nils-herrmann/clean_scirepeval huggingface dataset:
Design of high-speed wireline transceivers for backplane communications in 28nm CMOS

This paper describes the design of the architecture and circuit blocks for backplane communication transceivers. A channel study investigates the major challenges in the design of high-speed reconfigurable transceivers. Architectural solutions resolving channel-induced signal distortions are proposed and their effectiveness on various channels is investigated. Subsequently, the paper describes the design of a 0.6-13.1Gb/s fully-adaptive backplane transceiver embedded in state-of-the-art low-leakage 28nm CMOS FPGAs. The receiver front-end utilizes a 3-stage CTLE, a 7-tap speculative DFE, and a 4-tap sliding DFE to remove the immediate post-cursor ISI up to 64 taps. The clocking network provides continuous operation range between 0.6-13.1Gb/s. The transceiver achieves BER <; 10-15 over a 31dB-loss backplane at 13.1Gb/s and over channels with 10GBASE-KR characteristics at 10.3125Gb/s.
Information diffusion in online social networks: a survey

Online social networks play a major role in the spread of information at very large scale. A lot of effort have been made in order to understand this phenomenon, ranging from popular topic detection to information diffusion modeling, including influential spreaders identification. In this article, we present a survey of representative methods dealing with these issues and propose a taxonomy that summarizes the state-of-the-art. The objective is to provide a comprehensive analysis and guide of existing efforts around information diffusion in social networks. This survey is intended to help researchers in quickly understanding existing works and possible improvements to bring.
Safe robotic grasping: Minimum impact-force grasp selection

This paper addresses the problem of selecting from a choice of possible grasps, so that impact forces will be minimised if a collision occurs while the robot is moving the grasped object along a post-grasp trajectory. Such considerations are important for safety in human-robot interaction, where even a certified “human-safe” (e.g. compliant) arm may become hazardous once it grasps and begins moving an object, which may have significant mass, sharp edges or other dangers. Additionally, minimising collision forces is critical to preserving longevity of robots which operate in uncertain and hazardous environments, e.g. robots deployed for nuclear decommissioning, where removing a damaged robot from a contaminated zone for repairs may be extremely difficult and costly. Also, unwanted collisions between a robot and critical infrastructure (e.g. pipework) in such high-consequence environments can be disastrous. In this paper we investigate how the safety of the post-grasp motion can be considered during the pre-grasp approach phase, so that the selected grasp is optimal in terms of applying minimum impact forces if a collision occurs during the desired post-grasp manipulation. We build on the methods of augmented robot-object dynamic model and “effective mass” and propose a method for combining these concepts with modern grasp and trajectory planners, to enable the robot to achieve a grasp which maximises the safety of the post-grasp trajectory, by minimising potential collision forces. We demonstrate the effectiveness of our approach through several experiments with both simulated and real robots.
Beyond frontal faces: Improving Person Recognition using multiple cues

We explore the task of recognizing peoples' identities in photo albums in an unconstrained setting. To facilitate this, we introduce the new People In Photo Albums (PIPA) dataset, consisting of over 60000 instances of ~2000 individuals collected from public Flickr photo albums. With only about half of the person images containing a frontal face, the recognition task is very challenging due to the large variations in pose, clothing, camera viewpoint, image resolution and illumination. We propose the Pose Invariant PErson Recognition (PIPER) method, which accumulates the cues of poselet-level person recognizers trained by deep convolutional networks to discount for the pose variations, combined with a face recognizer and a global recognizer. Experiments on three different settings confirm that in our unconstrained setup PIPER significantly improves on the performance of DeepFace, which is one of the best face recognizers as measured on the LFW dataset.
A simple probabilistic spiking neuron model with Hebbian learning rules

Traditional spiking neural networks (SNNs) uses simulated spiking neuron models for computation units. Action potentials (APs or spikes) are generated when the integrated sensory or synaptic inputs to a neuron reach a threshold value. However, spiking generation is not a deterministic process, making current models limited for their potentials and applications. Here we consider the effects of adding probabilistic parameters to the spiking neuron model, which controls the synapses established during spiking generation and transmitting. The Hebbian learning rule is employed for controlling the probabilistic parameters self-adaptation and connection weights associated with the synapses are established using Thorpe's rule during the network learning procedure. The proposed framework combines the essence of stochastic characteristics of the cortical neurons in vivo, the biologically plausibility of Hodgkin-Huxley type neuron dynamics, as well as the computational efficiency of integrate-and-fire (I&F) type neurons. A simple simulation acquired following aforementioned instructions (based on Izhivich's SNN model) exhibits more explicit behavior and robust performance than the original model and deterministic network organizations.
Performance evaluation of OFDM-based 256- and 1024-QAM in multipath fading propagation conditions

