Miracle-dz/UniFAR-FTUs huggingface dataset:
SYSTRAN Chinese-English and English-Chinese Hybrid Machine Translation Systems

This report describes both of SYSTRAN's Chinese-English and English-Chinese machine translation systems that participated in the CWMT2009 machine translation evaluation tasks. The base systems are SYSTRAN rule-based machine translation systems, augmented with various statistical techniques. Based on the translations of the rule-based systems, we perform statistical post-editing with the provided bilingual and monolingual training corpora. In this report, we describe the technology behind the systems, the training data, and finally the evaluation results in the CWMT2009 evaluation. Our primary systems were top-ranked in the evaluation tasks.
The application of arti ﬁ cial intelligence to understand the pathophysiological basis of psychogenic nonepileptic seizures ☆

Psychogenic nonepileptic seizures (PNES) are episodes of paroxysmal impairment associated with a range of motor, sensory, and mental manifestations, which perfectly mimic epileptic seizures. Several patterns of neural abnormalities have been described without identifying a de ﬁ nite neurobiological substrate. In this multicenter cross-sectional study, we applieda multivariateclassi ﬁ cation algorithm on morphological brain imaging metrics to extract reliable biomarkers useful to distinguish patients from controls at an individual level. Twenty-three patients with PNES and 21 demographically matched healthy controls (HC) underwent an extensive neuropsychiatric/neuropsychological and neuroimaging assessment. One hundred and ﬁ fty morphological brain metrics were used for training a random forest (RF) machine-learning (ML) algorithm. A typical complex psychopathological construct was observed in PNES. Similarly, univariate neuroimaging analysis revealed widespread neuroanatomical changes affecting patients with PNES. Machine-learning approach, after fea-tureselection,wasabletoperformanindividualclassi ﬁ cationofPNESfromcontrolswithameanaccuracyof74.5%, revealing that brain regions in ﬂ uencing classi ﬁ cation accuracy were mainly localized within the limbic (posterior cingulate and insula) and motor inhibition systems (the right inferior frontal cortex (IFC)). ThisstudyprovidesClassIIevidencethattheconsiderableclinicalandneurobiologicalheterogeneityobservedin individuals with PNESmightbe overcomeby ML algorithms trainedon surface-basedmagneticresonance imaging (MRI) data. © 2018 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Model-based Analysis of Distributed Real-time Embedded System Composition

Key challenges in distributed real-time embedded (DRE) system developments include safe composition of system components and mapping the functional specifications onto the target platform. Model-based verification techniques provide a way for the design-time analysis of DRE systems enabling rapid evaluation of design alternatives with respect to given performance measures before committing to a specific platform. This paper introduces a semantic domain for model-based analysis of a general class of DRE systems capturing their key time-based performance measures. We then utilize this semantic domain to develop a verification strategy for preemptive schedulability using available model checking tools. The proposed framework and verification strategy is demonstrated on a mission-critical avionics DRE system case study.
SIGMOD Officers, Committees, and Awardees

Data science and artiﬁcial intelligence are driven by a plethora of diverse data-related assets, including datasets, data streams, algorithms, processing software, compute resources, and domain knowledge. As providing all these assets requires a huge investment, data science and artiﬁcial intelligence technologies are currently dominated by a small number of providers who can afford these investments. This leads to lock-in effects and hinders features that require a ﬂexible exchange of assets among users. In this paper, we introduce Agora, our vision towards a uniﬁed ecosystem that brings together data, algorithms, models, and computational resources and provides them to a broad audience. Agora (i) treats assets as ﬁrst-class citizens and leverages a ﬁne-grained exchange of assets, (ii) allows for combining assets to novel applications, and (iii) ﬂexi-bly executes such applications on available resources. As a result, it enables easy creation and composition of data science pipelines as well as their scalable execution. In contrast to existing data management systems, Agora operates in a heavily decentralized and dynamic environment: Data, algorithms, and even compute resources are dynamically created, modiﬁed, and removed by different stakeholders. Agora presents novel research directions for the data management community as a whole: It requires to combine our traditional expertise in scalable data processing and management with infrastructure provisioning as well as economic and application aspects of data, algorithms, and infrastructure.
USENIX Association Proceedings of the FREENIX Track : 2004 USENIX Annual Technical Conference

