Miracle-dz/UniFAR-FTUs huggingface dataset:
Cache Memories

design issues. Specific aspects of cache memories tha t are investigated include: the cache fetch algorithm (demand versus prefetch), the placement and replacement algorithms, line size, store-through versus copy-back updating of main memory, cold-start versus warm-start miss ratios, mulhcache consistency, the effect of input /output through the cache, the behavior of split data/instruction caches, and cache size. Our discussion includes other aspects of memory system architecture, including translation lookaside buffers. Throughout the paper, we use as examples the implementation of the cache in the Amdahl 470V/6 and 470V/7, the IBM 3081, 3033, and 370/168, and the DEC VAX 11/780. An extensive bibliography is provided.
Latent Words Recurrent Neural Network Language Models for Automatic Speech Recognition

This paper demonstrates latent word recurrent neural network language models (LW-RNN-LMs) for enhancing automatic speech recognition (ASR). LW-RNN-LMs are constructed so as to pick up advantages in both recurrent neural network language models (RNN-LMs) and latent word language models (LW-LMs). The RNN-LMs can capture longrange context information and offer strong performance, and the LW-LMs are robust for out-of-domain tasks based on the latent word space modeling. However, the RNN-LMs cannot explicitly capture hidden relationships behind observed words since a concept of a latent variable space is not present. In addition, the LW-LMs cannot take into account long-range relationships between latent words. Our idea is to combine RNN-LM and LW-LM so as to compensate individual disadvantages. The LW-RNN-LMs can support both a latent variable space modeling as well as LW-LMs and a long-range relationship modeling as well as RNN-LMs at the same time. From the viewpoint of RNN-LMs, LW-RNN-LM can be considered as a soft class RNN-LM with a vast latent variable space. In contrast, from the viewpoint of LW-LMs, LW-RNN-LM can be considered as an LWLM that uses the RNN structure for latent variable modeling instead of an n-gram structure. This paper also details a parameter inference method and two kinds of implementation methods, an n-gram approximation and a Viterbi approximation, for introducing the LW-LM to ASR. Our experiments show effectiveness of LW-RNN-LMs on a perplexity evaluation for the Penn Treebank corpus and an ASR evaluation for Japanese spontaneous speech tasks. key words: latent words recurrent neural network language models, ngram approximation, Viterbi approximation, automatic speech recognition
Scaling up POMDPs for Dialog Management: The ``Summary POMDP'' Method

Partially observable Markov decision processes (POMDPs) have been shown to be a promising framework for dialog management in spoken dialog systems. However, to date, POMDPs have been limited to artificially small tasks. In this work, we present a novel method called a "summary POMDP" for scaling slot-filling POMDP-based dialog managers to cope with tasks of a realistic size. An example dialog problem incorporating a user model built from real dialog data is presented. A dialog manager is created using this method and evaluated using a second user model created from held-out dialog data. Results confirm that summary POMDP policies scale well, and also show that summary POMDP policies are reasonably robust to variations in user behavior
Trio: a system for data, uncertainty, and lineage

In the Trio project at Stanford, we are building a new kindof database managementsystem: one in whichdata, uncer-tainty of the data, and datalineage are all ﬁrst-class citizensin an extended relational model and SQL-based query lan-guage. In an initial vision paper for the Trio project [5],we motivated the need for these three aspects to coexistin one system, and detailed numerous potential applica-tions including scientiﬁc data management, data cleaningandintegration,informationextractionsystems, andothers. (Speciﬁc example application scenarios will be discussedin Sections 2 and 4.)Since the inception of the project, we have:1. Studied the space of representation schemes for un-certain data, and properties of various schemes [3, 4]. 2. Proposed a new scheme called ULDBs. ULDBs ex-tend the relational model with simple forms of uncer-tainty that, when combined with lineage, yield niceproperties and strong expressiveness [1]. 3. Proposed a SQL-based query language for ULDBscalled TriQL (pronounced “treacle”). TriQL modiﬁesthe semantics of SQL to take uncertainty and lineageinto account, and introduces new constructs to queryuncertainty and lineage directly [2]. 4. Implemented a ﬁrst working prototype of our modeland language by building on top of a conventionalDBMS [2].
Quantifying and Predicting the Promiscuity and Isoform Specificity of Small-Molecule Cytochrome P450 Inhibitors

