenges. In this article, we will describe the progress made on differentially private machine learning and signal processing.
Restoring Natural Sensory Feedback in Real-Time Bidirectional Hand Prostheses

Hand loss is a highly disabling event that markedly affects the quality of life. To achieve a close to natural replacement for the lost hand, the user should be provided with the rich sensations that we naturally perceive when grasping or manipulating an object. Ideal bidirectional hand prostheses should involve both a reliable decoding of the user’s intentions and the delivery of nearly “natural” sensory feedback through remnant afferent pathways, simultaneously and in real time. However, current hand prostheses fail to achieve these requirements, particularly because they lack any sensory feedback. We show that by stimulating the median and ulnar nerve fascicles using transversal multichannel intrafascicular electrodes, according to the information provided by the artificial sensors from a hand prosthesis, physiologically appropriate (near-natural) sensory information can be provided to an amputee during the real-time decoding of different grasping tasks to control a dexterous hand prosthesis. This feedback enabled the participant to effectively modulate the grasping force of the prosthesis with no visual or auditory feedback. Three different force levels were distinguished and consistently used by the subject. The results also demonstrate that a high complexity of perception can be obtained, allowing the subject to identify the stiffness and shape of three different objects by exploiting different characteristics of the elicited sensations. This approach could improve the efficacy and “life-like” quality of hand prostheses, resulting in a keystone strategy for the near-natural replacement of missing hands.
The use of pro-ana blogs for online social support.

The aim of this research was to document the content on pro-ana blogs and to uncover how the unique socio-technical features of blogs (interactivity, self-disclosure, masspersonal communication) facilitate social support among members. A final sample of 48 pro-ana blogs provided 624 individual units for coding. Results indicate that prevalent forms of social support were emotional support, esteem support, and informational support. A new category, reciprocal self-disclosure, was also revealed to be quite frequent. Blogs are spaces where social support is sought and communicated among members of the pro-ana network. Interpretation of blog communication and implications for treatment and research are discussed.
Energy-credit scheduler: An energy-aware virtual machine scheduler for cloud systems

Virtualization facilitates the provision of flexible resources and improves energy efficiency through the consolidation of virtualized servers into a smaller number of physical servers. As an increasingly essential component of the emerging cloud computing model, virtualized environments bill their users based on processor time or the number of virtual machine instances. However, accounting based only on the depreciation of server hardware is not sufficient because the cooling and energy costs for data centers will exceed the purchase costs for hardware. This paper suggests a model for estimating the energy consumption of each virtual machine without dedicated measurement hardware. Our model estimates the energy consumption of a virtual machine based on in-processor events generated by the virtual machine. Based on this estimation model, we also propose a virtual machine scheduling algorithm that can provide computing resources according to the energy budget of each virtual machine. The suggested schemes are implemented in the Xen virtualization system, and an evaluation shows that the suggested schemes estimate and provide energy consumption with errors of less than 5% of the total energy
Taxonomy of Digital Forensics: Investigation Tools and Challenges

In today’s world of computers, any kind of information can be made available within few clicks for different endeavours. The information may be tampered by changing the statistical properties and can be further used for criminal activities. These days, Cybercrimes are happening at a very large scale, and possess big threats to the security of an individual, firm, industry and even to developed countries. To combat such crimes, law enforcement agencies and investment institutions are incorporating supportive examination policies, procedures and protocols to address the complete investigation process. The paper entails a detailed review of several cybercrimes followed by various digital forensics processes involved in the cybercrime investigation. Further various digital forensics tools with detail explanation are discussed with their advantages, disadvantages, challenges, and drawbacks. A comparison among all the selected tools is also presented. Finally the paper recommends the need of training programs for the first responder and judgement of signature based image authentication.
A sock puppet detection algorithm on virtual spaces

On virtual spaces, some individuals use multiple usernames or copycat/forge other users (usually called ‘‘sock puppet’’) to communicate with others. Those sock puppets are fake identities through which members of Internet community praise or create the illusion of support for the product or one’s work, pretending to be a different person. A fundamental problem is how to identify these sock puppets. In this paper, we propose a sock puppet detection algorithm which combines authorship-identification techniques and link analysis. Firstly, we propose an interesting social network model in which links between two IDs are built if they have similar attitudes to most topics that both of them participate in; then, the edges are pruned according a hypothesis test, which consider the impact of their writing styles; finally, the link-based community detection for pruned network is performed. Compared to traditional methods, our approach has three advantages: (1) it conforms to the practical meanings of sock puppet community; (2) it can be applied in online situation; (3) it increases the efficiency of link analysis. In the experimental work, we evaluate our method using real datasets and compared our approach with several previous methods; the results have proved above advantages.
Battery Charger for Electric Vehicle Traction Battery Switch Station

