Miracle-dz/UniFAR-FTUs huggingface dataset:
Clustered Modulo Scheduling in a VLIW Architecture with Distributed Cache

Clustering is an approach that many microprocessors are adopting in recent times in order to mitigate the increasing penalties of wire delays. In this work we propose a novel clustered VLIW architecture which has all its resources partitioned among clusters, including the cache memory. A modulo scheduling scheme for this architecture is also proposed. This algorithm takes into account both register and memory inter-cluster communications so that the final schedule results in a cluster assignment that favors cluster locality in cache references and register accesses. It has been evaluated for both 2and 4-cluster configurations and for differing number and latencies of inter-cluster buses. The proposed algorithm produces schedules with very low communication requirements and outperforms previous cluster-oriented schedulers.
REPORT RAPPORT

Sherali and Adams [SA90], Lov(cid:19)asz and Schrijver [LS91] and, recently, Lasserre [Las01b] have proposed lift and project methods for constructing hierarchies of successive linear or semide(cid:12)nite relaxations of a 0 − 1 polytope P (cid:18) R n converging to P in n steps. Lasserre’s approach uses re-sults about representations of positive polynomials as sums of squares and the dual theory of moments. We present the three methods in a common elementary framework and show that the Lasserre construction provides the tightest relaxations of P . As an application this gives a direct simple proof for the convergence of the Lasserre’s hierarchy. We describe applications to the stable set polytope and to the cut polytope.
GENERATIVE AND DISCRIMINATIVE CLASSIFIERS : NAIVE BAYES AND LOGISTIC REGRESSION Machine Learning

This is a rough draft chapter intended for inclusion in a possible second edition of the textbook Machine Learning, T.M. Mitchell, McGraw Hill. You are welcome to use this for educational purposes, but do not duplicate or repost it on the internet. For online copies of this and other materials related to this book, visit the web site www.cs. cmu.edu/ ∼tom/mlbook.html. Please send suggestions for improvements, or suggested exercises, to Tom.Mitchell@cmu.edu.
CHAPTER 3 GENERATIVE AND DISCRIMINATIVE CLASSIFIERS : NAIVE BAYES AND LOGISTIC REGRESSION Machine Learning

This is a rough draft chapter intended for inclusion in the upcoming second edition of the textbook Machine Learning, T.M. Mitchell, McGraw Hill. You are welcome to use this for educational purposes, but do not duplicate or repost it on the internet. For online copies of this and other materials related to this book, visit the web site www.cs. cmu.edu/∼tom/mlbook.html. Please send suggestions for improvements, or suggested exercises, to Tom.Mitchell@cmu.edu.
Affective Automotive User Interfaces - Reviewing the State of Emotion Regulation in the Car

Affective technology offers exciting opportunities to improve road safety by catering to human emotions. Modern car interiors enable the contactless detection of user states, paving the way for a systematic promotion of safe driver behavior through emotion regulation. We review the current literature regarding the impact of emotions on driver behavior and analyze the state of emotion regulation approaches in the car. We summarize challenges for affective interaction in form of cultural aspects, technological hurdles and methodological considerations, as well as opportunities to improve road safety by reinstating drivers into an emotionally balanced state. The purpose of this review is to outline the community's combined knowledge for interested researchers, to provide a focussed introduction for practitioners and to identify future directions for affective interaction in the car.
Knowledge Intensive Software Engineering Tools

Most software engineering tools use a shallow representation of software objects and manipulate this representation using procedural methods. This approach allows one to get off to a fast start and quickly provides a tool that delivers benefits. However, a point will be reached where more knowledge-intensive approaches will be needed to achieve significantly higher levels of capability. The authors suggest that the software engineering tools of the future will have to rely on: deep representation to capture a sufficiently large part of knowledge about programming in general and particular programs; inspection methods to deal with complexity; and intelligent assistance. >
Hamburger Beitr¨age zur Angewandten Mathematik

The challenging task of separating or extracting components from a signal has multiple applications in biomedical signal analysis, speech and musical acoustics, or image processing. Fourier analysis, wavelet transforms and frame theory are powerful tools for addressing these problems. However, due to the high complexity of many real-world signals, more sophisticated analysis methods are essentially required. During the last few years, there has been a strong development in data analysis using concepts from diﬀerential geometry and algebraic topology for analyzing the dimensionality reduction problem of high-dimensional data. In this paper, we propose a novel approach for signal separation and classiﬁcation combining standard signal processing tools with geometric algebra and dimensionality reduction. Our concept relies on geometrical transformations, deﬁned in the context of Cliﬀord algebras, for modifying the geometry of a point cloud data. The objective is to transform signal components to elements of an orthonormal basis, where ﬁltering procedures can be applied. This new framework is an addition to the manifold learning and dimensionality reduction toolbox, combining ideas from Cliﬀord algebras, dimensionality reduction, cluster analysis via persistent homology, and ﬁltering procedures in signal analysis. Some computational experiments 1 are presented indicating the potential and shortcomings of this framework.
HEER - a delay-aware and energy-efficient routing protocol for wireless sensor networks