Drug promiscuity ( i.e. , inhibition of multiple enzymes by a single compound) is increasingly recognized as an important pharmacological consideration in the drug development process. However, systematic studies of functional or physicochemical characteristics that correlate with drug promiscuity are handicapped by the lack of a good way of quantifying promiscuity. In this paper, we present a new entropy-based index of drug promiscuity. We apply this index to two high-throughput data sets describing inhibition of cytochrome P450 isoforms by small-molecule drugs and drug candidates, and demonstrate how drug promiscuity or specificity can be quantified. For these drug-metabolizing enzymes, we find that there is essentially no correlation between a drug’s potency and specificity. We also present an index to quantify the susceptibilities of different enzymes to inhibition by diverse substrates. Finally, we use partial least-squares regression to successfully predict isoform specificity and promiscuity of small molecules, using a set of fingerprint-based descriptors.
Vlsi Digital Signal Processing Systems Design And Implementation

Coverage presents new material and theory as well as synthesis of recent work with complete Project Designs using industry standard CAD tools and FPGA boards. The reader is taken step by step through different designs, from implementing a single digital gate to a massive design consuming well over 100,000 gates. All the design codes developed in this book are Register Transfer Level (RTL) compliant and can be readily used or amended to suit new projects. high-performance a 2-D Discrete Cosine Transform for HDTV, and a wave digital filter for interpolation of The case studies cover the essential parts of the in a top-down from specification of algorithm synthesis of optimal realization to the
Egoscanning: quickly scanning first-person videos with egocentric elastic timelines

This work presents EgoScanning, a video fast-forwarding interface that helps users find important events from lengthy first-person videos continuously recorded with wearable cameras. This interface features an elastic timeline that adaptively changes playback speeds and emphasizes egocentric cues specific to first-person videos, such as hand manipulations, moving, and conversations with people, on the basis of computer-vision techniques. The interface also allows users to input which of such cues are relevant to events of their interest. Through our user study, we confirm that users can find events of interest quickly from first-person videos thanks to benefits of using the EgoScanning interface.
Forecasting seismicity rate in the north-west Himalaya using rate and state dependent friction law

ABSTRACT In this study, rate and state Coulomb stress transfer model is adopted to forecast the seismicity rate of earthquakes (MW ≥ 5) in the north-west Himalaya region within the testing period 2011–2013. Coulomb stress changes (ΔCFF), considered to be the most critical parameter in the model, exhibit stress increase in the whole study region, excluding the Chaman fault of the Kirthar range where significant stress shadow has been observed. The estimated background seismicity rate varies in the range 0.0–0.7 in the region, which is preoccupied by low aftershock duration of <50 years. Furthermore, a low b-value that varies between 0.54 and 0.83 is observed in Kirthar ranges, Karakoram fault and Pamir-Hindukush region. However, areas like Hazara syntaxis of the northern Pakistan and northern Pamir of the Eurasian plate exhibit higher b-values in the range 1.23–1.74. Considering constant constitutive properties of the faults (i.e. Aσ = 0.05 MPa), our forecast model for variable ΔCFF and heterogeneous b-value successfully captures the observed seismicity rate of earthquakes. Results have been verified using statistical S-test. However, the model fails to capture the observed seismicity rate during the period when reconstructed for average b-value to be 0.86 and no change in ΔCFF (ΔCFF = 0).
Surface height recovery from surface normals using manifold embedding

In this paper we show how surface height recovery from the field of surface normals can be posed as one of low dimensional embedding. To do this, we make use of the surface normals to compute the surface height increments corresponding to each location on the pixel lattice. With the height increments to hand, we can estimate the surface height difference between each pair of pixel-locations. We pose the problem of surface height recovery as that of embedding the surface normals on a manifold in a 3D space that preserves both the pattern of surface height differences and the lattice footprint of the field of surface normals. We present a sensitivity study on synthetic imagery and perform experiments on a variety of real world image data, where it produces qualitatively good reconstructed surfaces.
Digit Classication using 8-bit Floating Point based Convolutional Neural Networks

