nils-herrmann/clean_scirepeval huggingface dataset:
Using MVA and EVA to measure financial performance.

Two measures of financial performance that are being applied increasingly in investor-owned and not-for-profit healthcare organizations are market value added (MVA) and economic value added (EVA). Unlike traditional profitability measures, both MVA and EVA measures take into account the cost of equity capital. MVA is most appropriate for investor-owned healthcare organizations and EVA is the best measure for not-for-profit organizations. As healthcare financial managers become more familiar with MVA and EVA and understand their potential, these two measures may become more widely accepted accounting tools for assessing the financial performance of investor-owned and not-for-profit healthcare organizations.
Bitcoin Market , economics and regulation SUMMARY

Bitcoin is a digital currency which started circulating in 2009. It was the first form of virtual money to become relatively popular. Bitcoin is public in nature as it maintains a log of all transactions. These are verified by its users in a process called mining. The extent of computing power and energy needed to mine bitcoins is set to increase over time. The main advantages of Bitcoin in comparison to traditional currencies are low transaction fees, and anonymity of use. It also has numerous drawbacks, in particular high price volatility, being prone to security breaches, inelastic supply coded by mathematical formula, lack of legal security as well as numerous risks stemming from its immaturity as a currency. Due to the anonymity embedded in the system Bitcoin has the potential to be used for money laundering and tax evasion. However, research so far shows this potential has not yet been taken up on a significant scale. Regulation of Bitcoin is at a nascent stage with those systems so far instituted characterised by strict capital controls banning its use, and a few governments aiming to ensure it is covered for tax purposes. Many central bank authorities have issued warnings about Bitcoin, mentioning in particular its high price volatility and lack of consumer protection as the main risks to its users.
Detecting Context Dependent Messages in a Conversational Environment

While automatic response generation for building chatbot s ys ems has drawn a lot of attention recently, there is limited understanding on when we need to c onsider the linguistic context of an input text in the generation process. The task is challeng ing, as messages in a conversational environment are short and informal, and evidence that can in dicate a message is context dependent is scarce. After a study of social conversation data cra wled from the web, we observed that some characteristics estimated from the responses of messa ges are discriminative for identifying context dependent messages. With the characteristics as we ak supervision, we propose using a Long Short Term Memory (LSTM) network to learn a classifier. O ur method carries out text representation and classifier learning in a unified framewor k. Experimental results show that the proposed method can significantly outperform baseline meth ods on accuracy of classification.
Auto-encoder Based Data Clustering

Linear or non-linear data transformations are widely used processing techniques in clustering. Usually, they are beneficial to enhancing data representation. However, if data have a complex structure, these techniques would be unsatisfying for clustering. In this paper, based on the auto-encoder network, which can learn a highly non-linear mapping function, we propose a new clustering method. Via simultaneously considering data reconstruction and compactness, our method can obtain stable and effective clustering. Experiments on three databases show that the proposed clustering model achieves excellent performance in terms of both accuracy and normalized mutual information.
Remote data checking for network coding-based distributed storage systems

Remote Data Checking (RDC) is a technique by which clients can establish that data outsourced at untrusted servers remains intact over time. RDC is useful as a prevention tool, allowing clients to periodically check if data has been damaged, and as a repair tool whenever damage has been detected. Initially proposed in the context of a single server, RDC was later extended to verify data integrity in distributed storage systems that rely on replication and on erasure coding to store data redundantly at multiple servers. Recently, a technique was proposed to add redundancy based on network coding, which offers interesting tradeoffs because of its remarkably low communication overhead to repair corrupt servers.
 Unlike previous work on RDC which focused on minimizing the costs of the prevention phase, we take a holistic look and initiate the investigation of RDC schemes for distributed systems that rely on network coding to minimize the combined costs of both the prevention and repair phases. We propose RDC-NC, a novel secure and efficient RDC scheme for network coding-based distributed storage systems. RDC-NC mitigates new attacks that stem from the underlying principle of network coding. The scheme is able to preserve in an adversarial setting the minimal communication overhead of the repair component achieved by network coding in a benign setting. We implement our scheme and experimentally show that it is computationally inexpensive for both clients and servers.
Energy proportional datacenter networks

