nils-herrmann/clean_scirepeval huggingface dataset:
Efficient Architecture Search by Network Transformation

Techniques for automatically designing deep neural network architectures such as reinforcement learning based approaches have recently shown promising results. However, their success is based on vast computational resources (e.g. hundreds of GPUs), making them difficult to be widely used. A noticeable limitation is that they still design and train each network from scratch during the exploration of the architecture space, which is highly inefficient. In this paper, we propose a new framework toward efficient architecture search by exploring the architecture space based on the current network and reusing its weights. We employ a reinforcement learning agent as the meta-controller, whose action is to grow the network depth or layer width with function-preserving transformations. As such, the previously validated networks can be reused for further exploration, thus saves a large amount of computational cost. We apply our method to explore the architecture space of the plain convolutional neural networks (no skip-connections, branching etc.) on image benchmark datasets (CIFAR-10, SVHN) with restricted computational resources (5 GPUs). Our method can design highly competitive networks that outperform existing networks using the same design scheme. On CIFAR-10, our model without skip-connections achieves 4.23% test error rate, exceeding a vast majority of modern architectures and approaching DenseNet. Furthermore, by applying our method to explore the DenseNet architecture space, we are able to achieve more accurate networks with fewer parameters.
Overview of the IBM Java Just-in-Time Compiler

We present the design and implementation of several optimizations and techniques included in the latest IBM Java Just-in-Time (JIT) Compiler. We first discuss some of the modifications we have applied to Sun Microsystems’ reference implementation of the Java Virtual Machine (JVM) Specification to increase the performance, including a change in the object layout. We then describe each of the optimizations, referring to what had to be taken into account because of both the just-in-time nature of the compiler and the requirements of the Java language specification, such as exception checking. We also present code generation techniques targeting Intel architectures, describing the register allocation schemes, exception handling, and code scheduling. Finally we report on the performance of the IBM JIT compiler, showing both the effectiveness of the individual optimizations and the competitive overall performance of the JIT compiler in comparison with a competitor, using industry-standard benchmarking programs. All the techniques presented here are included in the official product (JIT Compiler version 3.0), which has been integrated into the IBM Developer Kit for Windows, Java Technology Edition, Version 1.1.7.
Best-response multiagent learning in non-stationary environments

This paper investigates a relatively new direction in Multiagent Reinforcement Learning. Most multiagent learning techniques focus on Nash equilibria as elements of both the learning algorithm and its evaluation criteria. In contrast, we propose a multiagent learning algorithm that is optimal in the sense of finding a best-response policy, rather than in reaching an equilibrium. We present the first learning algorithm that is provably optimal against restricted classes of non-stationary opponents. The algorithm infers an accurate model of the opponentýs non-stationary strategy, and simultaneously creates a best-response policy against that strategy. Our learning algorithm works within the very general framework of n-player, general-sum stochastic games, and learns both the game structure and its associated optimal policy.
Development of robots for rehabilitation therapy: the Palo Alto VA/Stanford experience.

For over 25 years, personal assistant robots for severely disabled individuals have been in development. More recently, using robots to deliver rehabilitation therapy has been proposed. This paper summarizes the development and clinical testing of three mechatronic systems for post-stroke therapy conducted at the VA Palo Alto in collaboration with Stanford University. We describe the philosophy and experiences that guided their evolution. Unique to the Palo Alto approach is provision for bimanual, mirror-image, patient-controlled therapeutic exercise. Proof-of-concept was established with a 2-degree-of-freedom (DOF) elbow/forearm manipulator. Tests of a second-generation therapy robot producing planar forearm movements in 19 hemiplegic and control subjects confirmed the validity and reliability of interaction forces during mechanically assisted upper-limb movements. Clinical trials comparing 3-D robot-assisted therapy to traditional therapy in 21 chronic stroke subjects showed significant improvement in the Fugl-Meyer (FM) measure of motor recovery in the robot group, which exceeded improvements in the control group.
A Delta–Sigma Interface Circuit for Capacitive Sensors With an Automatically Calibrated Zero Point

We present a low-complexity interface circuit for capacitive sensors that are integrated into sensor microsystems. To reduce hardware cost while keeping high resolution, a first-order delta-sigma modulator (DSM), which balances the charge from the capacitive difference between sense and reference capacitors with the charge from a fixed-quantity capacitor, is employed. A charge-mode digital-to-analog converter and a successive approximation register are utilized to automatically calibrate the zero point of the interface circuit, which may shift further than a dynamic range. A prototype circuit fabricated in a 0.35-μm CMOS process has an active area of 0.048 mm2. Its DSM operates at a sampling frequency of 1 MS/s with an oversampling ratio of 128. Experiments show that this circuit can read a capacitive difference from -0.5 to +0.5 pF with a 0.49-fF resolution. A capacitive offset that causes the zero point to shift can be canceled in the range from -2 to 2 pF with a 31.25-fF resolution. Measured power consumption was 1.44 mW at a 3.3-V supply.
VERY HIGH RESOLUTION SAR TOMOGRAPHY VIA COMPRESSIVE SENSING