Minimizing energy consumption to maximize network lifetime is one of the crucial concerns in designing wireless sensor network routing protocols. Cluster-based protocols have shown promising energy-efficiency performance, where sensor nodes take turns to act as cluster heads (CHs), which carry out higher-level data routing and relaying. In such case the energy consumption is more evenly distributed for all the nodes. However, most cluster-based protocols improve energy-efficiency at the cost of transmission delay. In this paper, we propose an improved delay-aware and energy-efficient clustered protocol called Hamilton Energy-Efficient Routing Protocol (HEER). HEER forms clusters in the network initialization phase and links members in each cluster on a Hamilton Path, constructed using a greedy algorithm, for data transmission purpose. No cluster reformation is required and the members on the path will take turns to become cluster head. The design allows HEER to save on network administration energy and also balance the load comparing to traditional cluster-based protocols. The algorithms designed in HEER also means that it does not suffer long delay and does not require each node to have global location information comparing with classic chain-based protocols such as PEGASIS and its variations. We implemented the HEER protocol in MATLAB simulation and compared it with several cluster-based and chain-based protocols. We found that HEER is able to achieve an improved network lifetime over the current protocols while maintaining the average data transmission delay. In the simulation, HEER achieved 66.5% and 40.6% more rounds than LEACH and LEACH-EE, which are cluster-based protocols. When compared with chain-based protocols (PEGASIS and Intra-grid-PEGSIS), HEER managed 21.2 times and 16.7 times more rounds than PEGASIS and Intra-grid-PEGASIS respectively. In addition, HEER can eliminated 90% of transmission delay comparing to LEACH and LEACH-EE and 99% comparing with PEGASIS and Intra-grid-PEGASIS. Keywords— routing, WSNs, Hamilton Path, clustering
LiDAR-based mapping of paleo-ice streams in the eastern Great Lakes sector of the Laurentide Ice Sheet and a model for the evolution of drumlins and MSGLs

Abstract Analysis of newly available, high resolution DEM and LiDAR imagery permits detailed geomorphological mapping of glacially streamlined subglacial bedforms (drumlins and megascale glacial lineations; MSGLs) in unprecedented detail over a very large (170,500 km2) area of eastern North America extending from the western end of Lake Erie east through the Lake Ontario basin to the Hudson Valley east of the Adirondack Mountains, and south to the Finger Lakes. Digital imagery and field work identify the beds of eight paleo-ice streams that record a major region-wide ice streaming event within the Laurentide Ice Sheet (LIS) which appears to have commenced after c. 14,400 ybp. A large southwestward-flowing ice stream (the Ontario-Erie Ice Stream; OEIS) sourced from the southern Quebec sector of LIS was initiated along the axis of the Ontario basin coeval with its counterpart to the east, the long-recognized St. Lawrence Ice Stream flowing from the same Quebec source to Gulf of St. Lawrence. Deep ice-frontal lakes at the margin of the retreating ice sheet may have played a role in the onset of fast flow similar to its easternmost marine-terminating margin. It is hypothesized that drumlin bedforms formed under steady state flow regimes are reshaped and dissected into MSGLs by deforming subglacial debris as fast flow propagates upglacier from the ice margin or grounding line. Ice is pulled out of ice sheets by headward-propagating ice streams; the formation of MSGLs from drumlins reflects accompanying subglacial erosion by deforming subglacial sediment to reduce bed relief and basal drag.
Dixie Dixit: New Trends with Technology Gamification: What, Why, and How (Part 2)

