nils-herrmann/clean_scirepeval huggingface dataset:
A Microprocessor-Based Office Image Processing System

This paper presents a multiprocessor system architecture and the scheduling policies of the processors which can off-load a host computer in the image processing of office documents. The objective of this system is to provide extensive image processing capabilities at minimum cost and with an acceptable interactive response time. Multiprocessor system architecture, image partitioning strategies, image processing algorithm characteristics, performance modeling, and processor scheduling policies are investigated. A prototype multiprocessor image processing system has been built at the IBM San Jose Research Laboratory.
Online Routing of Stochastically Arriving Bundles in Delay Tolerant Networks

Future networks will expand current capabilities to service "outpost" regions in performance-challenging environments, in which link connectivity is intermittent, propagation delay may be extremely long, and/or communication conditions may be hostile. In such performance-challenging environments, a network is likely to be partitioned most of the time, and the message delivery mechanisms must have an opportune series of link-by-link transmittals from source to destination. The current Internet suite of protocols is less than adequate for networking in such performance-challenging environments, and new architecture and protocols, based on bundle transport, are being considered. A bundle is a message unit larger than a packet; it comprises a set of data that is sufficiently useful or meaningful to the application without additional data. In this paper, we focus on the online routing decision for a system in which bundle arrival times and bundle sizes are stochastic. We also model the time-varying link rates by stochastic processes. Then, we present an approach to "online" bundle routing based on "offline" traffic engineering. In this approach, we pre-compute nominal traffic flows based on the average available transmission rates of all links and the average rates of offered traffic for all source-destination node pairs. Then, online routing decisions are made on the basis of the nominal flows computed offline.
Dynamic routing and wavelength assignment algorithms for multifiber WDM networks with many wavelengths

We propose two dynamic RWA algorithms, the maximum-channel-path routing (MCPR) and the least utilized path routing (LUPR), which are suitable for multifiber WDM networks with many wavelengths available. Through our simulations on two network topologies, we compare the blocking probability of our proposed algorithms with the least-loaded routing (LLR) and the SPREAD, which has lower blocking probability than their predecessors. The proposed MCPR and the LUPR algorithms show lower blocking probability compared to the LLR algorithm. Our algorithms have higher blocking probability than SPREAD when the number of wavelengths per fiber is small, but they achieve more rapid improvement in blocking probability as the number of wavelengths per fiber increases. For sufficiently large number of wavelengths per fiber, MCPR yields blocking probability lower than SPREAD, and LUPR performs better than or similar to SPREAD, depending on the network topology. We believe that the reason is because MCPR and LUPR give preference to a path with more wavelengths available. Moreover, while the SPREAD algorithm considers all paths in the network to find the shortest path for an source-destination pair, our proposed algorithms search only the pre-determined paths, so their computational complexity is much less than that of SPREAD.
Online discriminative learning of phoneme recognition via collections of generalized linear models

We describe a new online discriminative learning algorithm that efficiently and effectively recognizes phonemes in a speech sequence. The method builds upon recent work in online learning of a collection of generalized linear models using second order statistics of the model weight vectors. Evaluation on the TIMIT database shows that the algorithm achieves state-of-the-art phoneme recognition error rates compared to many other generative and discriminative models with the same expressive power.
CCS-FOSSIL and dual-channel system that increases channel capacity per dynamic power range

We design a forest with nodes that represent orthogonal variable spreading factor (OVSF) sequences of different lengths (spreading codes of different lengths that can be used for multi-rate DS-CDMA). In addition to the non-descendant OVSF property exhibited in well-known tree-structured generation of sequences [(F. Adachi, et al., 1997) and (E.H. Dinan and B. Jabbari, 1998)], the sequences represented by the nodes of our forest have useful properties that can be used to achieve multi-channel communication (with a sequence providing a channel) with a lower total peak-to-mean envelope power ratio (PMEPR). These OSVF sequences grouped by our forest structure also have properties that facilitate symbol-by-symbol adaptation of the symbol duration in multi-rate CDMA systems. For example, certain lineages in the forest have the property that any pair of code sequences in the same lineage are shift orthogonal to each other, with the unit shift length being that of the shorter sequence. We present the forest-structured generation of the sequences (spreading codes) and their properties.
Policy management for BGP routing convergence using inter-AS relationship

The Internet routing instability, or the rapid fluctuation of network reachability information, is an important problem currently facing the Internet engineering community. High levels of network instability can lead to packet loss, increased network latency, and delayed routing convergence. At the extreme, high levels of routing instability can lead to the loss of internal connectivity in wide-area networks. In this paper, we investigate the variation of domain degree and domain count of the inter-domain network over time by using linear regression model in order to analyze the topology variation of inter-domain network. We also propose an efficient policy management model to reduce the instability in the inter-domain routing system. The proposed model can be used to identify whether a routing policy is adequate to reduce convergence time that is required to return to a normal state when BGP routing instability happens. Experimental analysis shows that the proposed model can be used to set up routing policy in domains for the purpose of minimizing the effects and the propagation of BGP routing instability.
Learning anisotropic ICP (LA-ICP) for robust and efficient 3D registration

