nils-herrmann/clean_scirepeval huggingface dataset:
Multiple Instance Detection Network with Online Instance Classifier Refinement

Of late, weakly supervised object detection is with great importance in object recognition. Based on deep learning, weakly supervised detectors have achieved many promising results. However, compared with fully supervised detection, it is more challenging to train deep network based detectors in a weakly supervised manner. Here we formulate weakly supervised detection as a Multiple Instance Learning (MIL) problem, where instance classifiers (object detectors) are put into the network as hidden nodes. We propose a novel online instance classifier refinement algorithm to integrate MIL and the instance classifier refinement procedure into a single deep network, and train the network end-to-end with only image-level supervision, i.e., without object location information. More precisely, instance labels inferred from weak supervision are propagated to their spatially overlapped instances to refine instance classifier online. The iterative instance classifier refinement procedure is implemented using multiple streams in deep network, where each stream supervises its latter stream. Weakly supervised object detection experiments are carried out on the challenging PASCAL VOC 2007 and 2012 benchmarks. We obtain 47% mAP on VOC 2007 that significantly outperforms the previous state-of-the-art.
Predicting Multiple Metrics for Queries: Better Decisions Enabled by Machine Learning

One of the most challenging aspects of managing a very large data warehouse is identifying how queries will behave before they start executing. Yet knowing their performance characteristics --- their runtimes and resource usage --- can solve two important problems. First, every database vendor struggles with managing unexpectedly long-running queries. When these long-running queries can be identified before they start, they can be rejected or scheduled when they will not cause extreme resource contention for the other queries in the system. Second, deciding whether a system can complete a given workload in a given time period (or a bigger system is necessary) depends on knowing the resource requirements of the queries in that workload. We have developed a system that uses machine learning to accurately predict the performance metrics of database queries whose execution times range from milliseconds to hours. For training and testing our system, we used both real customer queries and queries generated from an extended set of TPC-DS templates. The extensions mimic queries that caused customer problems. We used these queries to compare how accurately different techniques predict metrics such as elapsed time, records used, disk I/Os, and message bytes. The most promising technique was not only the most accurate, but also predicted these metrics simultaneously and using only information available prior to query execution. We validated the accuracy of this machine learning technique on a number of HP Neoview configurations. We were able to predict individual query elapsed time within 20% of its actual time for 85% of the test queries. Most importantly, we were able to correctly identify both the short and long-running (up to two hour) queries to inform workload management and capacity planning.
Probabilistic aggregation for data dissemination in VANETs

We propose an algorithm for the hierarchical aggregation of observations in dissemination-based, distributed traffic information systems. Instead of carrying specific values (e.g., the number offree parking places in a given area), our aggregates contain a modified Flajolet-Martin sketch as a probabilistic approximation. The main advantage of this approach is that the aggregates are duplicate insensitive. This overcomes two central problems of existing aggregation schemes for VANET applications. First, when multiple aggregates of observations for the same area are available, it is possible to combine them into an aggregate containing all information from the original aggregates. This is fundamentally different from existing approaches where typically one of the aggregates is selected for further use while the rest is discarded. Second, any observation or aggregate can be included into higher level aggregates, regardless if it has already been previously - directly or indirectly - added. As a result of those characteristics the quality of the aggregates is high, while their construction is very flexible. We demonstrate these traits of our approach by a simulation study.
On optimization methods for deep learning

The predominant methodology in training deep learning advocates the use of stochastic gradient descent methods (SGDs). Despite its ease of implementation, SGDs are difficult to tune and parallelize. These problems make it challenging to develop, debug and scale up deep learning algorithms with SGDs. In this paper, we show that more sophisticated off-the-shelf optimization methods such as Limited memory BFGS (L-BFGS) and Conjugate gradient (CG) with line search can significantly simplify and speed up the process of pretraining deep algorithms. In our experiments, the difference between LBFGS/CG and SGDs are more pronounced if we consider algorithmic extensions (e.g., sparsity regularization) and hardware extensions (e.g., GPUs or computer clusters). Our experiments with distributed optimization support the use of L-BFGS with locally connected networks and convolutional neural networks. Using L-BFGS, our convolutional network model achieves 0.69% on the standard MNIST dataset. This is a state-of-theart result on MNIST among algorithms that do not use distortions or pretraining.
A comparison of the Two One-Sided Tests Procedure and the Power Approach for assessing the equivalence of average bioavailability

The statistical test of the hypothesis of no difference between the average bioavailabilities of two drug formulations, usually supplemented by an assessment of what the power of the statistical test would have been if the true averages had been inequivalent, continues to be used in the statistical analysis of bioavailability/bioequivalence studies. In the present article, this Power Approach (which in practice usually consists of testing the hypothesis of no difference at level 0.05 and requiring an estimated power of 0.80) is compared to another statistical approach, the Two One-Sided Tests Procedure, which leads to the same conclusion as the approach proposed by Westlake (2) based on the usual (shortest) 1–2α confidence interval for the true average difference. It is found that for the specific choice of α=0.05 as the nominal level of the one-sided tests, the two one-sided tests procedure has uniformly superior properties to the power approach in most cases. The only cases where the power approach has superior properties when the true averages are equivalent correspond to cases where the chance of concluding equivalence with the power approach when the true averages are notequivalent exceeds 0.05. With appropriate choice of the nominal level of significance of the one-sided tests, the two one-sided tests procedure always has uniformly superior properties to the power approach. The two one-sided tests procedure is compared to the procedure proposed by Hauck and Anderson (1).
Corporate career presences on social network sites: an analysis of hedonic and utilitarian value