In a typical file system, only the current version of a file (or directory) is available. In Wayback, a user can also access any previous version, all the way back to the file’s creation time. Versioning is done automatically at the write level: each write to the file creates a new version. Wayback implements versioning using an undo log structure, exploiting the massive space available on modern disks to provide its very useful functionality. Wayback is a userlevel file system built on the FUSE framework that relies on an underlying file system for access to the disk. In addition to simplifying Wayback, this also allows it to extend any existing file system with versioning: after being mounted, the file system can be mounted a second time with versioning. We describe the implementation of Wayback, and evaluate its performance using several benchmarks.
STRPRO Tool for Manipulation of Stratified Programs Based on SEPN

Negation is useful in the majority of the real world applications. However, its introduction leads to semantic and canonical problems. SEPN nets are well adapted extension of predicate nets for the definition and manipulation of stratified programs. This formalism is characterized by two main contributions. The first concerns the management of the whole class of stratified programs. The second contribution is related to usual operations optimization (maximal stratification, incremental updates …). We propose, in this paper, useful algorithms for manipulating stratified programs using SEPN. These algorithms were implemented and validated with STRPRO tool.
Probability, Statistics and Queueing Theory with Computer Science Applications

Introduction. Probability. Probability and Random Variables. Probability Distributions. Stochastic Processes. Queueing Theory. Queueing Theory. Queueing Models of Computer Systems. Statistical Inference. Estimation and Data Analysis. Hypothesis Testing. Regression. Appendices. Statistical Programs. APL Programs. Queueing Theory Definitions and Formulas. Mathematica Programs. Answers to Exercises.
SoftHandler: An Integrated Soft Robotic System for the Handling of Heterogeneous Objects

The picking performance of a robot can be severely affected by measurement errors, especially when handling objects that are fragile or irregular in shape and size. This is one of the main reasons why the problem of autonomously picking and placing objects is still open. In this work, we exploit the “embodied” intelligence of soft robotic technologies to propose an integrated system, named SoftHandler, capable of overcoming some of the limitations of traditional pick-andplace industrial robots. The SoftHandler integrates a novel parallel soft manipulator, the SoftDelta, and a novel soft endeffector, the Pisa/IIT SoftGripper. We describe the mechatronic design and control architectures of the system, including a learning technique able to preserve the natural compliance of the system. After that, we design a benchmarking method, and we experimentally compare the developed soft manipulator with its rigid counterpart. Experimental results with reference objects show that the proposed system has a larger grasping success rate than the rigid one and is subject to smaller interaction forces during the picking phase. Finally, we report an extensive experimental validation of the grasping capability of the SoftHandler with real objects in realistic, physically simulated, scenarios, as the ones of bin picking, grocery and raw food handling.
This paper is included in the Proceedings of the 17th USENIX Symposium on Networked Systems Design and Implementation (NSDI ’20)

The emergence of dense, byte-addressable nonvolatile main memories (NVMMs) allows application developers to combine storage and memory into a single layer. With NVMMs, servers can equip terabytes of memory that survive power outages, and all of this persistent capacity can be managed through a specialized NVMM ﬁle system. NVMMs appear to mesh perfectly with another popular technology, remote direct memory access (RDMA). RDMA gives a client direct access to memory on a remote machine and mediates this access through a memory region abstraction that handles the necessary translations and permissions. NVMM and RDMA seem eminently compatible: by combining them, we should be able to build network-attached, byte-addressable, persistent storage. Unfortunately, however, the systems were not designed to work together. An NVMM-aware ﬁle system manages persistent memory as ﬁles, whereas RDMA uses a different abstraction — memory regions to organize remotely accessible memory. As a result, in practice, building RDMA-accessible NVMMs requires expensive translation layers resulting from this duplication of effort that spans permissions, naming, and address translation. This work introduces two changes to the existing RDMA protocol: ﬁle memory region (FileMR) and range-based address translation. These optimizations create an abstraction that combines memory regions and ﬁles: a client can directly access a ﬁle backed by NVMM ﬁle system through RDMA, addressing its contents via ﬁle offsets. By eliminating redundant translations, it minimizes the amount of translations done at the NIC, reduces the load on the NIC’s translation cache and increases the hit rate by 3.8 × - 340 × and resulting in application performance improvement by 1.8 × - 2.0 × .
A Multiple Level Network Approach for Clock Skew Minimization Under Process Variations