Training of deep neural networks is often constrained by the available memory and computational power. This often causes it to run for weeks even when the underlying platform is employed with multiple GPUs. In order to speed up the training and reduce space complexity the paper presents an approach of using reduced precision (8-bit) floating points for training hand-written characters classifier LeNeT-5 which allows for achieving 97.10% (Top-1 and Top-5) accuracy while reducing the overall space complexity by 75% in comparison to a model using single precision floating points. Keywords—approximate computing, deep learning
Using MPI-2: Advanced Features of the Message Passing Interface

From the Publisher: The Message Passing Interface (MPI) specification is widely used for solving significant scientific and engineering problems on parallel computers. There exist more than a dozen implementations on computer platforms ranging from IBM SP-2 supercomputers to clusters of PCs running Windows NT or Linux ("Beowulf" machines). The initial MPI Standard document, MPI-1, was recently updated by the MPI Forum. The new version, MPI-2, contains both significant enhancements to the existing MPI core and new features. Using MPI is a completely up-to-date version of the authors' 1994 introduction to the core functions of MPI. It adds material on the new C++ and Fortran 90 bindings for MPI throughout the book. It contains greater discussion of datatype extents, the most frequently misunderstood feature of MPI-1, as well as material on the new extensions to basic MPI functionality added by the MPI-2 Forum in the area of MPI datatypes and collective operations. Using MPI-2 covers the new extensions to basic MPI. These include parallel I/O, remote memory access operations, and dynamic process management. The volume also includes material on tuning MPI applications for high performance on modern MPI implementations.
SPECIAL COLLECTION: WATER IN NOMINALLY HYDROUS AND ANHYDROUS MINERALS OH defects in quartz as monitor for igneous, metamorphic, and sedimentary processes

Oriented sections of more than 500 quartz grains from sediments, igneous, and metamorphic rocks from different localities in Sweden, Austria, Germany, and South Africa were analyzed by FTIR spectroscopy, and their OH defect content was determined with respect to the speciation and total defect water content. Systematic variations of defect speciation and statistical evaluation of total defect contents were used to evaluate the potential of FTIR spectroscopy on quartz as a thermometer in quartzite, as a tool for differentiation trends in granitic systems, and for provenance analysis of sedimentary rocks. In addition to the analyses of natural crystals, high-pressure annealing experiments at lower crustal conditions (1–3 kbar and 650–750 °C) were performed to document the effect of high-grade metamorphism on the defect chemistry. Results indicate that (1) quartz grains from unmetamorphosed granite bodies reveal interesting differentiation trends; (2) sediments and sedimentary rocks are valuable archives to preserve the pre-sedimentary OH defect chemistry, where individual signatures are preserved and can be traced back to potential source rocks; (3) OH defects are retained up to 300 °C over geological time scales; (4) long-term low-grade metamorphic overprint leads to a continuous annealing to lower defect water contents, where Al-specific OH defects survive best; and (5) middle to high-grade annealing drives toward a homogeneous defect partitioning from grain to grain, where the degree of attainment of equilibrium depends on temperature and duration of the thermal event. In summary, OH defects in quartz crystals monitor parts of their geological history, and the systematic investigation and statistical treatment of a large amount of grains can be applied as an analytical tool to study sedimentary, metamorphic, and igneous processes.
International Journal of Advanced Research in Computer Science and Software Engineering