Numerous studies have shown that datacenter computers rarely operate at full utilization, leading to a number of proposals for creating servers that are energy proportional with respect to the computation that they are performing.
 In this paper, we show that as servers themselves become more energy proportional, the datacenter network can become a significant fraction (up to 50%) of cluster power. In this paper we propose several ways to design a high-performance datacenter network whose power consumption is more proportional to the amount of traffic it is moving -- that is, we propose energy proportional datacenter networks.
 We first show that a flattened butterfly topology itself is inherently more power efficient than the other commonly proposed topology for high-performance datacenter networks. We then exploit the characteristics of modern plesiochronous links to adjust their power and performance envelopes dynamically. Using a network simulator, driven by both synthetic workloads and production datacenter traces, we characterize and understand design tradeoffs, and demonstrate an 85% reduction in power --- which approaches the ideal energy-proportionality of the network.
 Our results also demonstrate two challenges for the designers of future network switches: 1) We show that there is a significant power advantage to having independent control of each unidirectional channel comprising a network link, since many traffic patterns show very asymmetric use, and 2) system designers should work to optimize the high-speed channel designs to be more energy efficient by choosing optimal data rate and equalization technology. Given these assumptions, we demonstrate that energy proportional datacenter communication is indeed possible.
Analysis and Design Considerations of Integrated 3-Level Buck Converters

This paper presents a systematic analysis of integrated 3-level buck converters under both ideal and real conditions as a guidance for designing robust and fast 3-level buck converters. Under ideal conditions, the voltage conversion ratio, the output voltage ripple and, in particular, the system's loop-gain function are derived. Design considerations for real circuitry implementations of an integrated 3-level converter, such as the implementation of the flying capacitor, the impacts of the parasitic capacitors of the flying capacitor and the 4 power switches, and the time mismatch between the 2 duty-cycle signals are thoroughly discussed. Under these conditions, the voltage conversion ratio, the voltage across the flying capacitor and the power efficiency are analyzed and verified with Cadence simulation results. The loop-gain function of an integrated 3-level buck converter with parasitic capacitors and time mismatch is derived with the state-space averaging method. The derived loop-gain functions are verified with time-domain small signal injection simulation and measurement, with a good match between the analytical and experimental results.
Feedback Networks

Urrently, the most successful learning models in computer vision are based on learning successive representations followed by a decision layer. This is usually actualized through feedforward multilayer neural networks, e.g. ConvNets, where each layer forms one of such successive representations. However, an alternative that can achieve the same goal is a feedback based approach in which the representation is formed in an iterative manner based on a feedback received from previous iterations output. We establish that a feedback based approach has several core advantages over feedforward: it enables making early predictions at the query time, its output naturally conforms to a hierarchical structure in the label space (e.g. a taxonomy), and it provides a new basis for Curriculum Learning. We observe that feedback develops a considerably different representation compared to feedforward counterparts, in line with the aforementioned advantages. We provide a general feedback based learning architecture, instantiated using existing RNNs, with the endpoint results on par or better than existing feedforward networks and the addition of the above advantages.
Imposing higher-level Structure in Polyphonic Music Generation using Convolutional Restricted Boltzmann Machines and Constraints

We introduce a method for imposing higher-level structure on generated, polyphonic music. A Convolutional Restricted Boltzmann Machine (C-RBM) as a generative model is combined with gradient descent constraint optimisation to provide further control over the generation process. Among other things, this allows for the use of a “template” piece, from which some structural properties can be extracted, and transferred as constraints to the newly generated material. The sampling process is guided with Simulated Annealing to avoid local optima, and to find solutions that both satisfy the constraints, and are relatively stable with respect to the C-RBM. Results show that with this approach it is possible to control the higher-level self-similarity structure, the meter, and the tonal properties of the resulting musical piece, while preserving its local musical coherence.
Evaluating role mining algorithms

While many role mining algorithms have been proposed in recent years, there lacks a comprehensive study to compare these algorithms. These role mining algorithms have been evaluated when they were proposed, but the evaluations were using different datasets and evaluation criteria. In this paper, we introduce a comprehensive framework for evaluating role mining algorithms. We categorize role mining algorithms into two classes based on their outputs; Class 1 algorithms output a sequence of prioritized roles while Class 2 algorithms output complete RBAC states. We then develop techniques that enable us to compare these algorithms directly. We also introduce a new role mining algorithm and two new ways for algorithmically generating datasets for evaluation. Using synthetic as well as real datasets, we compared nine role mining algorithms. Our results illustrate the strengths and weaknesses of these algorithms.
High efficiency and smooth transition buck-boost converter for extending battery life in portable devices

