Miracle-dz/UniFAR-FTUs huggingface dataset:
CuckooPIM: an efficient and less-blocking coherence mechanism for processing-in-memory systems

The ever-growing processing ability of in-memory processing logic makes the data sharing and coherence between processors and in-memory logic play an increasingly important role in Processing-in-Memory (PIM) systems. Unfortunately, the existing state-of-the-art coarse-grained PIM coherence solutions suffer from unnecessary data movements and stalls caused by a data ping-pong issue. This work proposes CuckooPIM, a criticality-aware and less-blocking coherence mechanism, which can effectively avoid unnecessary data movements and stalls. Experiments reveal that CuckooPIM achieves 1.68x speedup on average comparing with coarse-grained PIM coherence.
Towards cognitive obfuscation: impeding hardware reverse engineering based on psychological insights

In contrast to software reverse engineering, there are hardly any tools available that support hardware reversing. Therefore, the reversing process is conducted by human analysts combining several complex semi-automated steps. However, countermeasures against reversing are evaluated solely against mathematical models. Our research goal is the establishment of cognitive obfuscation based on the exploration of underlying psychological processes. We aim to identify problems which are hard to solve for human analysts and derive novel quantification metrics, thus enabling stronger obfuscation techniques.
EasyDist: An End-to-End Distributed Deep Learning Tool for Cloud

With the increasing number of artificial intelligence (AI) applications, complex neural network based modeling on large scale data is becoming quite common. Using distributed deep learning (DDL), a data scientist can harness the computational power of multiple graphics processing units (GPUs) spread across different machines and significantly reduce neural network training times. In this paper, we focus on a TensorFlow based distributed training approach, which, unfortunately, leaves the auxiliary modeling capabilities (e.g., resource deployment, data distribution, run-time control etc.) to user-discretion. Additionally, these training capabilities cannot be accessed directly through popular high-level interfaces such as Keras. As a result, the transition to DDL from a single node training setting involves a steep learning curve for data scientists. To bridge this gap, we present EasyDist, an end-to-end DDL tool that preserves the single-node programming model by leveraging distributed TensorFlow between a Keras interface and public Cloud infrastructure. EasyDist is model agnostic, i.e., any neural network model written in Keras can use EasyDist for distributed training. Internally, EasyDist incorporates resource deployment, data allocation, training orchestration and result aggregation within a clean abstraction which allows the data scientist to focus on model definition. Evaluation of EasyDist on publicly available benchmark datasets and models shows that the model accuracy is not compromised in any significant way while the training times can be reduced upto ~6-8x compared to single machine settings.
Methodological Control Framework and Scheduling Algorithm for Internet

Proper control model of the Internet is crucial in network control and management. In this paper, we presented a methodological control framework to describe general process logic and dataflow of network control, which could obtain consistent and global network view on elaborate resource data and network state. As for its scheduling problem, we found that it shares features with virtual network embedding problem if control request appears as a graph of atomic network control action. Inspired by this, we designed an algorithm for the scheduling problem of control request in the Internet based on isomorphic subgraph detection. Furthermore, we also discussed control action migration under measuring revenue and cost. Finally, we evaluated the performance of our proposed algorithm with different parameters by simulation experiments.
The FASTRAC Satellites: Software Implementation and Testing

The Formation Autonomy Spacecraft with Thrust, Relnav, Attitude, and Crosslink (FASTRAC) project from the University of Texas at Austin has developed two nanosatellites as the winner of the University Nanosat-3 Competition. Both satellites have been manifested for a launch provided by the Space Test Program (STP) in December of 2009. The FASTRAC satellites will demonstrate the following enabling technologies for nanosatellites: (1) on-orbit real-time GPS relative navigation via real-time crosslink data exchange; (2) on-orbit real-time attitude determination using a single frequency, C/A-code, reprogrammable GPS receiver; (3) a micro-discharge plasma thruster; and (4) a distributed ground station network. In this paper, the design and testing of the FASTRAC command and data handling system (C&DH) is described. The C&DH system is divided into four subsystems, each controlled by one Atmel Atmega128 microcontroller: communications, electrical power, GPS, and thruster or IMU (depending on the satellite). The major functionality of the C&DH software is presented: automatic crosslink, dual uplink frequency support, user command capabilities, automatic beacon updates, automatic storage and retrieval of experimental data, and support for all mission phases. By using commercially available off the shelf components and leveraging freely availably software, it was possible to build and deliver two low-cost, fully functional satellites. The major hardware and software testing and debugging tools, including the Flatsat electronic test-bed and the FASTRAC GUI Debugging Program, are discussed. Finally, the challenges encountered during the design process and the lessons learned through the numerous design iterations are presented.
Output DC Voltage Elimination in PWM Converters for Railway Applications