-Over the past decade or so, there has been rapid growth in wireless and mobile applications technologies. More recently, an increasing emphasis has been on the potential of infrastructure less wireless mobile networks that are easy, fast and in-expensive to set up, with the view that such technologies will enable numerous new applications in a wide range of areas. Such networks are commonly referred to as mobile ad hoc networks. In MANET, data is sent from one node to another node by using links, called path. Routing protocol is responsible to find out the path to be followed by the packets from source to destination. For MANET, there are different type of protocols are available and as per the requirement, we can use any one. Each protocol has its own criteria to find the path from source to destination. Some of researchers explored the concept of shortest path routing over ad hoc network. In this paper, we will analyse the impact of scalability over the mobile ad hoc network and the behaviour of shortest path routing algorithm.
SemEval-2010 Task 9: The Interpretation of Noun Compounds Using Paraphrasing Verbs and Prepositions

Previous research has shown that the meaning of many noun-noun compounds N 1 N 2 can be approximated reasonably well by paraphrasing clauses of the form ‘ N 2 that . N 1 ’, where ‘. ’ stands for a verb with or without a preposition. For example, malaria mosquito is a ‘ mosquito that carries malaria ’. Evaluating the quality of such paraphrases is the theme of Task 9 at SemEval-2010. This paper describes some background, the task deﬁnition, the process of data collection and the task results. We also venture a few general conclusions before the participating teams present their systems at the SemEval-2010 workshop. There were 5 teams who submitted 7 systems.
Constraint Based Frequent Pattern Mining Technique for Solving GCS Problem First

and Common substring problems. GCS are known to be NP-hard allowing the problems lies in the explosion of potential candidates. Finding longest center string without concerning the sequence that may not contain any motifs is not known in advance in any particular biological gene process. GCS solved by frequent pattern-mining techniques and known to be fixed parameter tractable based on the fixed input sequence length and symbol set size. Efficient method known as Bpriori algorithms can solve GCS with reasonable time/space complexities. Bpriori 2 and Bpriori 3-2 algorithm are been proposed of any length and any positions of all their instances in input sequences. In this paper, we reduced the time/space complexity of Bpriori algorithm by Constrained Based Frequent Pattern mining (CBFP) technique which integrates the idea of Constraint Based Mining and FP-tree mining. CBFP mining technique solves the GCS problem works for all center string of any length, but also for the positions of all their mutated copies of input sequence. CBFP mining technique construct TRIE like with FP tree to represent the mutated copies of center string of any length, along with constraints to restraint growth of the consensus tree. The complexity analysis for Constrained Based FP mining technique and Bpriori algorithm is done based on the worst case and average case approach. Algorithm's correctness compared with the Bpriori algorithm using artificial data is shown.
ROBUST MULTIPLE RESOLUTION AN SPEECH RECOGN

This paper describes the use of denoising techniques in the time domain applied to the outputs of filters corresponding to a Multi Resolution Analysis. The fact that energies of denoised samples are used for Automatic Speech Recognition (ASR) makes soft thresholding particularly attractive especially if Principal Component Analysis (PCA) is applied to the whole tree of energy features. This consideration is supported by experimental results on a very large test set including many speakers uttering proper names from different locations of the Italian public telephone network. The results show that soft thresholding outperforms J-Rasta PLP with a WER reduction, after denoising, of 26%.
Integrating Word Embedding O ﬀ sets into Espresso System for Part-Whole Relation Extraction

: Part-whole relation, or meronymy plays an important role in many domains. Among approaches to addressing part-whole relation extraction problem, the Espresso bootstrapping algorithm has proved to be e ﬀ ective by signiﬁcantly improving recall while keeping high precision. It is a popular bootstrapping-based method that uses a set of seed instances to induce patterns for the target relation and then acquire new instances in an iterative bootstrapping process. In this paper, we ﬁrst investigate the e ﬀ ect of using ﬁne-grained subtypes and careful seed sets on the performance of extracting part-whole relation. Then, we improve the Espresso bootstrapping algorithm for part-whole relation extraction problem by integrating word embedding approach into its iterations. The key idea of our approach is utilizing an additional ranker component, namely Similarity Ranker in the Instances Extraction phase of the Espresso system. This ranker component uses embedding o ﬀ set information between instance pairs of part-whole relation. The experiments show that our proposed system achieved a precision of 84.9% for harvesting instances of part-whole relation, and outperformed the original Espresso system.
Investigating Algorithmic Misconceptions in a Media Context: Source of a New Digital Divide?