This paper presents an online learning approach to 3D object registration that vastly improves the performance of Iterative Closest Point (ICP) methods. Our approach achieves better robustness and stable convergence by learning generalized distance functions directly from a stream of object depth data. The proposed algorithm, Learning Anisotropic ICP (LA-ICP), parameterizes the point uncertainty of the underlying object surface as an anisotropic Gaussian, and estimates the covariance parameters of the likelihood function for ICP from data. Our learning scheme does not require manual tuning and the parameters of the algorithm are continually updated from observed data. Experiments on various RGB-D object datasets demonstrate the effectiveness of our approach in terms of convergence and pose accuracy as well as robustness to initial conditions.
Near-optimal joint selection of transmit and receive antennas for MIMO systems

In this paper, we present a real-time low-complexity joint transmit and receive antenna selection algorithm for MIMO systems. We apply Ant Colony Optimization (ACO) to the joint antenna selection problem. The computational complexity of exhaustive search in this problem grows exponentially with the number of transmit and receive antennas. The proposed ACO for joint antenna selection has a low computational complexity, and its effectiveness is verified through simulation results.
Modeling of stacked-coil inductor from silicon integrated passive device technology

Stacked-coil inductor could be realized on multilevel thin-film structure with the configuration of two or more coils placed in different levels but shared the same magnetic flux. The configuration results mutual inductance to increase total inductance per unit area, it could make inductor compact to meet requirement of circuit on novel consumer electronic terminators. The metal coils of inductor on different levels are isolated by polymers which is an important component of IPD technology. Mechanical properties of polymers have strong influence on electric characteristic, which should be considered in model and will present in this paper. The scalable parameters of model are also extracted to implement process design kit (PDK) with parameterized inductors which could be used in system in package (SiP) or 3D IC interposer for RF applications.
Online, self-supervised terrain classification via discriminatively trained submodular Markov random fields

The authors present a novel approach to the task of autonomous terrain classification based on structured prediction. We consider the problem of learning a classifier that will accurately segment an image into "obstacle" and "ground" patches based on supervised input. Previous approaches to this problem have focused mostly on local appearance; typically, a classifier is trained and evaluated on a pixel-by-pixel basis, making an implicit assumption of independence in local pixel neighborhoods. We relax this assumption by modeling correlations between pixels in the submodular MRF framework. We show how both the learning and inference tasks can be simply and efficiently implemented-exact inference via an efficient max flow computation; and learning, via an averaged-subgradient method. Unlike most comparable MRF-based approaches, our method is suitable for implementation on a robot in real-time. Experimental results are shown that demonstrate a marked increase in classification accuracy over standard methods in addition to real-time performance.
Belief flows for robust online learning

This paper introduces a new probabilistic model for online learning which dynamically incorporates information from stochastic gradients of an arbitrary loss function. Similar to probabilistic filtering, the model maintains a Gaussian belief over the optimal weight parameters. Unlike traditional Bayesian updates, the model incorporates a small number of gradient evaluations at locations chosen using Thompson sampling, making it computationally tractable. The belief is then transformed via a linear flow field which optimally updates the belief distribution using rules derived from information theoretic principles. Several versions of the algorithm are shown using different constraints on the flow field and compared with conventional online learning algorithms. Results are given for several classification tasks including logistic regression and multilayer neural networks.
Power allocation for non-regenerative relaying in cognitive radio systems

In this paper, we present a low-complexity power allocation scheme for non-regenerative (amplify and forward) relaying in cognitive radio systems. The main objective of the power allocation is to maximize the signal to noise ratio (SNR) at the destination under the constraint of acceptable interference to the primary users (PU). In this paper, we propose an iterative power allocation using SNR upper Bound (IPAUB) for non-regenerative relaying. The proposed algorithm has low computational complexity, and we verify its effectiveness through simulation results.
Efficient resource minimization scheme for network coding-assisted multicast system

In this paper, we consider the problem of minimizing the resources used for network coding (MRUNC) while achieving the desired throughput in a multicast system. The problem of minimizing the number of network coding links is NP-hard. In this paper we propose a low-complexity Estimation of Distribution Algorithm (EDA) for MRUNC. Our EDA is applicable to the network with and without cycles. The numerical results show the effectiveness of the proposed method over previously proposed algorithms.
Thin film solar technologies: a review