By using multi-pass SAR acquisitions, SAR tomography (TomoSAR) extends the synthetic aperture principle into the elevation direction for 3-D imaging. Since the orbits of modern space-borne SAR systems, like TerraSAR-X, are tightly controlled, the elevation resolution (depending on the elevation aperture size) is at least an order of magnitude lower than in range and azimuth. Hence, superresolution algorithms are desired. The high anisotropic 3D resolution element renders the signals sparse in elevation. This property suggests using compressive sensing (CS) methods. The paper presents the theory of 4D (i.e. space-time) CS TomoSAR and compares it with classical tomographic methods. Super-resolution properties and point localization accuracies are demonstrated using simulations and real data. A CS reconstruction of a building complex from TerraSAR-X spotlight data is presented. In addition, the model based time warp method for differential tomographic non-linear motion monitoring is proposed and validated by reconstructing seasonal motion (caused by thermal expansion) of a building complex.
Telephone Call Centers: Tutorial, Review, and Research Prospects

Telephone call centers are an integral part of many businesses, and their economic role is significant and growing. They are also fascinating sociotechnical systems in which the behavior of customers and employees is closely intertwined with physical performance measures. In these environments traditional operational models are of great value—and at the same time fundamentally limited—in their ability to characterize system performance. We review the state of research on telephone call centers. We begin with a tutorial on how call centers function and proceed to survey academic research devoted to the management of their operations. We then outline important problems that have not been addressed and identify promising directions for future research.
Megaspeed Drive Systems: Pushing Beyond 1 Million r/min

The latest research in mesoscale drive systems is targeting rotational speeds toward 1 million r/min for a power range of 1-1 kW. Emerging applications for megaspeed drives (MegaNdrives) are to be found in future turbo compressor systems for fuel cells and heat pumps, generators/starters for portable nanoscale gas turbines, printed circuit board drilling and machining spindles, and electric power generation from pressurized gas flow. The selection of the machine type and the challenges involved in designing a machine for megaspeed operation such as the winding concepts, a mechanical rotor design capable of 1 000 000 r/min, the selection of magnetic materials for the stator, and the optimization concerning high-frequency losses and torque density are presented. Furthermore, a review of the advantageous inverter topologies, taking into account the extremely low stator inductance and possible high-speed bearing types such as ball bearings, air bearings, foil bearings, and magnetic bearings, are given. Finally, prototypes and experimental results originating from MegaNdrive research at Swiss Federal Institute of Technology Zurich are discussed and extreme temperature operation and power microelectricalmechanical system are identified as targets for future research.
Compressive imaging of color images

In this paper, we propose a novel compressive imaging framework for color images. We first introduce an imaging architecture based on combining the existing single-pixel Compressive Sensing (CS) camera with a Bayer color filter, thereby enabling acquisition of compressive color measurements. Then we propose a novel CS reconstruction algorithm that employs joint sparsity models in simultaneously recovering the R, G, B channels from the compressive measurements. Experiments simulating the imaging and reconstruction procedures demonstrate the feasibility of the proposed idea and the superior quality in reconstruction.
Hierarchical quadratic programming: Fast online humanoid-robot motion generation

Hierarchical least-square optimization is often used in robotics to inverse a direct function when multiple incompatible objectives are involved. Typical examples are inverse kinematics or dynamics. The objectives can be given as equalities to be satisfied (e.g. point-to-point task) or as areas of satisfaction (e.g. the joint range). This two-part paper proposes a complete solution to resolve multiple least-square quadratic problems of both equality and inequality constraints ordered into a strict hierarchy. Our method is able to solve a hierarchy of only equalities ten time faster than the classical method and can consider inequalities at any level while running at the typical control frequency on whole-body size problems. This generic solver is used to resolve the redundancy of humanoid robots while generating complex movements in constrained environment. In this first part, we establish the mathematical bases underlying the hierarchical problem and propose a dedicated solver. When only equalities are involved, the solver amounts to the classical solution used to handle redundancy in inverse kinematics in a far more efficient way. It is able to handle inequalities at any priority levels into a single resolution scheme, which avoids the high number of iterations encountered with cascades of solvers. A simple example is given to illustrate the interest of our approach. The second part of the paper focuses on the implementation of the solver for inverse kinematics and its use to generate robot motions. (a) (b) Fig. 1: Various situations of inequality and equality constraints. (a) reaching a distant object while keeping balance. The visibility and postural tasks are satisfied only if possible. (b) The target is kept inside a preference area.
Classroom Re-design to Facilitate Student Learning : A Case Study of Changes to a University Classroom