Algorithms are widely used in our data-driven media landscape. Many misconceptions have arisen about how these algorithms work and what they can do. In this study, we conducted a large representative survey (N = 2,106) in the Netherlands to explore algorithmic misconceptions. Results showed that a significant part of the general population holds (multiple) misconceptions about algorithms in the media. We found that erroneous beliefs about algorithms are more common among (1) older people (vs. younger people), (2) lower-educated people (vs. higher-educated), and (3) women (vs. men). In addition, it was found that people who had no specific sources to inform themselves about algorithms, and those relying on their friends/family for information, were more likely to have algorithmic misconceptions. Conversely, media channels, school, and having one’s own (online) experiences were found to be sources associated with having fewer algorithmic misconceptions. Theoretical implications are formulated in the context of algorithmic awareness and the digital divide. Finally, societal implications are discussed, such as the need for algorithmic literacy initiatives.
MSR Video to Language Challenge

Overview. In this paper, we propose a novel Two-Stream Fusion (denoted as “TSF”) video caption model for the 2 Microsoft Research Video to Text (MSR-VTT) Challenge [3, 4]. The core contribution of this model is to jointly discover and integrate the dynamics of both visual (i.e., Resnet101 features [2]) and semantic (i.e, high-level attributes) streams for video captioning. TSF. We present the overview of the proposed TSF model in Figure 1. Intuitively, a video can be complementarily represented by visual and semantic descriptors. The visual descriptor encodes the appearance information depicted in each frame, while the semantic descriptor encodes each video frame with high-level representation of semantic attributes (i.e., objects (nouns), motions (verbs) and properties (adjectives)). Given these two complementary information, our model first considers each modality as a unique stream, and uses a two-stream network to individually encode the dynamics of each modality. Particularly, we utilize the attention-LSTM unit [5] to enhance the individual feature learning. Then, a “combine unit” is deployed to linearly perform two-stream dynamic fusion for sentence generation. The softmax layer is deployed to get the probability distribution over the words. Feature Extraction. We selected 50 equally-spaced frames out of each video. For the visual features, we used a pretrained Resnet101 model [2] to obtain 2,048 dimensional frame-wise visual features, which were extracted from the ‘pool5’ layer. For the semantic features, we used a set of attributes to represent the visual content in each frame. Particularly, we used MSCOCO as the extended dataset and selected 256 most frequent words from the training captions as the high-level attributes. Then, we associated each MSCOCO image with a set of attributes according to its captions. The attribute detectors were trained with binary SVM [1]. Finally, the SVM [1] predictions were aggregated as a 256way vector and used as frame-wise semantic representation. Implementation. We randomly select 9,000 videos as training set and 1,000 videos as validation set. Each word in the sentence was represented as a “one-hot” vector. The word
UC Merced Proceedings of the Annual Meeting of the Cognitive Science Society

This is the first study to measure semantic guidance during scene inspection, based on the efforts by two other research groups, namely the development of the LabelMe object-annotated image database and the LSA@CU text/word latent semantic analysis tool, which computes the conceptual distance between two terms. Our analysis reveals the existence of semantic guidance during scene inspection, that is, eye movements during scene inspection being guided by a semantic factor reflecting the conceptual relation between the currently fixated object and the target object of the following saccade. This guidance may facilitate memorization of the scene for later recall by viewing semantically related objects consecutively.
Do Batch and User Evaluations Give the Same Results? An Analysis from the TREC-8 Interactive Track

An unanswered question in information retrieval research is whether improvements in system performance demonstrated by batch evaluations confer the same benefit for real users. We used the TREC-8 Interactive Track to investigate this question. After identifying a weighting scheme that gave maximum improvement over the baseline, we used it with real users searching on an instance recall task. Our results showed no improvement; although there was overall average improvement comparable to the batch results, it was not statistically significant and due to the effect of just one out of the six queries. Further analysis with more queries is necessary to resolve this question.