In this paper, we investigate the effect of multilevel networks on clock skew. We first define the simplified RC circuit model of a hybrid clock mesh/tree structure. The skew reduction effects of shunt segments contributed by the mesh is derived analytically from the simplified model. The result indicates that the skew decreases proportionally to the exponential of −Rs/R, whereRs is the driving resistance of the clock buffer at a leaf node in the clock tree and R is the resistance of a mesh segment. Based on our analysis, we propose a hybrid multi-level mesh and tree structure for global clock distribution. A simple optimization scheme is adopted to optimize the routing resource distribution of the multi-level mesh. Experimental results show that by adding a mesh to the bottom-level leaves of an H-tree, the clock skew can be reduced from 29.2 ps to 8.7 ps, and the multi-level networks with same total routing area can further reduce the clock skew by another 30%. We also discuss the inductive effect of the mesh. When the clock frequency is less than 2 GHz, the RC model remains valid for clock meshes with grounded shielding or using differential signals. H. Chen, B. Yao, and C.K. Cheng are with department of computer science and engineering, University of California, San Diego. M. Mori is currently with Fujitsu Inc., Japan. His work was performed while he was with the department of computer science and engineering, University of California, San Diego
An Adaptive Conditional Zero-Forcing Decoder With Full-Diversity, Least Complexity and Essentially-ML Performance for STBCs

A low complexity, essentially-ML decoding technique for the Golden code and the three antenna Perfect code was introduced by Sirianunpiboon, Howard and Calderbank. Though no theoretical analysis of the decoder was given, the simulations showed that this decoding technique has almost maximum-likelihood (ML) performance. Inspired by this technique, in this paper we introduce two new low complexity decoders for Space-Time Block Codes (STBCs)-the Adaptive Conditional Zero-Forcing (ACZF) decoder and the ACZF decoder with successive interference cancellation (ACZF-SIC), which include as a special case the decoding technique of Sirianunpiboon We show that both ACZF and ACZF-SIC decoders are capable of achieving full-diversity, and we give a set of sufficient conditions for an STBC to give full-diversity with these decoders. We then show that the Golden code, the three and four antenna Perfect codes, the three antenna Threaded Algebraic Space-Time code and the four antenna rate 2 code of Srinath and Rajan are all full-diversity ACZF/ACZF-SIC decodable with complexity strictly less than that of their ML decoders. Simulations show that the proposed decoding method performs identical to ML decoding for all these five codes. These STBCs along with the proposed decoding algorithm have the least decoding complexity and best error performance among all known codes for Nt ≤ 4 transmit antennas. We further provide a lower bound on the complexity of full-diversity ACZF/ACZF-SIC decoding. All the five codes listed above achieve this lower bound and hence are optimal in terms of minimizing the ACZF/ACZF-SIC decoding complexity. Both ACZF and ACZF-SIC decoders are amenable to sphere decoding implementation.
Modeling the Cross Section of Stock Returns Using Sensible Models in a Model Pool

