nils-herrmann/clean_scirepeval huggingface dataset:
Optimal demand response for thermal inertial loads employing stochastic renewables: A privacy respecting architecture and its continuum scaling limit

We consider the problem of optimally utilizing intermittently available renewable energy sources for inertial thermal loads. The particular focus of this paper is to reduce the need for dispatchable fossil fuel generation backup reserves, while satisfying thermal constraints of the loads, and to do so within an architecture where the individual loads control their own demands in a distributed manner so that privacy of every load's thermal state is respected. In this distributed architecture, no information about load states is communicated to the load aggregator. We model th e system as a distributed stochastic control problem with a control law parametrized by the individual set points of the loads, where the stochastic uncertainty is induced both by renewable energy availability as well as user temperature constraints. We design, optimize and analyze the parametrized policy, which has the key properties that it is privacy-respecting, employs distributed control via individual set-points, has low communication requirements, and is architecturally simple. While the optimal policy for a finite number of loads is intractable, and increasingly so as the number of loads increases, we show that the infinite population scaling limit as the number of loads increases is tractable. We employ Pontryagin's Minimum Principle to evaluate this, and show that it provides a near-optimal solution for the finite case.
Distributed estimation using reduced dimensionality sensor observations: A separation perspective

We consider distributed estimation for a geographically dispersed sensor network, where sensors collect observations that are linearly pre-processed and transmitted over dimensionality-constrained channels. A central processor utilizes the received sensor data to obtain a linear estimate of the desired signal. In this scenario, we consider the optimal preprocessing at the sensors under a mean squared error (MSE) metric. In the single-sensor case, applying a modification of Sakrison's separation principle we show that the optimal preprocessing can be decomposed into two steps: a LMMSE estimate followed by a (linear) MSE optimal dimensionality reduction of the estimate. The latter is readily obtained as the well-known Karhunen-Loeve transform (KLT). Under the multi-sensor scenario, we extend this result to show that given the pre-processing at other nodes, each node's optimal linear pre-processing again reduces to a side-informed linear estimation followed by a side-informed version of the KLT. The separation perspective thus provides a simple and intuitive derivation of the optimal linear pre-processing under reduced dimensionality channels.
Molecular mechanics simulation of bio-nanorobotic components using force feedback

This paper presents a molecular mechanics study using a molecular dynamics software (NAMD) coupled to virtual reality (VR) techniques for intuitive bio-nanorobotic prototyping. Using simulated bio-nano environments in VR, the operator can design and characterize through physical simulation and 3-D visualization the behavior of bionanorobotic components and structures. The main novelty of the proposed simulations is based on the characterization of stiffness performances of passive joints-based deca-alanine protein molecule and active joints-based viral protein motor (VPL) in their native environment. Their use as elementary bionanorobotic components (1 dof platform) are also simulated and the results discussed
Thermal modeling and simulation of a package-on-package embedded micro wafer level package (EMWLP) structure at the package and system-level

In the embedded wafer-level packaging field, the embedded micro wafer level package (EMWLP) technology leverages on fan-out redistribution connections, keeping the reliance on wire-bonding and flip-chip bump connections to a minimum, thus streamlining the packaging process. As the embedded micro wafer level packaging (EMWLP) technology evolves to capitalize on package-on-package (POP) technology, this study's parametric thermal modeling focuses on analyzing the thermal impact of increased density in a low thermal conductivity packaging material. The package design in this work has the following specifications: the memory EMWLP (die size: 7×7×0.2mm) on logic EMWLP structure (die size : 8×8×0.2mm), sized 12×12×0.9mm in total. The POP structure, furthermore, incorporates through-mold-interconnects (TMI) in the bottom logic package to provide short electrical connection from the top package to the board level. The impact on multi-package thermal resistance is then studied as the following parameters vary: power dissipation (in both packages); mold conductivity; number of TMIs; number of solder ball connections; inclusion and size of an interpackage heatslug; inclusion and thermal conductivity of a top heatspreader. The thermal management of the dies are also studied based on the above measures and was found to accommodate under 4W of total POP power. Finally, simulation results for a general system-level thermal modeling of the EMWLP POP in a cellphone scenario are also conducted with passive thermal management solutions proposed.
Improving computational efficiency for RNA secondary structure prediction via data-adaptive alignment constraints