Auxiliary power supply for modern railway vehicles, powered from the DC catenary line, today is typically connected to the line through an inverter, and an output three-phase transformer. Generally, inverter includes DC component in its output voltage because of difference in switching behaviour of power devices which are used in the power converters. DC component leads to the magnetic field saturation of the transformers, which causes energy loss, and acoustic noise. Therefore, it is necessary to prevent the appearance of DC component on the inverter output. This paper proposes the basic methods used in practice to reduce adverse effects of DC component on the operation of three-phase transformer. Keywords—Converter Control, DC/AC converters, Three-phase system, Railway Application.
Nonconvex Rectangular Matrix Completion via Gradient Descent without $\ell_{2,\infty}$ Regularization

The analysis of nonconvex matrix completion has recently attracted much attention in the community of machine learning thanks to its computational convenience. Existing analysis on this problem, however, usually relies on $\ell_{2,\infty}$ projection or regularization that involves unknown model parameters, although they are observed to be unnecessary in numerical simulations, see, e.g., Zheng and Lafferty [2016]. In this paper, we extend the analysis of the vanilla gradient descent for positive semidefinite matrix completion proposed in Ma et al. [2017] to the rectangular case, and more significantly, improve the required sampling rate from $O(\operatorname{poly}(\kappa)\mu^3 r^3 \log^3 n/n )$ to $O(\mu^2 r^2 \kappa^{14} \log n/n )$. Our technical ideas and contributions are potentially useful in improving the leave-one-out analysis in other related problems.
Game-Theoretic Randomness for Blockchain Games

In this paper, we consider the problem of generating fair randomness in a deterministic, multi-agent context (for instance, a decentralised game built on a blockchain). The existing state-of-the-art approaches are either susceptible to manipulation if the stakes are high enough, or they are not generally applicable (specifically for massive game worlds as opposed to games between a small set of players). We propose a novel method based on game theory: By allowing agents to bet on the outcomes of random events against the miners (who are ultimately responsible for the randomness), we are able to align the incentives so that the distribution of random events is skewed only slightly even if miners are trying to maximise their profit and engage in block withholding to cheat in games.
The Design and Deployment of an End-to-end IoT Infrastructure for the Natural Environment

Internet of Things (IoT) systems have seen recent growth in popularity for city and home environments. We report on the design, deployment, and use of the IoT infrastructure for environmental monitoring and management. Working closely with hydrologists, soil scientists, and animal behaviour scientists, we successfully deployed and utilised a system to deliver integrated information across these two fields in the first such example of real-time multidimensional environmental science. We describe the design of this system; its requirements and operational effectiveness for hydrological, soil, and ethological scientists; and our experiences from building, maintaining, and using the deployment at a remote site in difficult conditions. Based on this experience, we discuss key future work for the IoT community when working in these kinds of environmental deployments.
Personalized Visited-POI Assignment to Individual Raw GPS Trajectories

Knowledge discovery from GPS trajectory data is an essential topic in several scientific areas, including data mining, human behavior analysis, and user modeling. This article proposes a task that assigns personalized visited points of interest (POIs). Its goal is to assign every fine-grain location (i.e., POIs) that a user actually visited, which we call visited-POI, to the corresponding span of his or her (personal) GPS trajectories. We also introduce a novel algorithm to solve this assignment task. First, we exhaustively extract stay-points as span candidates of visits using a variant of a conventional stay-point extraction method and then extract POIs that are located close to the extracted stay-points as visited-POI candidates. Then, we simultaneously predict which stay-points and POIs can be actual user visits by considering various aspects, which we formulate as integer linear programming. Experimental results conducted on a real user dataset show that our method achieves higher accuracy in the visited-POI assignment task than the various cascaded procedures of conventional methods.
LINEARIZED ADMM AND FAST NONLOCAL DENOISING FOR EFFICIENT PLUG-AND-PLAY RESTORATION

In plug-and-play image restoration, the regularization is performed using powerful denoisers such as nonlocal means (NLM) or BM3D. This is done within the framework of alternating direction method of multipliers (ADMM), where the regularization step is formally replaced by an off-the-shelf denoiser. Each plug-and-play iteration involves the inversion of the forward model followed by a denoising step. In this paper, we present a couple of ideas for improving the efficiency of the inversion and denoising steps. First, we propose to use linearized ADMM, which generally allows us to perform the inversion at a lower cost than standard ADMM. Moreover, we can easily incorporate hard constraints into the optimization framework as a result. Second, we develop a fast algorithm for doubly stochastic NLM, originally proposed by Sreehari et al. (IEEE TCI, 2016), which is about 80× faster than brute-force computation. This particular denoiser can be expressed as the proximal map of a convex regularizer and, as a consequence, we can guarantee convergence for linearized plug-and-play ADMM. We demonstrate the effectiveness of our proposals for super-resolution and single-photon imaging.
Neural-network simulations of memory consolidation and reconsolidation