This paper presents a high-efficiency and smoothtransition buck-boost (BB) converter to extend the battery life of portable devices. Owing to the usage of four switches, the BB control topology needs to minimize the switching and conduction losses at the same time. Therefore, over a wide input voltage range, the proposed BB converter consumes minimum switching loss like the basic operation of buck or boost converter. Besides, the conduction loss is reduced by means of the reduction of the inductor current level. Especially, the proposed BB converter offers good line/load regulation and thus provides a smooth and stable output voltage when the battery voltage decreases. Simulation results show that the output voltage drops is very small during the whole battery life time and the output transition is very smooth during the mode transition by the proposed BB control scheme.
DaDianNao: A Neural Network Supercomputer

Many companies are deploying services largely based on machine-learning algorithms for sophisticated processing of large amounts of data, either for consumers or industry. The state-of-the-art and most popular such machine-learning algorithms are Convolutional and Deep Neural Networks (CNNs and DNNs), which are known to be computationally and memory intensive. A number of neural network accelerators have been recently proposed which can offer high computational capacity/area ratio, but which remain hampered by memory accesses. However, unlike the memory wall faced by processors on general-purpose workloads, the CNNs and DNNs memory footprint, while large, is not beyond the capability of the on-chip storage of a multi-chip system. This property, combined with the CNN/DNN algorithmic characteristics, can lead to high internal bandwidth and low external communications, which can in turn enable high-degree parallelism at a reasonable area cost. In this article, we introduce a custom multi-chip machine-learning architecture along those lines, and evaluate performance by integrating electrical and optical inter-chip interconnects separately. We show that, on a subset of the largest known neural network layers, it is possible to achieve a speedup of 656.63× over a GPU, and reduce the energy by 184.05× on average for a 64-chip system. We implement the node down to the place and route at 28 nm, containing a combination of custom storage and computational units, with electrical inter-chip interconnects.
5G Mobile Phone Concept

Today 3G mobile systems are on the ground providing IP connectivity for real-time and non-real-time services. On the other side, there are many wireless technologies that have proven to be important, with the most important ones being 802.11 Wireless Local Area Networks (WLAN) and 802.16 Wireless Metropolitan Area Networks (WMAN), as well as ad-hoc Wireless Personal Area Network (WPAN) and wireless networks for digital TV and radio broadcast. Then, the concepts of 4G is already much discussed and it is almost certain that 4G will include several standards under a common umbrella, similarly to 3G, but with IEEE 802.xx wireless mobile networks included from the beginning. The main contribution of this paper is definition of 5G (Fifth Generation) mobile network concept, which is seen as user-centric concept instead of operator-centric as in 3G or service-centric concept as seen for 4G. In the proposed concept the mobile user is on the top of all. The 5G terminals will have software defined radios and modulation scheme as well as new error-control schemes can be downloaded from the Internet on the run. The development is seen towards the user terminals as a focus of the 5G mobile networks. The terminals will have access to different wireless technologies at the same time and the terminal should be able to combine different flows from different technologies. Each network will be responsible for handling user-mobility, while the terminal will make the final choice among different wireless/mobile access network providers for a given service. The paper also proposes intelligent Internet phone concept where the mobile phone can choose the best connections by selected constraints and dynamically change them during a single end-to-end connection. The proposal in this paper is fundamental shift in the mobile networking philosophy compared to existing 3G and near-soon 4G mobile technologies, and this concept is called here the 5G.
Hybrid Reward Architecture for Reinforcement Learning

One of the main challenges in reinforcement learning (RL) is generalisation. In typical deep RL methods this is achieved by approximating the optimal value function with a low-dimensional representation using a deep network. While this approach works well in many domains, in domains where the optimal value function cannot easily be reduced to a low-dimensional representation, learning can be very slow and unstable. This paper contributes towards tackling such challenging domains, by proposing a new method, called Hybrid Reward Architecture (HRA). HRA takes as input a decomposed reward function and learns a separate value function for each component reward function. Because each component typically only depends on a subset of all features, the corresponding value function can be approximated more easily by a low-dimensional representation, enabling more effective learning. We demonstrate HRA on a toy-problem and the Atari game Ms. Pac-Man, where HRA achieves above-human performance.
Multiple-input Multiple-output Radar and Sparse Array Synthetic Impulse and Aperture Radar

Radar theory and radar system have developed a lot for the last 50 years or so. Recently, a new concept in array radar has been introduced by the multiple-input multiple-output (MIMO) radar, which has the potential to dramatically improve the performance of radars in parameters estimation. While an earlier appeared concept, synthetic impulse and aperture radar (SIAR) is a typical kind of MIMO radar and probes a channel by transmitting multiple signals separated both spectrally and spatially. To the best knowledge of the authors, almost all the analyses available are based on the simple linear array while our SIAR system is based on a circular array. This paper first introduces the recent research and development in and the features of MIMO radars, then discusses our SIAR system as a specific example of MIMO system and finally the unique advantages of SIAR are listed
I-CASH: Intelligently Coupled Array of SSD and HDD