This case study examines the physical aspects of a particular university classroom, and what affect specific changes to the classroom had on the perceptions of students, instructors and observers regarding the room as an effective learning space. We compare survey and focus group data collected from students taking courses in the classroom prior to changes to the physical environment with comparable data from students taking courses in the same classroom after specific changes had been made. Immediately following changes to the classroom, notable increases were observed in reported perceptions of student satisfaction with the physical environment, including perceptions of the classroom as a more effective and engaging learning space. Similar perceptions of improvement as a teaching-learning space were reported by instructors and observers. However, subsequent follow-up data collection and analyses suggested little if any sustained increase in perceptions of efficacy of the room as a learning space; indeed, most reported variables returned to baseline levels. The implications of these findings and their relevance to classroom design nevertheless may provide insight regarding the manner in which physical space might support or even enhance teaching and learning.  learning spaces, active learning, classrooms, teaching and learning environment, classroom design For a number of years there has been an on-going pedagogical shift in higher education away from a traditional content delivery model of instruction to more active models of learning in which students play more involved and interactive roles within the classroom (Cornell, 2002; Brown, 2006). This movement has been coupled with the recognition that the traditional university classroom, with its unidirectional design and tiered, fixed theatre-like seating, is insufficient to accommodate what have increasingly become more varied teaching and learning practices. This growing realization that as the nature of teaching and learning evolves so too must teaching and learning spaces has, in recent years, resulted in a heightened interest among scholars in the examination of classroom space and, more specifically, the inquiry into the connection between classroom design and pedagogy and learning (Brooks, 2011). This study presents findings from an on-going research project examining classroom spaces on a small university campus. The present case study, which is one component of the larger project, compares perceptions of a specific classroom as an effective learning space before and after relatively extensive changes were made to the physical environment of the room. These changes were guided by feedback from students and instructors as well as by existing literature on innovative classroom designs. In particular, we were interested in assessing if these changes 1	  University	  of	  Lethbridge,	  Lethbridge,	  Alberta,	  Canada
BOES : Building Occupancy Estimation System Using Sparse Ambient Vibration Monitoring

In this paper, we present a room-level building occupancy estimation system (BOES) utilizing low-resolution vibration sensors that are sparsely distributed. Many ubiquitous computing and building maintenance systems require fine-grained occupancy knowledge to enable occupant centric services and optimize space and energy utilization. The sensing infrastructure support for current occupancy estimation systems often requires multiple intrusive sensors per room, resulting in systems that are both costly to deploy and difficult to maintain. To address these shortcomings, we developed BOES. BOES utilizes sparse vibration sensors to track occupancy levels and activities. Our system has three major components. 1) It extracts features that distinguish occupant activities from noise prone ambient vibrations and detects human footsteps. 2) Using a sequence of footsteps, the system localizes and tracks individuals by observing changes in the sequences It uses this tracking information to identify when an occupant leaves or enters a room. 3) The entering and leaving room information are combined with detected individual location information to update the room-level occupancy state of the building. Through validation experiments in two different buildings, our system was able to achieve 99.55% accuracy for event detection, less than three feet average error for localization, and 85% accuracy in occupancy counting.
Effects on blood pressure of reduced dietary sodium and the Dietary Approaches to Stop Hypertension (DASH) diet. DASH-Sodium Collaborative Research Group.

The effect of dietary composition on blood pressure is a subject of public health importance. We studied the effect of different levels of dietary sodium, in conjunction with the Dietary Approaches to Stop Hypertension (DASH) diet, which is rich in vegetables, fruits, and low-fat dairy products, in persons with and in those without hypertension.



A total of 412 participants were randomly assigned to eat either a control diet typical of intake in the United States or the DASH diet. Within the assigned diet, participants ate foods with high, intermediate, and low levels of sodium for 30 consecutive days each, in random order.



Reducing the sodium intake from the high to the intermediate level reduced the systolic blood pressure by 2.1 mm Hg (P<0.001) during the control diet and by 1.3 mm Hg (P=0.03) during the DASH diet. Reducing the sodium intake from the intermediate to the low level caused additional reductions of 4.6 mm Hg during the control diet (P<0.001) and 1.7 mm Hg during the DASH diet (P<0.01). The effects of sodium were observed in participants with and in those without hypertension, blacks and those of other races, and women and men. The DASH diet was associated with a significantly lower systolic blood pressure at each sodium level; and the difference was greater with high sodium levels than with low ones. As compared with the control diet with a high sodium level, the DASH diet with a low sodium level led to a mean systolic blood pressure that was 7.1 mm Hg lower in participants without hypertension, and 11.5 mm Hg lower in participants with hypertension.