This paper reviews the three main thin film solar cell technologies: amorphous silicon (α-Si), copper indium gallium selenide (CIGS), and cadmium telluride (CdTe). The evolution of these three technologies is discussed in comparison to commercial applications, reliability, and market share. While the α-Si cell is almost extinct in terrestrial applications, CIGS and CdTe are comparable to multicrystalline solar cells, which have greater than 55% market share. Therefore, CIGS and CdTe thin film technologies are expected to compete with the crystalline solar cell technology. However, the longevity of thin film solar cells remains a problem that begs an answer before it can be explored on building integrated photovoltaic systems.
Active stabilization of a humanoid robot for impact motions with unknown reaction forces

During heavy work, humans utilize whole body motions in order to generate large forces. In extreme cases, exaggerated weight shifts are used to impart large impact forces. There have been approaches to design stable whole body impact motions based on precise dynamic models of the robot and the target object, but they have practical limitations as the uncertainty in the ensuing reaction forces can lead to instability. In the current work, we describe a motion controller for a humanoid robot that generates impacts at an end effector while keeping the robot body balanced before and after the impact. Instead of relying on the accuracy of the impact dynamics model, we use a simplified model of the robot and biomechanically motivated push recovery controllers to reactively stabilize the robot against unknown perturbations from the impact. We demonstrate our approach in physically realistic simulations, as well as experimentally on a small humanoid robot platform.
Cooperative humanoid stretcher manipulation and locomotion

In this paper and accompanying video, we demonstrate a humanoid robotic system where two humanoids are physically constrained by the cooperative manipulation of a rigid stretcher body. The humanoids walk together as a virtual quadruped, showing how principles of quadruped gait generation can be applied to cooperating humanoids. In the experiments, two DARwIn-OP humanoid robots estimate the position and orientation of the stretcher, in order to approach it and pick it up. Once they are standing with the stretcher in their hands, they walk by synchronizing their gaits to simulate a virtual quadruped. By systemically tuning the underlying gait parameters, we show how the cooperative humanoids can carry the stretcher in the most stable fashion.
Tight Bound on the Error Probability of Rotation Code in Rayleigh Fading Channels

In this paper, we present a novel method to derive a tight upper bound on the symbol error probability (SEP) of a rotation coded scheme in the case of Rayleigh fading channels. This upper bound has a closed-form expression and provides results closer to the exact expression than the union-Chernoff bound. We present numerical simulations that show the tightness of the proposed analytical upper bound. Based on the tight upper bound, we also find the optimal rotation angle, which minimizes the derived tight upper bound expression of the SEP.
Rao-Blackwellized particle filtering for 6-DOF estimation of attitude and position via GPS and inertial sensors

The authors present an innovative method for the efficient joint estimation of attitude and position in six degrees of freedom via sensors such as GPS, inertial measurement units, and odometry. Traditional methods for attitude estimation via Kalman filtering are beset by many conceptual problems relating to the representation of orientations in linear spaces, leading to difficulties in implementation and the interpretation of uncertainty estimates, among other issues. These problems are compounded when it is necessary to jointly estimate position and attitude. We demonstrate how Rao-Blackwellized particle filtering provides a framework for approaching this estimation problem that is both conceptually appealing and practical. Results are shown that demonstrate the filter's robustness to sensor outages and its ability to perform well even in situations with noisy sensors and high initial uncertainty in all state dimensions; these situations are precisely those in which traditional Kalman filtering approaches are most likely to experience problems
III-V/Si Hybrid Laser Arrays Using Back End of the Line (BEOL) Integration

Creating arrays of efficient optical sources to enable dense integration of silicon photonic transceivers with silicon very large-scale integration circuits remains a challenge. We review manufacturing strategies and challenges for back-end-of-line integration of tunable, external-cavity lasers using edge-coupled and surface-normal-coupled integration approaches showing that such laser arrays can be manufactured with a fab-less model. We demonstrate, for the first time, hybrid laser arrays using both integration approaches highlighting performance and design differences. We also discuss challenges and opportunities for each method and present experimental techniques to reduce alignment tolerance and improve laser stability and mode control.
An uplink packet relay protocol for CDMA cellular-like systems

We present a simple design of a packet relay protocol that can be used for the reverse channel data traffic in CDMA TDD cellular systems. We name it synchronous Location Dependent Packet Relay (synchronous-LDPR) protocol. Mobile users in this scheme can relay its data packets via several mobile stations to the base station. By doing so, the total required power to deliver a data packet can be reduced, and the system capacity can be increased due to the reduction of the other cell interference. The synchronous-LDPR scheme utilizes a mobile's uplink time slot not in use for transmitting its own traffic. This scheme can be superimposed on the current CDMA TDD systems.
Design and Analysis of Symbol Detection Schemes with Imperfect CSI for SDF-Relay Networks