This paper presents a new disk I/O architecture composed of an array of a flash memory SSD (solid state disk) and a hard disk drive (HDD) that are intelligently coupled by a special algorithm. We call this architecture I-CASH: Intelligently Coupled Array of SSD and HDD. The SSD stores seldom-changed and mostly read reference data blocks whereas the HDD stores a log of deltas between currently accessed I/O blocks and their corresponding reference blocks in the SSD so that random writes are not performed in SSD during online I/O operations. High speed delta compression and similarity detection algorithms are developed to control the pair of SSD and HDD. The idea is to exploit the fast read performance of SSDs and the high speed computation of modern multi-core CPUs to replace and substitute, to a great extent, the mechanical operations of HDDs. At the same time, we avoid runtime SSD writes that are slow and wearing. An experimental prototype I-CASH has been implemented and is used to evaluate I-CASH performance as compared to existing SSD/HDD I/O architectures. Numerical results on standard benchmarks show that I-CASH reduces the average I/O response time by an order of magnitude compared to existing disk I/O architectures such as RAID and SSD/HDD storage hierarchy, and provides up to 2.8 speedup over state-of-the-art pure SSD storage. Furthermore, I-CASH reduces random writes to SSD implying reduced wearing and prolonged life time of the SSD.
Unsupervised graph-based topic labelling using dbpedia

Automated topic labelling brings benefits for users aiming at analysing and understanding document collections, as well as for search engines targetting at the linkage between groups of words and their inherent topics. Current approaches to achieve this suffer in quality, but we argue their performances might be improved by setting the focus on the structure in the data. Building upon research for concept disambiguation and linking to DBpedia, we are taking a novel approach to topic labelling by making use of structured data exposed by DBpedia. We start from the hypothesis that words co-occuring in text likely refer to concepts that belong closely together in the DBpedia graph. Using graph centrality measures, we show that we are able to identify the concepts that best represent the topics. We comparatively evaluate our graph-based approach and the standard text-based approach, on topics extracted from three corpora, based on results gathered in a crowd-sourcing experiment. Our research shows that graph-based analysis of DBpedia can achieve better results for topic labelling in terms of both precision and topic coverage.
Automatic TFT-LCD mura detection based on image reconstruction and processing

Automatic inspection of Mura defects is a challenging task in thin-film transistor liquid crystal display (TFT-LCD) defect detection, which is critical for LCD manufacturers to guarantee high standard quality control. In this paper, we propose a set of automatic procedures to detect mura defects by using image processing and computer vision techniques. Singular Value Decomposition (SVD) and Discrete Cosine Transformation(DCT) techniques are employed to conduct image reconstruction, based on which we are able to obtain the differential image of LCD Cells. In order to detect different types of mura defects accurately, we then design a method that employs different detection modules adaptively, which can overcome the disadvantage of simply using a single threshold value. Finally, we provide the experimental results to validate the effectiveness of the proposed method in mura detection.
Design of a cybernetic hand for perception and action

Strong motivation for developing new prosthetic hand devices is provided by the fact that low functionality and controllability—in addition to poor cosmetic appearance—are the most important reasons why amputees do not regularly use their prosthetic hands. This paper presents the design of the CyberHand, a cybernetic anthropomorphic hand intended to provide amputees with functional hand replacement. Its design was bio-inspired in terms of its modular architecture, its physical appearance, kinematics, sensorization, and actuation, and its multilevel control system. Its underactuated mechanisms allow separate control of each digit as well as thumb–finger opposition and, accordingly, can generate a multitude of grasps. Its sensory system was designed to provide proprioceptive information as well as to emulate fundamental functional properties of human tactile mechanoreceptors of specific importance for grasp-and-hold tasks. The CyberHand control system presumes just a few efferent and afferent channels and was divided in two main layers: a high-level control that interprets the user’s intention (grasp selection and required force level) and can provide pertinent sensory feedback and a low-level control responsible for actuating specific grasps and applying the desired total force by taking advantage of the intelligent mechanics. The grasps made available by the high-level controller include those fundamental for activities of daily living: cylindrical, spherical, tridigital (tripod), and lateral grasps. The modular and flexible design of the CyberHand makes it suitable for incremental development of sensorization, interfacing, and control strategies and, as such, it will be a useful tool not only for clinical research but also for addressing neuroscientific hypotheses regarding sensorimotor control.