The most accurate methods for RNA secondary structure prediction simultaneously predict the common structure and alignment among multiple homologs. In addition to dynamic programming, practical algorithms utilize heuristics to restrict the search space and further reduce time and memory requirements. This work is directed toward improving these heuristics in order to reduce computation without a compromise in accuracy. In this paper, a new, principled method for restricting the alignment search space in Dynalign  is introduced. Our results indicate that we are able to improve runtime with little affect on the accuracy of the structure predictions. This work utilizes Dynalign, but this method is also applicable to other structure prediction programs.
Camera Scheduling and Energy Allocation for Lifetime Maximization in User-Centric Visual Sensor Networks

We explore camera scheduling and energy allocation strategies for lifetime optimization in image sensor networks. For the application scenarios that we consider, visual coverage over a monitored region is obtained by deploying wireless, battery-powered image sensors. Each sensor camera provides coverage over a part of the monitored region and a central processor coordinates the sensors in order to gather required visual data. For the purpose of maximizing the network operational lifetime, we consider two problems in this setting: a) camera scheduling, i.e., the selection, among available possibilities, of a set of cameras providing the desired coverage at each time instance, and b) energy allocation, i.e., the distribution of total available energy between the camera sensor nodes. We model the network lifetime as a stochastic random variable that depends upon the coverage geometry for the sensors and the distribution of data requests over the monitored region, two key characteristics that distinguish our problem from other wireless sensor network applications. By suitably abstracting this model of network lifetime and utilizing asymptotic analysis, we propose lifetime-maximizing camera scheduling and energy allocation strategies. The effectiveness of the proposed camera scheduling and energy allocation strategies is validated by simulations.
Collusion-resilient fingerprinting by random pre-warping

Fingerprinting of audio-visual content using digital watermarks is an effective means of determining originators of unauthorized/pirated copies. Watermarks embedded in content can trace the traitor responsible for piracy. Multiple users may, however, collude and collectively escape identification by creating an average of their individually watermarked copies that appears unwatermarked. We propose a novel collusion-resilience mechanism, wherein the host signal is warped randomly prior to watermarking. As each copy undergoes a distinctive warp, collusion through averaging either yields low-quality results or requires substantial computational resources to undo random warps. The method is independent of the watermarking scheme used and imposes no restrictions on the watermark signal. We demonstrate the effectiveness of this approach on digital images.
An Acute Position Based VANET Routing Protocol

Vehicular Ad-hoc Networks (VANETs) is highly dynamic by nature because in VANETs, nodes are highly mobile. And the survey shows that the position based routing protocol is best suitable for VANETs because position based routing approach can deal with highly mobile nodes. In this paper, we propose an acute position based routing protocol (APR) for vehicular ad hoc networks (VANETs) which performs better when the number of vehicle increases on the road as compare to GPSR. In this, we assume that all vehicles know their own position and have Global Positioning System (GPS). Our protocol uses restricted greedy to transfer a data packet and for recovery carry and forwarding strategy will used. We consider both the vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. All the intersection point on the road have a RSU (Road Side Unit) which takes all the decisions that on which path the data packet will flow. If there is no appropriate path then the RSUs will store the data packet until they find the appropriate path to transfer the data packet. This protocol is suitable for city scenario. Simulation is done in NS-2.35 and compared APR with GPSR.
Hybrid sensor and mesh networks: paradigms for fair and energy efficient communication

The issues pertaining to capacity, fairness, and energy efficiency are central to all wireless networks, including wireless sensor and mesh networks. In this paper, we consider hybrid sensor and mesh networks as paradigms for fair and energy efficient communication. A hybrid network consists of sensor or mesh nodes with wireless communication capability and some infrastructure in the form of cables with wireless transceivers attached to their ends. We investigate the trade-offs that exist between the amount of infrastructure and the improvement in fairness and energy efficiency over a pure wireless network. In particular, we show that a limited amount of infrastructure is enough to significantly improve fairness as well as energy efficiency. Moreover, in many scenarios the aggregate capacity of the network can also be substantially improved with the help of a limited infrastructure.
Discriminatively Trained Latent Ordinal Model for Video Classification

We address the problem of video classification for facial analysis and human action recognition. We propose a novel weakly supervised learning method that models the video as a sequence of automatically mined, discriminative sub-events (e.g., onset and offset phase for “smile”, running and jumping for “highjump”). The proposed model is inspired by the recent works on Multiple Instance Learning and latent SVM/HCRF - it extends such frameworks to model the ordinal aspect in the videos, approximately. We obtain consistent improvements over relevant competitive baselines on four challenging and publicly available video based facial analysis datasets for prediction of expression, clinical pain and intent in dyadic conversations, and on three challenging human action datasets. We also validate the method with qualitative results and show that they largely support the intuitions behind the method.
Delay and Capacity Trade-Offs in Mobile Ad Hoc Networks: A Global Perspective