The use of higher-order modulation is an important approach for increasing data rates within a limited bandwidth. In this paper, we demonstrate the performance of the orthogonal frequency division multiplexing (OFDM)-based 256- and 1024-QAM in multipath fading propagation conditions by link-level simulation, as well as under static propagation condition. The BER performance is investigated for two types of propagation models, i.e., “extended pedestrian A” and “extended vehicular A” models, specified by 3GPP as parameters of the coding rates. Using these results, we validate the application of OFDM-based 256- and 1024-QAM to mobile communication systems.
A Multiphase Switched-Capacitor DC–DC Converter Ring With Fast Transient Response and Small Ripple

A fully integrated step-down switched-capacitor dc–dc converter ring with 123 phases has been designed that could achieve fast dynamic voltage scaling for the microprocessor of wearable devices. The symmetrical multiphase converter ring surrounds its load in the square and supplies power to the on-chip power grid that is easily accessible at any point of the chip edges. The frequency of the <inline-formula> <tex-math notation="LaTeX">$V_{\mathrm {DD}}$ </tex-math></inline-formula>-controlled oscillator is adjusted through its supply voltage <inline-formula> <tex-math notation="LaTeX">$V_{\mathrm {DD}}$ </tex-math></inline-formula>, which allows the unity-gain frequency to be designed higher than the switching frequency. The converter ring has been fabricated in a low-leakage 65-nm CMOS process. This converter achieves a response time of 3 ns, a reference tracking speed of 2.5 V/<inline-formula> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula>, and a minimum output ripple of 2.2 mV. The peak efficiency is 80% at the power density of 66.6 mW/mm<sup>2</sup>, and the maximum power density is 180 mW/mm<sup>2</sup>.
Prospects and limitations of non-invasive blood glucose monitoring using near-infrared spectroscopy

Diabetes is a chronic metabolic disorder which can lead to severe complications and affect all vital organs. The occurrence of complications due to diabetes can be prevented by regular monitoring and maintaining the blood glucose level in the normal range. Most of the commercially available devices for glucose measurement are invasive or minimally invasive. Invasive devices used for blood glucose monitoring are inconvenient and painful whereas minimal invasive devices have limited time span and stability. Thus, there is a need of an economic, compact, painless and convenient non-invasive device which can promote lood glucose monitoring ear-infrared spectroscopy on-invasive glucose monitoring frequent blood testing which help in control of blood glucose level. In this paper various methods of glucose monitoring are reviewed and overall emphasis is laid on the development of NIRS (near-infrared spectroscopy) based non-invasive glucose monitoring. The motivation of this review is to demonstrate the prospects, limitations and technical challenges for development of NIRS based non-invasive blood glucose measurement system.
Secrets of optical flow estimation and their principles

The accuracy of optical flow estimation algorithms has been improving steadily as evidenced by results on the Middlebury optical flow benchmark. The typical formulation, however, has changed little since the work of Horn and Schunck. We attempt to uncover what has made recent advances possible through a thorough analysis of how the objective function, the optimization method, and modern implementation practices influence accuracy. We discover that “classical” flow formulations perform surprisingly well when combined with modern optimization and implementation techniques. Moreover, we find that while median filtering of intermediate flow fields during optimization is a key to recent performance gains, it leads to higher energy solutions. To understand the principles behind this phenomenon, we derive a new objective that formalizes the median filtering heuristic. This objective includes a nonlocal term that robustly integrates flow estimates over large spatial neighborhoods. By modifying this new term to include information about flow and image boundaries we develop a method that ranks at the top of the Middlebury benchmark.
Scheduling and locking in multiprocessor real-time operating systems

