Miracle-dz/UniFAR-FTUs huggingface dataset:
Serpentinites from Central Cuba: petrology and HRTEM study

We have sampled serpentinites and the closely associated metabasites in the paleosubduction zone context of Central Cuba in order to characterise the microstructures of serpentine minerals as a function of metamorphic grade. The samples were collected in the eclogitic unit of the Escambray Massif and in the Zaza Zone, where eclogitic conditions were locally attained. Serpentinites are associated to the metabasites in lenses embedded in a metasediment matrix (Escambray) or form the matrix that embeds the metabasites (Zaza Zone). Field and petrological evidence suggests that serpentinites and associated metabasites underwent the same metamorphic history. Serpentinites from the Escambray Massif have preserved high-grade structures. In the Zaza Zone, most of the sampled serpentinites only underwent low-grade metamorphism and the sample that underwent eclogitic conditions is strongly retrogressed. Low-grade serpentinites show pseudomorphic textures in thin section, a mixture of chrysotile, poorly crystallised serpentine and minor lizardite. High-grade samples, characterised by non-pseudomorphic textures, mainly consist of antigorite, associated to minor chrysotile. Transmission electron microscopy shows that antigorite from preserved samples (Escambray Massif) displays few microstructural defects such as stacking faults or modulation dislocations, in contrast to the higher density of defects encountered in highly retrogressed serpentinites (Zaza Zone). We thus propose that the record of high metamorphic grade in matrix antigorite is best characterised by the elimination of structural defects.
EmbLexChange at SemEval-2020 Task 1: Unsupervised Embedding-based Detection of Lexical Semantic Changes

This paper describes EmbLexChange, a system introduced by the “Life-Language” team for SemEval-2020 Task 1, on unsupervised detection of lexical-semantic changes. EmbLexChange is defined as the divergence between the embedding based profiles of word w (calculated with respect to a set of reference words) in the source and the target domains (source and target domains can be simply two time frames t_1 and t_2). The underlying assumption is that the lexical-semantic change of word w would affect its co-occurring words and subsequently alters the neighborhoods in the embedding spaces. We show that using a resampling framework for the selection of reference words (with conserved senses), we can more reliably detect lexical-semantic changes in English, German, Swedish, and Latin. EmbLexChange achieved second place in the binary detection of semantic changes in the SemEval-2020.
Characterizing GAN Convergence Through Proximal Duality Gap

Despite the accomplishments of Generative Ad-versarial Networks (GANs) in modeling data distributions, training them remains a challenging task. A contributing factor to this difﬁculty is the non-intuitive nature of the GAN loss curves, which necessitates a subjective evaluation of the generated output to infer training progress. Recently, motivated by game theory, duality gap has been proposed as a domain agnostic measure to monitor GAN training. However, it is restricted to the setting when the GAN converges to a Nash equilibrium. But GANs need not always converge to a Nash equilibrium to model the data distribution. In this work, we extend the notion of duality gap to proximal duality gap that is applicable to the general context of training GANs where Nash equilibria may not exist. We show theoretically that the proximal duality gap is capable of monitoring the convergence of GANs to a wider spectrum of equilibria that subsumes Nash equilibria. We also theoretically establish the relationship between the proximal duality gap and the divergence between the real and generated data distributions for different GAN formulations. Our results provide new insights into the nature of GAN convergence. Finally, we validate experimentally the usefulness of proximal duality gap for monitoring and inﬂuencing GAN training.
On the Effectiveness of Fine-tuning Versus Meta-reinforcement Learning