Since the original work of Grossglauser and Tse, which showed that mobility can increase the capacity of an ad hoc network, there has been a lot of interest in characterizing the delay-capacity relationship in ad hoc networks. Various mobility models have been studied in the literature, and the delay-capacity relationships under those models have been characterized. The results indicate that there are trade-offs between the delay and capacity, and that the nature of these trade-offs is strongly influenced by the choice of the mobility model. Some questions that arise are: (i) How representative are these mobility models studied in the literature? (ii) Can the delay-capacity relationship be significantly different under some other "reasonable" mobility model? (iii) What sort of delay-capacity trade-off are we likely to see in a real world scenario? In this paper, we take the first step toward answering some of these questions. In particular, we analyze, among others, the mobility models studied in recent related works, under a unified framework. We relate the nature of delay-capacity trade-off to the nature of node motion, thereby providing a better understanding of the delay-capacity relationship in ad hoc networks in comparison to earlier works.
Expanded Parts Model for Semantic Description of Humans in Still Images

We introduce an Expanded Parts Model (EPM) for recognizing human attributes (e.g., young, short hair, wearing suits) and actions (e.g., running, jumping) in still images. An EPM is a collection of part templates which are learnt discriminatively to explain specific scale-space regions in the images (in human centric coordinates). This is in contrast to current models which consist of a relatively few (i.e., a mixture of) ‘average’ templates. EPM uses only a subset of the parts to score an image and scores the image sparsely in space, i.e., it ignores redundant and random background in an image. To learn our model, we propose an algorithm which automatically mines parts and learns corresponding discriminative templates together with their respective locations from a large number of candidate parts. We validate our method on three recent challenging datasets of human attributes and actions. We obtain convincing qualitative and state-of-the-art quantitative results on the three datasets.
A study of position based VANET routing protocols

In VANETs, vehicles communicate with each other or with the road side units (RSU) to avoid accidents and traffics. For this communication, VANETs need a routing protocol which helps vehicles to exchange information in the network. Due to dynamic nature of VANET, routing the packets in the vehicular network is one of the challenging tasks. VANETs have many routing protocols but position based routing protocol is more promising one, as it supports geographical position information of vehicles to provide efficient routing in vehicular networks. In this paper, different position based routing protocols are presented. The purpose of this survey is to present the effective position based routing protocols with their sound parameters for the concerned readers.
Improved specular regions localization and optical-flow based motion estimation via joint processing

For specular regions (SRs), the assumption of brightness (or other) constancy between images corresponding to multiple views of a scene breaks down. As a consequence, optical-flow (OF) based motion-estimation (ME) algorithms that rely on constancy assumptions fail for specular regions. At the same time estimation of SRs in an image is also prone to errors, particularly to false positives from bright regions in the scene. In this paper, motivated by the fact that specular regions are typically encountered in image regions corresponding to portions of relatively smooth 3D surfaces, we propose an algorithm for improving ME and SRs localization via joint processing. Initial estimates of OF and of the SRs are obtained by conventional methods. The estimate of the SRs is updated using inconsistency of the OF with respect to the neighboring region to reinforce true positives and to reject false positives. The OF is then re-computed with a modified energy functional that, in effect, emphasizes regularization in a spatially adaptive neighborhood of the SRs to improve the estimated OF. Experimental results on synthetic and real image pairs demonstrate that the proposed algorithm offers a significant improvement in both SRs localization and ME over recently proposed methods for tackling these problems.
Big data challenges in decoding cortical activity in a human with quadriplegia to inform a brain computer interface.

Recent advances in Brain Computer Interfaces (BCIs) have created hope that one day paralyzed patients will be able to regain control of their paralyzed limbs. As part of an ongoing clinical study, we have implanted a 96-electrode Utah array in the motor cortex of a paralyzed human. The array generates almost 3 million data points from the brain every second. This presents several big data challenges towards developing algorithms that should not only process the data in real-time (for the BCI to be responsive) but are also robust to temporal variations and non-stationarities in the sensor data. We demonstrate an algorithmic approach to analyze such data and present a novel method to evaluate such algorithms. We present our methodology with examples of decoding human brain data in real-time to inform a BCI.
Lossless watermarking for image authentication: a new framework and an implementation

We present a novel framework for lossless (invertible) authentication watermarking, which enables zero-distortion reconstruction of the un-watermarked images upon verification. As opposed to earlier lossless authentication methods that required reconstruction of the original image prior to validation, the new framework allows validation of the watermarked images before recovery of the original image. This reduces computational requirements in situations when either the verification step fails or the zero-distortion reconstruction is not needed. For verified images, integrity of the reconstructed image is ensured by the uniqueness of the reconstruction procedure. The framework also enables public(-key) authentication without granting access to the perfect original and allows for efficient tamper localization. Effectiveness of the framework is demonstrated by implementing the framework using hierarchical image authentication along with lossless generalized-least significant bit data embedding.
Device temporal forensics: An information theoretic approach