We propose a new two-step trimming-then-pooling procedure to forecast joint distributions of asset returns using a large pool of asset pricing models. This procedure allows us to focus on certain regions of the distributions. The first step trims uninformative models from a pool of candidates, and the second step pools the forecasts of the surviving models. This procedure enhances portfolio performance significantly and predicts downside risk precisely, and the improvements are due mainly to trimming. The pool of sensible models becomes larger when focusing on extreme events, responds rapidly to rising uncertainty, and reflects the magnitude of the size premium. These findings provide a new angle to evaluate asset pricing models. JEL Classification: C52; C53; C58; G12
A Novel Fault Diagnosis Method Based on Topological Data Analysis

In this paper, we proposed a new method for fault diagnosis of mechanical bearings. The method is based on topological data analysis (TDA) technology, such as persistent homology to analyze time series. We used Case Western Reserve University bearing dataset to conduct experiments for different fault diameters and different load conditions. Through comparison with previous work, the main contribution of this paper is to use topological data analysis to enhance the representativeness and expressiveness of the extracted features. The experimental results under different working conditions to verify the effectiveness and development of the proposed model. We provided a new idea for the field of fault diagnosis.
MIT Sloan School of Management

We propose a boundedly-rational model of opinion formation where agents are subject to the phenomenon of persuasion. We argue that persuasion – whereby repeated exposure to an opinion has a cumulative effect on an agent’s beliefs – is pervasive and closely related to the concept of social influence. In our model, agents communicate repeatedly according to a social network, but fail to adjust properly for possible repetitions of information they receive. We show that under general conditions, agents’ beliefs converge over time to a common belief, which is a weighted average of initial beliefs. Agents’ weights can be characterized in several manners related to their network position, and can be interpreted as a measure of social influence. Furthermore, agents’ multi-dimensional beliefs generally converge to a single line prior to obtaining a consensus. Thus, long-run differences in opinion can be characterized by a uni-dimensional measure. We explore the implications of our model in several natural settings, including neighborhoods with bilateral communication, hierarchies, and political science. ∗DeMarzo: Graduate School of Business, Stanford University, Stanford CA 94305, Vayanos: MIT Sloan School of Management, 50 Memorial Drive E52-437, Cambridge MA 02142, Zwiebel: Graduate School of Business, Stanford University, Stanford CA 94305. This paper is an extensive revision of our paper, “A Model of Persuasion – With Implication for Financial Markets,” (first draft, May 1997). We are grateful to Nick Barberis, Gary Becker, Jonathan Bendor, Larry Blume, Simon Board, Darrell Duffie, David Easley, Glenn Ellison, Simon Gervais, Ken Judd, David Kreps, Edward Lazear, Lee Nelson, Anthony Neuberger, Matthew Rabin, José Scheinkman, Antoinette Schoar, Peter Sorenson, Pietro Veronesi, Richard Zeckhauser, and seminar participants at the American Finance Association Annual Meetings, Boston University, Cornell, Carnegie-Mellon, ESSEC, the European Summer Symposium in Financial Markets at Gerzensee, HEC, the Hoover Institution, Insead, MIT, the NBER Asset Pricing Conference, the Northwestern Theory Summer Workshop, NYU, the Stanford Institute for Theoretical Economics, Stanford, Texas A&M, UCLA, U.C. Berkeley, Université Libre de Bruxelles, University of Michigan, Univerity of Texas at Austin, University of Tilburg, and the Utah Winter Finance Conference for helpful comments and discussions. All errors are our own.
An intelligent framework for monitoring student performance using fuzzy rule-based linguistic summarization

—Monitoring students’ activity and performance is vital to enable educators to provide effective teaching and learning in order to better engage students with the subject and improve their understanding of the material being taught. We describe the use of a fuzzy Linguistic Summarisation (LS) technique for extracting linguistically interpretable scaled fuzzy weighted rules from student data describing prominent relationships between activity / engagement characteristics and achieved performance. We propose an intelligent framework for monitoring individual or group performance during activity and problem based learning tasks. The system can be used to more effectively evaluate new teaching approaches and methodologies, identify weaknesses and provide more personalised feedback on learner’s progress. We present a case study and initial experiments in which we apply the fuzzy LS technique for analysing the effectiveness of using a Group Performance Model (GPM) to deploy Activity Led Learning (ALL) in a Master-level module. Results show that the fuzzy weighted rules can identify useful relationships between student engagement and performance providing a mechanism allowing educators to transparently evaluate teaching and factors effecting student performance, which can be incorporated as part of an automated intelligent analysis and feedback system.
Towards a biologically-plausible computational model of ventral visual cortex