Intelligent agents should have the ability to leverage knowledge from previously learned tasks in order to learn new ones quickly and efficiently. Meta-learning approaches have emerged as a popular solution to achieve this. However, meta-reinforcement learning (meta-RL) algorithms have thus far been restricted to simple environments with narrow task distributions. Moreover, the paradigm of pretraining followed by fine-tuning to adapt to new tasks has emerged as a simple yet effective solution in supervised and self-supervised learning. This calls into question the benefits of meta-learning approaches also in reinforcement learning, which typically come at the cost of high complexity. We hence investigate meta-RL approaches in a variety of vision-based benchmarks, including Procgen, RLBench, and Atari, where evaluations are made on completely novel tasks. Our findings show that when meta-learning approaches are evaluated on different tasks (rather than different variations of the same task), multi-task pretraining with fine-tuning on new tasks performs equally as well, or better, than meta-pretraining with meta test-time adaptation. This is encouraging for future research, as multi-task pretraining tends to be simpler and computationally cheaper than meta-RL. From these findings, we advocate for evaluating future meta-RL methods on more challenging tasks and including multi-task pretraining with fine-tuning as a simple, yet strong baseline.
Quality control and preprocessing of metagenomic datasets

Summary: Here, we present PRINSEQ for easy and rapid quality control and data preprocessing of genomic and metagenomic datasets. Summary statistics of FASTA (and QUAL) or FASTQ files are generated in tabular and graphical form and sequences can be filtered, reformatted and trimmed by a variety of options to improve downstream analysis. Availability and Implementation: This open-source application was implemented in Perl and can be used as a stand alone version or accessed online through a user-friendly web interface. The source code, user help and additional information are available at http://prinseq.sourceforge. net/. Contact: rschmied@sciences.sdsu. edu; redwards@cs.sdsu. edu
Emotion from Motion

This paper introduces a model to generate \emo-tional" animation from \neutral" human motion. Using techniques from signal processing, our method calculates certain emotional transforms which are then applied to existing motions of articulated gures in order to produce the same motions, but with an emotional quality such as angry or sad. These transforms capture the dierence between a neutral and emotional movement with respect to two components: speed (timing), and spatial amplitude (range) of a movement. Since the transforms are applied as global operations , they provide a convenient and ecient way to adapt motion-captured, simulated or keyframed animation of articulated gures to dierent situations and characters.
Medieval upland cultivation on the Berwyns in north Wales

ABSTRACT While historians have increasingly appreciated the role of agriculture in the medieval Welsh economy, even in some upland areas, the physical remains have been little studied. This paper identifies three areas of relict medieval cultivation, together with associated habitation sites, at high altitude in the Berwyn mountains of North Wales. Their characteristics are considered and it is argued that they reflect the discrete expansion of existing settlements in the valleys on to more marginal hill areas prior to the climatic changes and natural disasters of the fourteenth century. There are hints of similar patterns of medieval upland exploitation elsewhere in Wales, and this suggests that models illustrating the progressive expansion of land intakes in a series of stages from valley floor to hill top during the medieval and post-medieval centuries may need to be revised.
Exploiting Intrinsic Stochasticity of Real-Time Simulation to Facilitate Robust Reinforcement Learning for Robot Manipulation

—Simulation is essential to reinforcement learning (RL) before implementation in the real world, especially for safety-critical applications like robot manipulation. Convention-ally, RL agents are sensitive to the discrepancies between the simulation and the real world, known as the sim-to-real gap. The application of domain randomization, a technique used to ﬁll this gap, is limited to the imposition of heuristic-randomized models. We investigate the properties of intrinsic stochasticity of real-time simulation (RT-IS) of off-the-shelf simulation software and its potential to improve the robustness of RL methods and the performance of domain randomization. Firstly, we conduct analytical studies to measure the correlation of RT-IS with the occupation of the computer hardware and validate its comparability with the natural stochasticity of a physical robot. Then, we apply the RT-IS feature in the training of an RL agent. The simulation and physical experiment results verify the feasibility and applicability of RT-IS to robust RL agent design for robot manipulation tasks. The RT-IS-powered robust RL agent outperforms conventional RL agents on robots with modeling uncertainties. It requires fewer heuristic randomization and achieves better generalizability than the conventional domain-randomization-powered agents. Our ﬁndings provide a new perspective on the sim-to-real problem in practical applications like robot manipulation tasks.
Carbon and Nitrogen Mineralization of a Semiarid Shrubland Exposed to Experimental Nitrogen Deposition