In a previous column, I discussed the term gamification. I mentioned that it can be defined as the application of game-like elements to non-gaming contexts (Deterding et al., 2011) but it goes beyond just including games in a class. I also mentioned how gamification is being used in different types of courses and at different levels. In this second column, I will explore the application of gamification specifically in the language classroom. So far, there is little literature on the use of gamification for language learning, although the language classroom seems ideally suited to it. Gamification in education is still relatively new and the pioneers tend to work in other disciplines. One exception, however, is Lee Sheldon (Plenda, 2014) who had extensive experience working as a television scriptwriter before moving to teaching. He has used his writing experience to develop complex stories where participants play roles and learn basic Mandarin as a language. Abrams and Walsh (2014) have carried out studies on adolescents' vocabulary learning through online gamified tools. Specifically, they looked at a free online platform which uses gamified structures to help users increase their vocabulary (www.vocabulary.com). The site teaches and tests vocabulary in context, via multiple choice options in example sentences or in longer reading passages. It offers tips on how to look up words, and definitions and pronunciation help, quizzes and challenges. It can be accessed via computers or mobile devices. It was not developed especially for second language learners, but it is a great site and can be very useful. Abrams and Walsh (2014) conducted two studies: one included using the above mentioned site (www.vocabulary.com) in the context of extracurricular tutoring and the other study incorporated the site into a literature course. Levels of engagement among the participants varied, but most showed an important increase in vocabulary knowledge, not just in word recognition, but in use as well. A third example of gamification in the language classroom took place in my university, and I will provide the details. Two teachers participated in a Massive Online Open Course (MOOC) on the subject of gamification, and were interested in applying the concepts to their classes. They received permission to do so. They used the intensive summer course to pilot their ideas, and then started to document their activities and students' reactions and progress. Five groups of students participated in the course which was at a level equivalent to B2 on the Common European Framework scale. There was an average of 16 students in each group (81 students in all). The course ran for sixteen weeks, from May to November of 2014. The premise of the course was that students were working for a firm called Creative, Inc. which provides creative solutions to business problems. The students did not use a coursebook and there was no focus on
Poster: detection and correction of silent data corruption for large-scale high-performance computing

Faults have become the norm rather than the exception for high-end computing on clusters with 10s/100s of thousands of cores. Exacerbating this situation, some of these faults will not be detected, manifesting themselves as silent errors that will corrupt memory while applications continue to operate and report incorrect results. This paper introduces RedMPI, an MPI library which resides in the MPI profiling layer. RedMPI is capable of both online detection and correction of soft errors that occur in MPI applications without requiring any modifications to the application source. By providing redundancy, RedMPI is capable of transparently detecting corrupt messages from MPI processes that become faulted during execution. Furthermore, with triple redundancy RedMPI additionally "votes" out MPI messages of a faulted process by replacing corrupted results with corrected results from unfaulted processes. We present an experimental evaluation of RedMPI on an assortment of applications to demonstrate the effectiveness of this approach.
Sequence Models

Suppose we have data x ∈ RT×d, which we will interpret as a sequence of d-dimensional vectors xt indexed by a discrete temporal 0 ≤ t < T . As usual, we are interested in modeling the unknown distribution p over a collection of observed sequences x1, . ,xn ∼ p (for time series, we will index sequences with a superscript to distinguish this index from the temporal index). One way to approach time series modeling is to ignore the temporal structure, and simply treat sequences as vectors in RTd. This is technically valid, but there are a couple of reasons we might want to incorporate temporal structure into our models:
Building Watson : An Overview of the DeepQA Project

by exploring new ways for computer technology to affect science, business, and society. Roughly three years ago, IBM Research was looking for a major research challenge to rival the scientific and popular interest of Deep Blue, the computer chess-playing champion (Hsu 2002), that also would have clear relevance to IBM business interests. With a wealth of enterprise-critical information being captured in natural language documentation of all forms, the problems with perusing only the top 10 or 20 most popular documents containing the user’s two or three key words are becoming increasingly apparent. This is especially the case in the enterprise where popularity is not as important an indicator of relevance and where recall can be as critical as precision. There is growing interest to have enterprise computer systems deeply analyze the breadth of relevant content to more precisely answer and justify answers to user’s natural language questions. We believe advances in question-answering (QA) technology can help support professionals in critical and timely decision making in areas like compliance, health care, business integrity, business intelligence, knowledge discovery, enterprise knowledge management, security, and customer support. For Articles
COMPARATIVE ANALYSIS OF HIDDEN MARKOV MODELS FOR MULTI-MODAL DIALOGUE SCENE INDEXING

A class of audio-visual content is segmented into dialogue scenes using the state transitions of a novel hidden Markov model (HMM). Each shot is classi ed using both audio track and visual content to determine the state/scene transitions of the model. After simulations with circular and left-to-right HMM topologies, it is observed that both are performing very good with multi-modal inputs. Moreover, for circular topology, the comparisons between different training and observation sets show that audio and face information together gives the most consistent results among di erent observation sets.
Computational scientific inquiry with virtual worlds and agent-based models: New ways of “doing” science to learn science

Free to read on publisher's website The main objective of this paper is to report on design research that involves the use of a virtual “game-like” world in conjunction with agent-based computer models to enable an innovative learning approach we refer to as computational scientific inquiry. In particular, we present the results of a study that explored a set of computational inquiry activities intended to help secondary students learn general principles of scientific inquiry and scientific knowledge about biological systems.
A rational subdivision scheme using cosine-modulated wavelets