In this paper, we study the issue of optimally detecting symbols for a Selective-Decode-and-Forward (SDF) relay-assisted communication system in the presence of channel estimation error for flat Rayleigh fading channels. In the system studied, a pilot symbol-assisted modulation (PSAM) scheme is used along with a channel estimation scheme based on minimum mean square error (MMSE) criterion. In particular, we present a novel spectrum detection in presence of channel estimation error for the SDF protocol. Numerical simulation results are presented to show that the presented spectrum detection scheme performs well in terms of bit error probability (BEP).
A mock circulatory loop for designing and evaluating total artificial hearts

A mock circulatory loop was constructed to facilitate total artificial heart development. The loop includes many novel features such as a pressure-regulated tank to simulate exercise conditions, controllable systemic and pulmonary vascular resistance to create left-right flow imbalances as seen in postural change and breathing, and a left atrial suction valve. Dual HeartMate II pumps and the BiVACOR® rotary total artificial heart were used to generate pressure and flow data characterizing the flow loop.
Dynamic and probabilistic estimation of manipulable obstacles for indoor navigation

In this paper we derive and implement an algorithm for an indoor mobile robotics platform to estimate the manipulability of initially unknown obstacles while navigating through its environment to a pre-specified goal. The environment is represented by an evidence grid, where each cell contains a gamma-distributed cost as well as visual feature data in the form of a color histogram. While navigating, the robot associates visual features of objects occupying a given cell with manipulability cost estimates of that cell, learning whether an object or obstacle can be moved or not in the robot's attempt to reach the goal. We derive and utilize a lower confidence bound (LCB) estimate for the cost of each cell in order to incorporate an exploration (versus pure exploitation) element to the robot's search for the lowest-cost path. Combining the LCB cost estimates with the dynamic replanning search algorithm D*-Lite, we can quickly compute optimal navigation paths regardless of the numerous changes occurring in the robot's environmental belief state. We explain the probabilistic representation of cost in the evidence grid and provide simulation and real-world results for our algorithm in a navigation scenario with static and movable objects.
A Knowledge-based Environment For The Development Of Software Parts Composition Systems

A number of systems have been developed that demonstrate the utility of knowledge-based techniques in the construction of software reuse systems. The amount of manual effort expended in the development of such systems can be reduced through the use of a knowledge acquisition environment tailored for a reuse-oriented model of the software development process. We present a model of the software development process and then describe the Bauhaus, a knowledge-based user interface to Ada software libraries.
THOR-OP humanoid robot for DARPA Robotics Challenge Trials 2013

This paper describes the hardware design and motion control algorithms that have been used by Team THOR in the DARPA Robotics Challenge (DRC) Trials 2013 competition. The robotic hardware we use, the THOR-OP robot, consists of standardized and general purpose actuators and structural components, which greatly reduce the build and reconfiguration time and allows for quick field repair capability. Our software framework is also composed of fully modular function modules. This modular structure helps us to keep up easily with hardware changes and to have multiple control options to suit various situations. We validated our approach at the DRC Trials where we fared well against other robots many times more expensive and acquired the finalist status.
In vitro study of an intra-aortic VAD: Effect of reverse-rotating mode on ventricular recovery

Cardiac recovery has been observed in end-stage heart failure patients with mechanical circulatory support. An intra-aortic ventricular assist device (IntraVAD) is a novel rotary blood pump designed to operate in the ascending aorta behind the aortic valve, working in series with the compromised left ventricle (LV). Such a device requires optimal motion control in order to enhance the myocardial perfusion and thus promote cardiac recovery. Therefore, a reverse-rotating control (RRc) mode has been proposed to increase the mean arterial pressure (MAP) in diastole where the most coronary flow occurs. The RRc mode consists of two motions - forward rotating speed (FS) and reversely rotating speed (RS). The capability of cardiac recovery of three control modes, including `continuous', `on/off ' and `RRc' modes, was evaluated in vitro. Stroke work (SW), ventricular volume, coronary perfusion pressure (CPP), and arterial pulsatility index (API) were used to evaluate LV unloading, myocardial perfusion and arterial pulsatility. The results show that, all three modes increased the LV stroke work (0.98W vs 1.00W vs 1.01W for continuous, on/off and RRc, respectively; baseline 0.9W) and decreased both end-diastolic volume (EDV) and end-systolic volume (ESV). The “RRc” mode improved CPP significantly (78.4mmHg compared to 66.4 mmHg and 70.9mmHg for continuous and on/off modes; baseline 71mmHg). The arterial pulsatility was higher in `RRc' mode (0.84 compared to 0.43 and 0.59; baseline 0.48). In summary, the IntraVAD operating in the RRc mode can successfully unload the LV, enhance the myocardial perfusion, and restore the arterial pulsatility; therefore, it could be a promising therapeutic option to bridge heart failure patients to recovery.