Soil Sci. Soc. Am. J. 76:2068–2073 doi:10.2136/sssaj2012.0101n Received 23 Mar. 2012. *Corresponding author (georgev@csusm.edu). © Soil Science Society of America, 5585 Guilford Rd., Madison WI 53711 USA All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permission for printing and for reprinting the material contained herein has been obtained by the publisher. Carbon and Nitrogen Mineralization of a Semiarid Shrubland Exposed to Experimental Nitrogen Deposition Soil Biology & Biochemistry Note
Conjecturing-Based Computational Discovery of Patterns in Data

Modern machine learning methods are designed to exploit complex patterns in data regardless of their form, while not necessarily revealing them to the investigator. Here we demonstrate situations where modern machine learning methods are ill-equipped to reveal feature interaction effects and other nonlinear relationships. We propose the use of a conjecturing machine that generates feature relationships in the form of bounds for numerical features and boolean expressions for nominal features that are ignored by machine learning algorithms. The proposed framework is demonstrated for a classification problem with an interaction effect and a nonlinear regression problem. In both settings, true underlying relationships are revealed and generalization performance improves. The framework is then applied to patient-level data regarding COVID-19 outcomes to suggest possible risk factors.
Maintaining Plasticity via Regenerative Regularization

In continual learning, plasticity refers to the ability of an agent to quickly adapt to new information. Neural networks are known to lose plasticity when processing non-stationary data streams. In this paper, we propose L 2 Init , a very simple approach for maintaining plasticity by incorporating in the loss function L2 regularization toward initial parameters. This is very similar to standard L2 regularization (L2), the only difference being that L2 regularizes toward the origin. L2 Init is simple to implement and requires selecting only a single hyper-parameter. The motivation for this method is the same as that of methods that reset neurons or parameter values. Intuitively, when recent losses are insensitive to particular parameters, these parameters drift toward their initial values. This prepares parameters to adapt quickly to new tasks. On simple problems representative of different types of nonstationarity in continual learning, we demonstrate that L2 Init consistently mitigates plasticity loss. We additionally find that our regularization term reduces parameter magnitudes and maintains a high effective feature rank.
FLAC: Practical Failure-Aware Atomic Commit Protocol for Distributed Transactions (Extended Version)

In distributed transaction processing, atomic commit protocol (ACP) is used to ensure database consistency. With the use of commodity compute nodes and networks, failures such as system crashes and network partitioning are common. It is therefore important for ACP to dynamically adapt to the operating condition for efficiency while ensuring the consistency of the database. Existing ACPs often assume stable operating conditions, hence, they are either non-generalizable to different environments or slow in practice. In this paper, we propose a novel and practical ACP, called Failure-Aware Atomic Commit ( FLAC ). In essence, FLAC includes three sub-protocols, which are specifically designed for three different environments: (i) no failure occurs, (ii) participant nodes might crash but there is no delayed connection, or (iii) both crashed nodes and delayed connection can occur. It models these environments as the failure-free, crash-failure, and network-failure robustness levels. During its operation, FLAC can monitor if any failure occurs and dynamically switch to operate the most suitable sub-protocol, using a robustness level state machine, whose parameters are fine-tuned by reinforcement learning. Consequently, it improves both the response time and throughput, and effectively handles nodes distributed across the Internet where crash and network failures might occur. We implement FLAC in a distributed transactional key-value storage system based on Google Percolator and evaluate its performance with both a micro benchmark and a macro benchmark of real workload. The results show that FLAC achieves up to 2.22x throughput improvement and 2.82x latency speedup, compared to existing ACPs for high-contention workloads.
Converting the Point of View of Message Spoken to Virtual Assistants