This paper presents the functionality of a commercialized fast charger for a lithium-ion electric vehicle propulsion battery. The device is intended to operate in a battery switch station, allowing an up-to 1-h recharge of a 25-kWh depleted battery, removed from a vehicle. The charger is designed as a dual-stage-controlled ac/dc converter. The input stage consists of a three-phase full-bridge diode rectifier combined with a reduced rating shunt active power filter. The input stage creates an uncontrolled pulsating dc bus while complying with the grid codes by regulating the total harmonic distortion and power factor according to the predetermined permissible limits. The output stage is formed by six interleaved groups of two parallel dc-dc converters, fed by the uncontrolled dc bus and performing the battery charging process. The charger is capable of operating in any of the three typical charging modes: constant current, constant voltage, and constant power. Extended simulation and experimental results are shown to demonstrate the functionality of the device.
A MATLAB-based tool for EV-design

This paper presents a MATLAB-based software tool for modeling and simulation of electric vehicles. It is a software package developed using MATLAB 7.8 2009. Each EV element is specified by a set of dynamic equations. The constants associated with each of these sets are the input data to the simulation system. The dynamic equations used are context-sensitively displayed. It is a feed-backward simulator that estimates the vehicle element by element requirements and performance hierarchically starting from the tire to the battery-package. One of the primary advantages of using MATLAB is its advanced charting features. The simulator provides dual-outputs in the form of tables and in the form of charts. Lamar Electric Vehicle I, built in 2007, is used as a hardware-platform to relate and verify the simulation results.
ParkGauge: Gauging the Occupancy of Parking Garages with Crowdsensed Parking Characteristics

Finding available parking spaces in dense urban areas is a globally recognized issue in urban mobility. Whereas prior studies have focused on outdoor/street parking due to a common belief that parking garages are capable of delivering real-time occupancy information, we specifically target at (indoor) parking garages as this belief is far from true. This problem is very challenging as all the infrastructure supports (e.g., GPS and Wi-Fi) assumed by existing proposals are not available to parking garages, so counting how many vehicles are using a parking garage by crowd sensing can be extremely difficult. To this end, we present Park Gauge, a method to gauge the occupancy of parking garages, along with a reference system prototype for performance evaluation, it infers parking occupancy from crowd sensed parking characteristics instead of counting the parked vehicles. Park Gauge adopts low-power sensors (e.g., accelerometer and barometer) in the driver's smartphone to determine the driving states (e.g., turning and braking). A sequence of such states further allows the inference of driving contexts (e.g., driving, queuing and parked) that in turn yield temporal parking characteristics of a parking garage, including time-to-park and time-in-cruising/queuing. Mining such mobile data opportunistically collected from a crowd of drivers arriving at various garages yields a good measure of their occupancies and hence useful recommendations can be generated (in real-time) to inform drivers coming toward these venues. Through extensive experiments, we demonstrate that our method fully explores these parking characteristics to efficiently infer occupancies of parking garages with high accuracy.
A Double-Sided Parallel-Strip Line Push–Pull Oscillator

A novel double-sided parallel-strip line (DSPSL) push-pull oscillator using two identical sub-oscillators on the opposite sides of a dielectric substrate is proposed. The two sub-oscillators, sharing a common DSPSL resonator and common ground in the middle of the substrate, generate out-of-phase fundamental signals and in-phase second harmonics. At the common DSPSL output, the second harmonics are cancelled out while the fundamental signals are well combined. By this design, an additional combiner at the output, as required by the conventional push-pull circuits, is not needed, which greatly reduces the circuit size and simplifies the design procedures of the proposed push-pull oscillator.
Data Poisoning Attacks in Contextual Bandits

We study offline data poisoning attacks in contextual bandits, a class of reinforcement learning problems with important applications in online recommendation and adaptive medical treatment, among others. We provide a general attack framework based on convex optimization and show that by slightly manipulating rewards in the data, an attacker can force the bandit algorithm to pull a target arm for a target contextual vector. The target arm and target contextual vector are both chosen by the attacker. That is, the attacker can hijack the behavior of a contextual bandit. We also investigate the feasibility and the side effects of such attacks, and identify future directions for defense. Experiments on both synthetic and real-world data demonstrate the efficiency of the attack algorithm.