BEWARE: These are only highly preliminary notes. In the future, they will become part of a textbook on " Visual Object Recognition ". In the meantime, please interpret with caution. Feedback is welcome at Lecture 11: Towards a biologically-plausible computational model of ventral visual cortex We have now come a long way since our initial steps towards defining the proble of visual recognition. We started with characterizing the spatial and temporal statistics of natural images (Lecture 2). We explored how neurons along ventral visual cortex respond to a variet of different stimulus conditions (Lectures 3, 5, 7, 8). We described the recognition impairments that arise through cortical lesions (Lecture 4) and the effect of applying currents to the neural circuitry (Lecture 9). We would like to put all of these separate bits and pieces of data into a coherent framework to rigorously understand how neuronal circuits help us recognize objecst. Here we summarize some of the initial steps towards a theoretical understanding of the computational principles behind transformation-invariance visual recognition in the primate cortex. 11.1 Defining the problem We start by defining what needs to be explained and the necessary constraints to solve the problem. A theory of visual object recognition, implemented by a computational model, should be able to explain the following phenomena and have the following characteristics: 1. Selectivity. The primate visual system shows a remarkable degree of selectivity and can differentiate among shapes that appear to be very similar at the pixel level (e.g. arbitrary 3D shapes created from paperclips, different faces, etc.). Critical to object recognition, a model should be able to discriminate among physically similar but distinct shapes. 2. Transformation tolerance. A trivial solution to achieve high selectivity would be to memorize all the pixels in the object. The problem with this type of algorithm is that it would not tolerate any changes in the image. An object can cast an infinite number of projections onto the retina. These changes arise due to changes in object position with respect to fixation, object scale, plane or depth rotation, changes in contrast or illumination, color, occlusion and others. The importance of combining selectivity and tolerance has been emphasized by many investigators (e.g.
Sketch-based Dynamic Illustration of Fluid Systems

This paper presents a lightweight sketching system that enables interactive illustration of complex ﬂuid systems. Users can sketch on a 2.5-dimensional (2.5D) canvas to design the shapes and connections of a ﬂuid circuit. These input sketches are automatically analyzed and abstracted into a hydraulic graph, and a new hybrid ﬂuid model is used in the background to enhance the illustrations. The system provides rich simple operations for users to edit the ﬂuid system incrementally, and the new internal ﬂow patterns can be simulated in real time. Our system is used to illustrate various ﬂuid systems in medicine, biology, and engineering. We asked professional medical doctors to try our system and obtained positive feedback from them.
Synonym Dictionary Improvement through Markov Clustering and Clustering Stability

Abstract. The aim of the work presented here is to clean up a dictionary of synonyms which appeared to be ambiguous, incomplete and inconsistent. The key idea is to use Markov Clustering and Clustering Stability techniques on the network that represents the synonymy relation contained in the dictionary. Each densely connected cluster is considered to correspond to a specific concept, and ambiguous words are identified by the evaluation of the stability of the clustering under noise. This allows to disambiguate polysemic words, introducing missing senses where required, and merge similar senses of a same word if necessary.
Archives des publications du CNRC

Statistical machine translation systems are usually trained on large amounts of bilingual text (used to learn a translation model), and also large amounts of monolingual text in the target language (used to train a language model). In this article we explore the use of semi-supervised model adaptation methods for the effective use of monolingual data from the source language in order to improve translation quality. We propose several algorithms with this aim, and present the strengths and weaknesses of each one. We present detailed experimental evaluations on the French–English EuroParl data set and on data from the NIST Chinese–English large-data track. We show a signiﬁcant improvement in translation quality on both tasks.