This paper proposes a rational subdivision scheme using cosine-modulated wavelets. Subdivision schemes constructed from iterated filter banks can be used to generate wavelets and limit functions for multiresolution analysis. The proposed subdivision scheme is based on a kind of nonuniform filter banks called recombination nonuniform filter banks (RN FB). It is shown that if the component FBs in a RNFB are wavelet FBs, then the necessary condition for convergence to limit functions in the subdivision scheme is also satisfied. Therefore, the design of different rational subdivision schemes is considerably simplified. An efficient RNFB, called RN cosine modulated FBs (CMFB), constructed from uniform CMFBs and cosine-modulated transmultiplexers (TMUX) are further investigated. Using a design technique for designing RN CMFB and cosine modulated wavelets (CMW) previously reported by the authors, very smooth limit functions can be generated from the rational subdivision scheme. A design example is given to illustrate the proposed method.
Computer Vision and Image Understanding

Skeleton-based human action recognition has been drawing more interest recently due to its low sensitivity to appearance changes and the accessibility of more skeleton data. However, the skeletons captured in practice are sensitive to the view of an actor, given the occlusion of different human-body joints and the errors in human joint localization. Each view is noisy and incomplete, but important factors, such as motion and semantics, should be shared between all views in action representation learning. We support the classic hypothesis that a powerful representation is one that models view-invariant factors, and so does unsupervised learning. Therefore, we study this hypothesis under the framework of contrastive multiview learning, where we learn a representation for action recognition that aims to maximize the mutual information between different views of the same action sequence. Apart from that, a global–local contrastive loss is proposed to model the multi-scale co-occurrence relationships in both spatial and temporal domains. Extensive experimental results show that the proposed method significantly boosts the performance of unsupervised skeleton-based human action methods on three challenging benchmarks of PKUMMD, NTU RGB+D 60, and NTU RGB+D 120.
Weikum On the Usage of Global Document Occurrences in Peer-to-Peer Information Systems

I hereby certify that the work contained in this Master thesis is my own work, unless explicitly mentioned and cited. I certify that the electronic version of this Master thesis is exactly the same as the printed version of the thesis. I give permission to the University of Saar-land to publish this thesis at the Data Network Odysseas Papapetrou Saarbrücken, October 2005 Acknowledgments I would like to thank Prof. Dr. Gerhard Weikum, for his guidance in the completion of this research. Special thanks belong to my tutors, Sebastian Michel and Matthias Bender. Working with them was a wonderful experience. Furthermore, I would like to thank Kerstin Meyer Ross, the IMPRS coordinator, as well as the IMPRS institution, providing me the stipend for my M.Sc. degree. Last, but not least I would like to thank my wife, Katerina Ioannou, for the moral support and advices. Summary There exist a number of approaches for query processing in Peer-to-Peer information systems that efficiently retrieve relevant information from distributed peers. However, very few of them take into consideration the overlap between peers. As the most popular resources (e.g., documents or files) are often present at most of the peers, a large fraction of the documents eventually received by the query initiator are duplicates. In this work we develop a technique based on the notion of global document occurrences (GDO), that, when processing a query, penalizes frequent documents increasingly as more and more peers contribute their local results. We argue that the additional effort to create and maintain the GDO information is reasonably low, as the necessary information can be piggybacked onto the existing communication. Our experiments indicate that our approach significantly decreases the number of peers that have to be involved in a query to reach a certain level of recall and, thus, decreases user-perceived latency and the wastage of network resources.
1 State-ofthe-Art of Intention Recognition and its use in Decision Making – A Research Summary The

Intention recognition is the process of becoming aware of the intentions of other agents, inferring them through observed actions or effects on the environment. Intention recognition enables pro-activeness, in cooperating or promoting cooperation, and in preempting danger. Technically, intention recognition can be performed incrementally as you go along, which amounts to learning. Intention recognition can also use past experience from a database of past interactions, not necessarily with the same agent. Bayesian Networks (BN) can be employed to dynamically summarize general statistical evidence, furnishing heuristic information to link with the situation specific information, about which logical reasoning can take place, and decisions made on actions to be performed, possibly involving actions to obtain new observations. This situated reasoning feeds into the BN to tune it, and back again into the logic component. In this article, we provide a review bearing on the state-of-the-art work on intention and plan recognition, which includes a comparison with our recent research, where we address a number of important issues of intention recognition. We also argue for an integrative approach to
EDITORIAL Reading the Digital Tea Leaves Hope or Hype for Peripheral Artery Disease

In 1950, Winsor proposed the ankle-brachial index as an accessible and cost-effective means to diagnose peripheral artery disease (PAD). 1 That same year, Alan Turing, the mathematics prodigy who famously helped decode the Nazi Enigma machine and bend the course of World War II, laid the groundwork for the role that artificial intelligence (AI) could play in the future.2 Seventy years later, these bold predictions and pioneering work have collided to address perennial deficiencies in disease detection, which is perhaps the central challenge to reducing disparities and improving outcomes for patients with ambulatory PAD.