Virtual Assistants can be quite literal at times. If the user says “tell Bob I love him,” most virtual assistants will extract the message “I love him” and send it to the user’s contact named Bob, rather than properly converting the message to “I love you. ” We designed a system to allow virtual assistants to take a voice message from one user, convert the point of view of the message, and then deliver the result to its target user. We developed a rule-based model, which integrates a linear text classification model, part-of-speech tagging, and constituency parsing with rule-based transformation methods. We also investigated Neural Machine Translation (NMT) approaches, including LSTMs, CopyNet, and T5. We explored 5 metrics to gauge both naturalness and faithfulness automatically, and we chose to use BLEU plus METEOR for faithfulness and relative perplexity using a separately trained language model (GPT) for naturalness. Transformer-Copynet and T5 performed similarly on faithfulness metrics, with T5 achieving slight edge, a BLEU score of 63.8 and a METEOR score of 83.0. CopyNet was the most natural, with a relative perplexity of 1.59. CopyNet also has 37 times fewer parameters than T5. We have publicly released our dataset, which is composed of 46,565 crowd-sourced samples.
LH*RS---a highly-available scalable distributed data structure

LH*RS is a high-availability scalable distributed data structure (SDDS). An LH*RS file is hash partitioned over the distributed RAM of a multicomputer, for example, a network of PCs, and supports the unavailability of any k ≥ 1 of its server nodes. The value of k transparently grows with the file to offset the reliability decline. Only the number of the storage nodes potentially limits the file growth. The high-availability management uses a novel parity calculus that we have developed, based on Reed-Salomon erasure correcting coding. The resulting parity storage overhead is about the lowest possible. The parity encoding and decoding are faster than for any other candidate coding we are aware of. We present our scheme and its performance analysis, including experiments with a prototype implementation on Wintel PCs. The capabilities of LH*RS offer new perspectives to data intensive applications, including the emerging ones of grids and of P2P computing.
No Silver Bullet - Essence and Accidents of Software Engineering

But, as we look to the horizon of a decade hence, we see no silver bullet. There is no single development, in either technology or in management technique, that by itself promises even one order-of-magnitude improvement in productivity, in reliability, in simplicity. In this article, I shall try to show why, by examining both the nature of the software problem and the properties of the bullets proposed.
Cooperative Maneuvering in Close Environments Among Cybercars and Dual-Mode Cars

This paper describes the results of vehicle-to-vehicle (V2V) and infrastructure-to-vehicle (I2V) experiments implementing cooperative maneuvering for three different vehicles driving automatically. The cars used were cybercars from the Institut National de Recherche en Informatique et Automatique (INRIA), (France), which are fully automated road vehicles, and two mass-produced cars-one a Smart Fortwo car from TNO (Netherlands) equipped with additional actuators and sensors and the other a convertible Citroën C3 from IAI (Spain) that uses sensorial information to manage the actuators. The cars communicate by a wireless mesh network over Wi-Fi using the optimized link state routing (OLSR) ad-hoc protocol. The entire communication task is embedded in a small MIPS Linux Box (4G System Cube) that is transparent for the cars. A standard framework was defined with the parameters needed to perform adaptive cruise control (ACC) and intersection maneuvers among the cars, as well as emergency stops via a signal sent by the infrastructure. The experiments were carried out in La Rochelle (France) during the final demonstration of the European Union (EU) Cybercars-2 Project.
Halfway to Nowhere: Liberian Former Child Soldiers in a Ghanaian Refugee Camp

This study utilizes Kunz's kinetic model of refugee displacement to interpret the placelessness experienced by Liberian former child soldiers in the Buduburam refugee camp in Ghana. From August to December 2007, a clinical psychologist and a geographer interviewed ten Liberian former child soldiers to determine spatial and social barriers to successful resettlement and the prospects for overcoming these obstacles. Based on the interviews, five areas of intervention were suggested: (1) geographic desegregation and relocation, (2) education and employment, (3) psychological counseling, (4) societal acceptance and reintegration, and (5) security and protection.
SMART: Self-Morphing Anytime Replanning Tree