In the mammalian brain newly acquired memories depend on the hippocampus for maintenance and recall, but over time these functions are taken over by the neocortex through a process called systems consolidation. However, reactivation of a consolidated memory can induce a brief period of temporary hippocampus-dependence, followed by return to hippocampus-independence. Here we present a computational model that uses simulation of recently described mechanisms of synaptic plasticity to account for findings from the systems consolidation/reconsolidation literature and to make predictions for future research.
Characterizing the Use of Images by State-Sponsored Troll Accounts on Twitter

Anecdotal evidence has emerged suggesting that state-sponsored organizations, like the Russian Internet Research Agency, have exploited mainstream social. Their primary goal is apparently to conduct information warfare operations to manipulate public opinion using accounts disguised as "normal" people. To increase engagement and credibility of their posts, these accounts regularly share images. However, the use of images by state-sponsored accounts has yet to be examined by the research community. In this work, we address this gap by analyzing a ground truth dataset of 1.8M images posted to Twitter by called Russian trolls. More specifically, we analyze the content of the images, as well as the posting activity of the accounts. Among other things, we find that image posting activity of Russian trolls is tightly coupled with real-world events, and that their targets, as well as the content shared, changed over time. When looking at the interplay between domains that shared the same images as state-sponsored trolls, we find clear cut differences in the origin and/or spread of images across the Web. Overall, our findings provide new insight into how state-sponsored trolls operate, and specifically how they use imagery to achieve their goals.
Accelerated Experimental Design for Pairwise Comparisons

Pairwise comparison labels are more informative and less variable than class labels, but generating them poses a challenge: their number grows quadratically in the dataset size. We study a natural experimental design objective, namely, D-optimality, that can be used to identify which $K$ pairwise comparisons to generate. This objective is known to perform well in practice, and is submodular, making the selection approximable via the greedy algorithm. A na\"ive greedy implementation has $O(N^2d^2K)$ complexity, where $N$ is the dataset size, $d$ is the feature space dimension, and $K$ is the number of generated comparisons. We show that, by exploiting the inherent geometry of the dataset--namely, that it consists of pairwise comparisons--the greedy algorithm's complexity can be reduced to $O(N^2(K+d)+N(dK+d^2) +d^2K). $ We apply the same acceleration also to the so-called lazy greedy algorithm. When combined, the above improvements lead to an execution time of less than 1 hour for a dataset with $10^8$ comparisons; the na\"ive greedy algorithm on the same dataset would require more than 10 days to terminate.
BEARS: An Extensible Java Bug Benchmark for Automatic Program Repair Studies

Benchmarks of bugs are essential to empirically evaluate automatic program repair tools. In this paper, we present BEARS, a project for collecting and storing bugs into an extensible bug benchmark for automatic repair studies in Java. The collection of bugs relies on commit building state from Continuous Integration (CI) to find potential pairs of buggy and patched program versions from open-source projects hosted on GitHub. Each pair of program versions passes through a pipeline where an attempt of reproducing a bug and its patch is performed. The core step of the reproduction pipeline is the execution of the test suite of the program on both program versions. If a test failure is found in the buggy program version candidate and no test failure is found in its patched program version candidate, a bug and its patch were successfully reproduced. The uniqueness of Bears is the usage of CI (builds) to identify buggy and patched program version candidates, which has been widely adopted in the last years in open-source projects. This approach allows us to collect bugs from a diversity of projects beyond mature projects that use bug tracking systems. Moreover, BEARS was designed to be publicly available and to be easily extensible by the research community through automatic creation of branches with bugs in a given GitHub repository, which can be used for pull requests in the BEARS repository. We present in this paper the approach employed by BEARS, and we deliver the version 1.0 of BEARS, which contains 251 reproducible bugs collected from 72 projects that use the Travis CI and Maven build environment.
Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection

Pavement crack detection is a critical task for insuring road safety. Manual crack detection is extremely time-consuming. Therefore, an automatic road crack detection method is required to boost this progress. However, it remains a challenging task due to the intensity inhomogeneity of cracks and complexity of the background, e.g., the low contrast with surrounding pavements and possible shadows with a similar intensity. Inspired by recent advances of deep learning in computer vision, we propose a novel network architecture, named feature pyramid and hierarchical boosting network (FPHBN), for pavement crack detection. The proposed network integrates context information to low-level features for crack detection in a feature pyramid way, and it balances the contributions of both easy and hard samples to loss by nested sample reweighting in a hierarchical way during training. In addition, we propose a novel measurement for crack detection named average intersection over union (AIU). To demonstrate the superiority and generalizability of the proposed method, we evaluate it on five crack datasets and compare it with the state-of-the-art crack detection, edge detection, and semantic segmentation methods. The extensive experiments show that the proposed method outperforms these methods in terms of accuracy and generalizability. Code and data can be found in https://github.com/fyangneil/pavement-crack-detection.
Entanglement-Assisted Quantum Data Compression