Scheduling and Locking in Multiprocessor Real-Time Operating Systems (Under the direction of James H. Anderson) With the widespread adoption of multicore architectures, multiprocessors are now a standard deployment platform for (soft) real-time applications. This dissertation addresses two questions fundamental to the design of multicore-ready real-time operating systems: (1) Which scheduling policies offer the greatest flexibility in satisfying temporal constraints; and (2) which locking algorithms should be used to avoid unpredictable delays? With regard to Question 1, LITMUSRT, a real-time extension of the Linux kernel, is presented and its design is discussed in detail. Notably, LITMUSRT implements link-based scheduling, a novel approach to controlling blocking due to non-preemptive sections. Each implemented scheduler (22 configurations in total) is evaluated under consideration of overheads on a 24-core Intel Xeon platform. The experiments show that partitioned earliest-deadline first (EDF) scheduling is generally preferable in a hard real-time setting, whereas global and clustered EDF scheduling are effective in a soft real-time setting. With regard to Question 2, real-time locking protocols are required to ensure that the maximum delay due priority inversion can be bounded a priori. Several spinlockand semaphore-based multiprocessor real-time locking protocols for mutual exclusion (mutex), reader-writer (RW) exclusion, and k-exclusion are proposed and analyzed. A new category of RW locks suited to worst-case analysis, termed phase-fair locks, is proposed and three efficient phase-fair spinlock implementations are provided (one with few atomic operations, one with low space requirements, and one with constant RMR complexity). Maximum priority-inversion blocking is proposed as a natural complexity measure for semaphore protocols. It is shown that there are two classes of schedulability analysis, namely suspensionoblivious and suspension-aware analysis, that yield two different lower bounds on blocking.
Push-Net: Deep Planar Pushing for Objects with Unknown Physical Properties

This paper introduces Push-Net, a deep recurrent neural network model, which enables a robot to push objects of unknown physical properties for re-positioning and re-orientation, using only visual camera images as input. The unknown physical properties is a major challenge for pushing. Push-Net overcomes the challenge by tracking a history of push interactions with an LSTM module and training an auxiliary objective function that estimates an object’s center of mass. We trained Push-Net entirely in simulation and tested it extensively on many different objects in both simulation and on two real robots, a Fetch arm and a Kinova MICO arm. Experiments suggest that Push-Net is robust and efficient. It achieved over 97% success rate in simulation on average and succeeded in all real robot experiments with a small number of pushes.
On The Modeling and Analysis of Uplink and Downlink IEEE 802.11ax Wi-Fi With LTE in Unlicensed Spectrum

The growing demand on data to enhance the user experience has motivated research toward increasing the efficiency of spectrum utilization by extending cellular technology, such as LTE, toward unlicensed bands. In order to study the fair coexistence of LTE and Wi-Fi, we consider the IEEE 802.11ax Wi-Fi standard, where we use stochastic geometry to model and analyze the coexistence of LTE with simultaneous uplink and downlink IEEE 802.11ax transmissions. Mainly, we consider LTE with continuous transmissions (no protocol change), LTE with discontinuous transmissions (LTE-U), and licensed-assisted access (LAA) coexistence mechanisms. In addition, we focus on single unlicensed frequency band transmissions, where the locations of Wi-Fi access points, Wi-Fi stations (users), and LTE eNodeBs (eNBs) are modeled as three independent homogeneous Poisson point processes. Then, we derive analytical expressions for a set of performance metrics, which are the medium access probability, signal-to-interference-plus-noise ratio coverage probability, density of successful transmissions, and Shannon throughput probability for both uplink and downlink of IEEE 802.11ax in addition to LTE. Our analysis quantifies both single-user and multi-user operation modes of the IEEE802.11ax and show that LTE-U or LAA can be a good neighbor for IEEE 802.11ax for different traffic types.
FlashGraph: Processing Billion-Node Graphs on an Array of Commodity SSDs

Graph analysis performs many random reads and writes, thus, these workloads are typically performed in memory. Traditionally, analyzing large graphs requires a cluster of machines so the aggregate memory exceeds the graph size. We demonstrate that a multicore server can process graphs with billions of vertices and hundreds of billions of edges, utilizing commodity SSDs with minimal performance loss. We do so by implementing a graph-processing engine on top of a user-space SSD file system designed for high IOPS and extreme parallelism. Our semi-external memory graph engine called FlashGraph stores vertex state in memory and edge lists on SSDs. It hides latency by overlapping computation with I/O. To save I/O bandwidth, FlashGraph only accesses edge lists requested by applications from SSDs; to increase I/O throughput and reduce CPU overhead for I/O, it conservatively merges I/O requests. These designs maximize performance for applications with different I/O characteristics. FlashGraph exposes a general and flexible vertex-centric programming interface that can express a wide variety of graph algorithms and their optimizations. We demonstrate that FlashGraph in semi-external memory performs many algorithms with performance up to 80% of its in-memory implementation and significantly outperforms PowerGraph, a popular distributed in-memory graph engine.
Opsin genes, cone photopigments, color vision, and color blindness

In this chapter, we introduce the molecular structure of the genes encoding the human cone photopigments and their expression in photoreceptor cells. We also consider the consequences that alterations in those genes have on the spectral sensitivity of the photopigments, the cone photoreceptor mosaic, and the perceptual worlds of the color normal and color blind individuals who possess them. Throughout, we highlight areas in which our knowledge is still incomplete.
Author Attribution with CNN’s