The reduction of sodium intake to levels below the current recommendation of 100 mmol per day and the DASH diet both lower blood pressure substantially, with greater effects in combination than singly. Long-term health benefits will depend on the ability of people to make long-lasting dietary changes and the increased availability of lower-sodium foods.
Entry: visual programming to enhance children's computational thinking

This paper introduces Entry, a visual programming application which is developed to facilitate children's computational thinking. As an HTML5-based visual programming platform, it provides children and beginners with an integrated environment in which they not only learn programming in an easy and fun way but also create, post and share their own programs. This will enhance children's computational thinking and is expected to serve as an effective learning tool for the STEAM education. We have held a workshop with 16 elementary school teachers and conducted a preliminary survey to illuminate Entry's 1) potential educational implications on logical thinking, problem solving and creativity, and 2) applicability in the field of education. This paper is expected to contribute to future research on programming education and STEAM education programs for children.
vFPGAmanager: A Virtualization Framework for Orchestrated FPGA Accelerator Sharing in 5G Cloud Environments

Network operators are actively pushing towards the new 5G era and a crucial part to accomplish this is the Network Functions Virtualization (NFV). FPGAs and their hardware accelerators are a promising solution for NFV and 5G cloud environments because of their fast turnaround time and great speedup potential through application parallelism mapping on the reconfigurable fabric. Recently, consolidation reached a plateau in this field with lightweight virtualization techniques, that require a high overcommitment of FPGA accelerator resources to cope with numerous demands of guests. Although FPGAs can play an important role for the future 5G networks their capability to manage and control them from the upper layers of the software stack is inadequate. The lack of such support coupled with cloud integration and programmability issues can repel potential providers from utilizing FPGAs at their data centers. This paper presents the communication mechanism of the vFPGAmanager, an FPGA virtualization framework which can be orchestrated, monitored and enables accelerators overcommitment with direct guest access. These are key features to allow potential adopters of FPGA technology to include them in the next generation of NFV systems. The communication mechanism architecture is detailed and then benchmarked to show that even under heavy load on the system it demonstrates a minimal overhead to orchestrate and monitor the FPGA as a resource.
A SECURE ONLINE PAYMENT SYSTEM USING STEGANOGRAPHY AND VISUAL CRYPTOGRAPHY

In This paper we presents a new methodology which provides limited information only that is important in transferring money while shopping online provides security for customers data and increasing in confidence of customer and preventing identity stealing. Here we used cryptography technique for image encryption. Data is encrypted in images shares which are meaningless images. These shares are then transferred over an untrusted communication channel. By combining only these shares give the original secret image. This images may contain ATM card details, credit card information etc. At merchants side merchant have to pay some amount per transaction so it’s merchant’s responsibility to protect customers information from being misused. So here is solution for such operations.
Flow map layout

Cartographers have long used flow maps to show the movement of objects from one location to another, such as the number of people in a migration, the amount of goods being traded, or the number of packets in a network. The advantage of flow maps is that they reduce visual clutter by merging edges. Most flow maps are drawn by hand and there are few computer algorithms available. We present a method for generating flow maps using hierarchical clustering given a set of nodes, positions, and flow data between the nodes. Our techniques are inspired by graph layout algorithms that minimize edge crossings and distort node positions while maintaining their relative position to one another. We demonstrate our technique by producing flow maps for network traffic, census data, and trade data.
A Comparative Evaluation of Blockchain Systems for Application Sharing Using Containers

The Cloud computing paradigm is built on the concept of virtualization, allowing multiple virtual machines to cohabit on one physical device to enable the scaling up and down of applications through elastic on-demand provisioning. More recently containers e.g. Docker, have been shown to enable a more lightweight mechanism than hypervisors and proved to be a viable alternative for virtualization, based on shared operating systems. The advent of such lightweight environments has brought a multitude of application uses in research, science and industry, enabling pre-configured operating environments to be shared, reused and instantiated on demand. The sharing of containers has currently been exposed using centralized repositories (e.g. Dockerhub), which allows containers to be shared and to form the building blocks for further development. In this paper, we take a look at the next evolution of this lifecycle and consider whether it is viable to securely share container-based applications within a decentralized group of individuals and to provide an audit trail recording exactly who has shared what, and with whom. For this purpose we consider the use of Blockchain technologies, and consequently perform a comparative analysis of Blockchain technologies for this use case. The paper provides mostly a review and taxonomy of different ledger systems, which we believe may be of interest to the SafeData Workshop participants.