—The paper presents an algorithm, called Self-Morphing Anytime Replanning Tree (SMART), that facilitates anytime replanning in dynamic environments. SMART performs risk-based tree-pruning if its current path is obstructed by nearby moving obstacle(s), resulting in multiple disjoint subtrees. Then, for speedy recovery, it exploits these subtrees and performs informed tree-repair at hot-spots that lie at the intersection of subtrees to ﬁnd a new path. The performance of SMART is comparatively evaluated with seven existing algorithms through extensive simulations. Two scenarios are considered with: 1) dynamic obstacles and 2) both static and dynamic obstacles. The results show that SMART yields signiﬁcant improvements in replanning time, success rate and travel time. Finally, the performance of SMART is validated by a real laboratory experiment.
A GPU Algorithm for Earliest Arrival Time Problem in Public Transport Networks

Given a temporal graph $G$, a source vertex $s$, and a departure time at source vertex $t_{s}$, the earliest arrival time problem (EAT) is to start from $s$ on or after $t_{s}$ and reach all the vertices in $G$ as early as possible. Ni et al. have proposed a parallel algorithm for EAT and obtained a speedup up to 9.5× on real-world graphs with respect to the connection-scan serial algorithm by using multi-core processors. We propose a topology-driven parallel algorithm for EAT on public transport networks and implement using general-purpose programming on the graphics processing unit (GPU). A temporal connection in a temporal graph for a public transport network is associated with a departure time and a duration time, and many connections exist from $u$ to $v$ for an edge ($u, v$). We propose two pruning techniques connection-type and clustering, and use arithmetic progression technique appropriately to process many connections of an edge, without scanning all of them. In the connection-type technique, the connections of an edge with the same duration are grouped together. In the clustering technique, we follow 24-hour format and the connections of an edge are partitioned into 24 clusters so that the departure time of connections in the $i^{th}$ cluster is at least $i$-hour and at most $i+1$-hour. The arithmetic progression technique helps to store a sequence of departure times of various connections in a compact way. We propose a hybrid approach to combine the three techniques (connection-type, clustering and arithmetic progression) in an efficient way. Our techniques achieve an average speedup up to 61× when compared to the existing connection-scan serial algorithm running on CPU. Also, the average speedup of our algorithm is 12.65× against the parallel edge-scan-dependency graph algorithm running on GPU.
Advanced Data Augmentation Approaches: A Comprehensive Survey and Future directions

—Deep learning (DL) algorithms have shown significant performance in various computer vision tasks. However, having limited labelled data lead to a network overﬁtting problem, where network performance is bad on unseen data as compared to training data. Consequently, it limits performance improvement. To cope with this problem, various techniques have been proposed such as dropout, normalization and advanced data augmentation. Among these, data augmentation, which aims to enlarge the dataset size by including sample diversity, has been a hot topic in recent times. In this article, we focus on advanced data augmentation techniques. we provide a background of data augmentation, a novel and comprehensive taxonomy of reviewed data augmentation techniques, and the strengths and weaknesses (wherever possible) of each technique. We also provide comprehensive results of the data augmentation effect on three popular computer vision tasks, such as image classiﬁcation, object detection and semantic segmentation. For results reproducibility, we compiled available codes of all data augmentation
Improvement of dual rail logic as a countermeasure against DPA

Dual rail logic is considered as a relevant hardware countermeasure against Differential Power Analysis (DPA) by making power consumption data independent. In this paper, we deduce from a thorough analysis of the robustness of dual rail logic against DPA the design range in which it can be considered as effectively robust. Surprisingly this secure design range is quite narrow. We therefore propose the use of an improved logic, called Secure Triple Track Logic, as an alternative to more conventional dual rail logics. To validate the claimed benefits of the logic introduced herein, we have implemented a sensitive block of the Data Encryption Standard algorithm (DES) and carried out by simulation DPA attacks.