By formulating the problem of ordering the outputs observed from a device over time, we pose a new problem in forensics and propose a framework for addressing this problem of device temporal forensics. Our proposed framework is based on a two-stage approach wherein time-dependent device parameters are first estimated from observed outputs and the resulting estimates are then temporally ordered by employing a Markov model for the temporal evolution of device parameters and exploiting the data processing inequality in information theory. We demonstrate and evaluate a simple realization of the framework for digital camera forensics based on photo-response non-uniformity. Results obtained over a database of online images indicate that the method provides accurate temporal ordering.
Requested-QOS driven runtime reconfiguration of mobile devices

The present paper embodies a technique for on-the-fly remote reconfiguration of mobile device for power optimization according to the requested quality of service (RQoS) from user. The method is initiated by mobile device user who sends RQoS parameters to remote server, where the multimedia algorithms are re-optimized as per RQoS, and a new bit-stream is transmitted to reconfigure the device for low power with acceptable degradation in quality. Experimental results show that the approach is able to reduce bitwidth by 64.31% to 73.95% for varying PSNR from 40dB to 26dB. Much higher power savings can be achieved if user is willing to tolerate more quality degradation.
CMOS-memristor inverter circuit design and analysis using Cadence Virtuoso

Memristor is known for its primary fundamental property called the variation of resistance with memory and time, thus memristor name combines memory plus resistor. That's why the major applications for memristors are investigated solely in the field of neuromorphic systems and memory. This paper investigates the benefits of combining CMOS logic along with memristors. It is because of the way that this field is currently existing in its initial phases of exploration, different sorts of models exists in memristor, and no specific agreement which suggests the best portray of physical properties. All together further exploration is regardless until the consideration of hybrid memristor. memristor is considered for implementation of logic groups like the memristors as logic inside memory, memristance as calculation units. The paper incorporates the usage of CMOS-memristor combination Inverter utilizing Cadence Virtuoso. Its DC response is registered at different input Voltages and contrasted with standard CMOS 180 nanometer technology.
Large Scale Novel Object Discovery in 3D

We present a method for discovering never-seen-before objects in 3D point clouds obtained from sensors like Microsoft Kinect. We generate supervoxels directly from the point cloud data and use them with a Siamese network, built on a recently proposed 3D convolutional neural network architecture. We use known objects to train a non-linear embedding of supervoxels, by optimizing the criteria that supervoxels which fall on the same object should be closer than those which fall on different objects, in the embedding space. We test on unknown objects, which were not seen during training, and perform clustering in the learned embedding space of supervoxels to effectively perform novel object discovery. We validate the method with extensive experiments, quantitatively showing that it can discover numerous unseen objects while being trained on only a few dense 3D models. We also show very good qualitative results of object discovery in point cloud data when the test objects, either specific instances or even categories, were never seen during training.
A control system framework for privacy preserving demand response of thermal inertial loads

We address a problem in demand response for the class of thermal inertial loads: How to control the total demand of a set of loads, and also what to control it to, all while respecting constraints on load states, as well as maintaining privacy of load states. The architecture we investigate consists of an aggregator or Load Serving Entity (LSE) serving several thermostatically controlled loads (TCLs), whose set-points are controlled by the LSE to follow a desired total power consumption. We propose a modeling, analysis and design framework for the LSE that accomplishes this goal by solving a hierarchy of two control problems. The first control problem consists of determining an optimal aggregate power trajectory given the price forecasts. The second problem is that of controlling the set-points of the TCL population so as to drive their total power consumption to follow the optimal power trajectory. We show how the LSE can solve these control problems while guaranteeing privacy and contractual comfort bounds for the TCL states. Numerical simulations illustrate the efficacy of the proposed framework.
Improved one-pass IMS authentication in UMTS

Universal Mobile Telecommunications System (UMTS), as a core network can support both circuit-switch as well as packet-switch networks. UMTS supports IP Multimedia services by including the IP Multimedia Subsystem (IMS) as part of its core network. IMS services are used by authenticating user equipment with the UMTS using UMTS AKA and then with the IMS using IMS AKA. This paper concentrates on the IMS authentication and proposes an improved one-pass IMS authentication procedure which will result in reduction in message delivery cost and improvement over IMS AKA. This proposed procedure also preserves security features such as mutual authentication and key agreement expected from an IMS authentication procedure.