Ask how the quantum compression of ensembles of pure states is affected by the availability of entanglement, and in settings where the encoder has access to side information. We find the optimal asymptotic quantum rate and the optimal tradeoff (rate region) of quantum and entanglement rates. It turns out that the amount by which the quantum rate beats the Schumacher limit, the entropy of the source, is precisely half the entropy of classical information that can be extracted from the source and side information states without disturbing them at all ("reversible extraction of classical information"). In the special case that the encoder has no side information, or that she has access to the identity of the states, this problem reduces to the known settings of blind and visible Schumacher compression, respectively, albeit here additionally with entanglement assistance. We comment on connections to previously studied and further rate tradeoffs when also classical information is considered.
TuckerMPI

Our goal is compression of massive-scale grid-structured data, such as the multi-terabyte output of a high-fidelity computational simulation. For such data sets, we have developed a new software package called TuckerMPI, a parallel C++/MPI software package for compressing distributed data. The approach is based on treating the data as a tensor, i.e., a multidimensional array, and computing its truncated Tucker decomposition, a higher-order analogue to the truncated singular value decomposition of a matrix. The result is a low-rank approximation of the original tensor-structured data. Compression efficiency is achieved by detecting latent global structure within the data, which we contrast to most compression methods that are focused on local structure. In this work, we describe TuckerMPI, our implementation of the truncated Tucker decomposition, including details of the data distribution and in-memory layouts, the parallel and serial implementations of the key kernels, and analysis of the storage, communication, and computational costs. We test the software on 4.5 and 6.7 terabyte data sets distributed across 100 s of nodes (1,000 s of MPI processes), achieving compression ratios between 100 and 200,000×, which equates to 99--99.999% compression (depending on the desired accuracy) in substantially less time than it would take to even read the same dataset from a parallel file system. Moreover, we show that our method also allows for reconstruction of partial or down-sampled data on a single node, without a parallel computer so long as the reconstructed portion is small enough to fit on a single machine, e.g., in the instance of reconstructing/visualizing a single down-sampled time step or computing summary statistics. The code is available at https://gitlab.com/tensors/TuckerMPI.
Les objets paléomonétaires de l'Europe atlantique protohistorique

Summary. —The first examples of pre-money are the Columbella shells of the Mediterranean. Some Chalcolithic exports included copper spear heads from Palmela and crockery beads from Egypt. There was also « ring money » consisting of copper rings covered in silver and gold. Pieces of bronze were also broken for exchange. Armorican lead socketed axes are the first complete system of paleo-money. In England another form of pre- money were the iron currency bars of the Early Iron Age.
Toward a Deaf Translation Norm

As access to deaf people grows around the world, a new profession has begun to emerge as well, that of Deaf translators and interpreters (T/Is). In his new study Toward a Deaf Translation Norm, Christopher Stone explores this innovation, including its antecedents and how it is manifested in public places. Most importantly, Stone investigates whether or not a translation norm has evolved for Deaf T/Is as increasing numbers of them work in the mainstream translating for websites, public services, government literature, and television media. For his study, the sixth volume in the Studies in Interpretation series, Stone concentrated his research in the United Kingdom. Specifically, he examined the rendering of English broadcast television news into British Sign Language (BSL) by both Deaf and hearing T/Is. Segments of the data feature simultaneous Deaf and hearing in-vision T/I broadcasts. Recording these broadcasts produced a controlled product that enabled direct comparison of the Deaf and hearing T/Is. Close analysis of these examples revealed to Stone that Deaf T/s not only employ a Deaf translation norm, they take labors to shape their BSL text into a stand-alone product rather than a translation. Ultimately, Toward a Deaf Translation Norm opens up engrossing new vistas on current deliberation about neutrality in translation and interpretation.
Starch Trek: The Search for Yield

Starch is a plant storage polyglucan that accumulates in plastids. It is composed of two polymers, amylose and amylopectin, with different structures and plays several roles in helping to determine plant yield. In leaves, it acts as a buffer for night time carbon starvation. Genetically altered plants that cannot synthesize or degrade starch efficiently often grow poorly. There have been a number of successful approaches to manipulate leaf starch metabolism that has resulted in increased growth and yield. Its degradation is also a source of sugars that can help alleviate abiotic stress. In edible parts of plants, starch often makes up the majority of the dry weight constituting much of the calorific value of food and feed. Increasing starch in these organs can increase this as well as increasing yield. Enzymes involved in starch metabolism are well known, and there has been much research analyzing their functions in starch synthesis and degradation, as well as genetic and posttranslational regulatory mechanisms affecting them. In this mini review, we examine work on this topic and discuss future directions that could be used to manipulate this metabolite for improved yield.