In this report, the results from my CS224D final project are given and explained. The project was based on the application of relatively new neural network architectures, namely convolutional neural networks over word embeddings, to the task of authorship identification. The problem was posed as a classification task and models were evaluated over two datasets, a baseline of my own collection and a competition dataset with pre-existing, published results. Despite robust results on the baseline dataset, the CNN architecture failed to outmatch the best competition submission, although it did outscore most of the competitors’ submissions.
SoftRAN: software defined radio access network

An important piece of the cellular network infrastructure is the radio access network (RAN) that provides wide-area wireless connectivity to mobile devices. The fundamental problem the RAN solves is figuring out how best to use and manage limited spectrum to achieve this connectivity. In a dense wireless deployment with mobile nodes and limited spectrum, it becomes a difficult task to allocate radio resources, implement handovers, manage interference, balance load between cells, etc.
 We argue that LTE's current distributed control plane is suboptimal in achieving the above objective. We propose SoftRAN, a fundamental rethink of the radio access layer. SoftRAN is a software defined centralized control plane for radio access networks that abstracts all base stations in a local geographical area as a virtual big-base station comprised of a central controller and radio elements (individual physical base stations). In defining such an architecture, we create a framework through which a local geographical network can effectively perform load balancing and interference management, as well as maximize throughput, global utility, or any other objective.
Complexity in Phonological Treatment: Clinical Factors.

The construct of complexity has been advanced recently as a potentially contributing variable in the efficacy of treatment for children with functional phonological disorders. Thus far, complexity has been defined in terms of linguistic and psycholinguistic structure, articulatory phonetic variables, and conventional clinical factors. The focus of this paper is on clinical complexity as it influences the selection of target sounds for treatment, with three clinical factors reviewed: consistency of the error, normative age of acquisition, and number of errors to be treated. The collective findings suggest that treatment of seemingly more complex targets results in greater phonological gains. These results are integrated with converging evidence from other populations and language and learning domains.
Measuring phase synchrony in brain signals.

This article presents, for the first time, a practical method for the direct quantification of frequency-specific synchronization (i.e., transient phase-locking) between two neuroelectric signals. The motivation for its development is to be able to examine the role of neural synchronies as a putative mechanism for long-range neural integration during cognitive tasks. The method, called phase-locking statistics (PLS), measures the significance of the phase covariance between two signals with a reasonable time-resolution (<100 ms). Unlike the more traditional method of spectral coherence, PLS separates the phase and amplitude components and can be directly interpreted in the framework of neural integration. To validate synchrony values against background fluctuations, PLS uses surrogate data and thus makes no a priori assumptions on the nature of the experimental data. We also apply PLS to investigate intracortical recordings from an epileptic patient performing a visual discrimination task. We find large-scale synchronies in the gamma band (45 Hz), e.g., between hippocampus and frontal gyrus, and local synchronies, within a limbic region, a few cm apart. We argue that whereas long-scale effects do reflect cognitive processing, short-scale synchronies are likely to be due to volume conduction. We discuss ways to separate such conduction effects from true signal synchrony.
A Primer on Memory Consistency and Cache Coherence

Many modern computer systems and most multicore chips (chip multiprocessors) support shared memory in hardware. In a shared memory system, each of the processor cores may read and write to a single shared address space. For a shared memory machine, the memory consistency model defines the architecturally visible behavior of its memory system. Consistency definitions provide rules about loads and stores (or memory reads and writes) and how they act upon memory. As part of supporting a memory consistency model, many machines also provide cache coherence protocols that ensure that multiple cached copies of data are kept up-to-date. The goal of this primer is to provide readers with a basic understanding of consistency and coherence. This understanding includes both the issues that must be solved as well as a variety of solutions. We present both highlevel concepts as well as specific, concrete examples from real-world systems.
No Place to Hide that Bytes won't Reveal: Sniffing Location-Based Encrypted Traffic to Track User Position

News reports of the last few years indicated that several intelligence agencies are able to monitor large networks or entire portions of the Internet backbone. Such a powerful adversary has only recently been considered by the academic literature. In this paper, we propose a new adversary model for Location Based Services (LBSs). The model takes into account an unauthorized third party, different from the LBS provider itself, that wants to infer the location and monitor the movements of a LBS user. We show that such an adversary can extrapolate the position of a target user by just analyzing the size and the timing of the encrypted traffic exchanged between that user and the LBS provider. We performed a thorough analysis of a widely deployed location based app that comes pre-installed with many Android devices: GoogleNow. The results are encouraging and highlight the importance of devising more effective countermeasures against powerful adversaries to preserve the privacy of LBS users.