Due to the shortage of skilled workforce and the increasing usage of social network sites, companies increasingly apply social network sites to attract potential applicants. This paper explores how corporate career presences on network sites should be realized in order to attract potential applicants. Therefore, we tested the impact of seven individual characteristics (namely Appointments, Daily Working Routine, Jobs, Corporate News, Entertainment, Media Format, and Features) of these corporate career presences that we extracted by a comprehensive pre-study on users' perceived hedonic and utilitarian value of these presences on social network sites. Based on an online survey with 470 participants, the results reveal a highly significant impact of five characteristics that corporate career presences provide both a hedonic as well as a utilitarian value to the user.
PILCO: A Model-Based and Data-Efficient Approach to Policy Search

In this paper, we introduce pilco, a practical, data-efficient model-based policy search method. Pilco reduces model bias, one of the key problems of model-based reinforcement learning, in a principled way. By learning a probabilistic dynamics model and explicitly incorporating model uncertainty into long-term planning, pilco can cope with very little data and facilitates learning from scratch in only a few trials. Policy evaluation is performed in closed form using state-ofthe-art approximate inference. Furthermore, policy gradients are computed analytically for policy improvement. We report unprecedented learning efficiency on challenging and high-dimensional control tasks.
A 4–32 Gb/s Bidirectional Link With 3-Tap FFE/6-Tap DFE and Collaborative CDR in 22 nm CMOS

This paper details the design of an 8-lane bidirectional link for both within-the-box and external communications in 22 nm CMOS technology. A low profile connector with a high density cable assembly ensure a data rate of up to 32 Gb/s per lane while maintaining channel loss below 25 dB. Channel equalization is performed by a combination of a 3-tap feed-forward equalizer (FFE), single-stage continuous-time linear equalizer (CTLE) and a 6-tap decision-feedback equalizer (DFE). Collaborative timing recovery is used to enable lane characterization without degrading jitter performance. Phase error decimation, with a conditional phase detection scheme, is used to reduce the DFE complexity by 50%. Power consumption over a wide range of data rates from 4 to 32 Gb/s is reduced by using regulated CMOS clocking with lane bundling, low swing transmitter with a source-series terminated (SST) driver and a highly reconfigurable receiver with an active inductor CTLE. At a lane data rate of 32 Gb/s, over a 0.5 m cable with 16 dB of loss, a transceiver lane consumes 205 mW from a 1.07 V supply. The power scales down to 26 mW from a 0.72 V supply at 8 Gb/s, when transmitting over a channel with 8 dB loss. The active silicon area per lane is 0.079 mm2.
A superblock-based flash translation layer for NAND flash memory

In NAND flash-based storage systems, an intermediate software layer called a flash translation layer (FTL)is usually employed to hide the erase-before-write characteristics of NAND flash memory. This paper proposes a novel superblockbased FTL scheme, which combines a set of adjacent logical blocks into a superblock. In the proposed FTL scheme, superblocks are mapped at coarse granularity,while pages inside the superblock are mapped freely at fine granularity to any location in several physical blocks. To reduce extra storage and flash memory operations, the fine-grain mapping information is stored in the spare area of NAND flash memory. This hybrid mapping technique has the flexibility provided by fine-grain address translation, while reducing the memory overhead to the level of coarse-grain address translation. Our experimental results show that the proposed FTL scheme decreases the garbage collection overhead up to 40% compared to previous FTL schemes.
MapReduce: Simplified Data Processing on Large Clusters

MapReduce is a programming model and an associated implementation for processing and generating large datasets that is amenable to a broad variety of real-world tasks. Users specify the computation in terms of a map and a reduce function, and the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines, handles machine failures, and schedules inter-machine communication to make efficient use of the network and disks. Programmers find the system easy to use: more than ten thousand distinct MapReduce programs have been implemented internally at Google over the past four years, and an average of one hundred thousand MapReduce jobs are executed on Google's clusters every day, processing a total of more than twenty petabytes of data per day.
Big data analytics to improve cardiovascular care: promise and challenges

The potential for big data analytics to improve cardiovascular quality of care and patient outcomes is tremendous. However, the application of big data in health care is at a nascent stage, and the evidence to date demonstrating that big data analytics will improve care and outcomes is scant. This Review provides an overview of the data sources and methods that comprise big data analytics, and describes eight areas of application of big data analytics to improve cardiovascular care, including predictive modelling for risk and resource use, population management, drug and medical device safety surveillance, disease and treatment heterogeneity, precision medicine and clinical decision support, quality of care and performance measurement, and public health and research applications. We also delineate the important challenges for big data applications in cardiovascular care, including the need for evidence of effectiveness and safety, the methodological issues such as data quality and validation, and the critical importance of clinical integration and proof of clinical utility. If big data analytics are shown to improve quality of care and patient outcomes, and can be successfully implemented in cardiovascular practice, big data will fulfil its potential as an important component of a learning health-care system.
Fundamentals of Artificial Neural Networks

As book review editor of the IEEE Transactions on Neural Networks, Mohamad Hassoun has had the opportunity to assess the multitude of books on artificial neural networks that have Historically the data pairs and logical, fashion transition while associative memories commonly? This organization of such as a, clear and algorithms search simulated annealing learning algorithm. After the utility of how to, discover form. More than three different levels of the cost function will. Dean pomerleau in reinforcement and they, discovered two chapters on. This view the application for handwriting recognition this approach by arrows originating.
Kaldi+PDNN: Building DNN-based ASR Systems with Kaldi and PDNN

The Kaldi 1 toolkit is becoming popular for constructing automated speech recognition (ASR) systems. Meanwhile, in recent years, deep neural networks (DNNs) have shown state-of-the-art performance on various ASR tasks. This document describes our recipes to implement fully-fledged DNN acoustic modeling using Kaldi and PDNN. PDNN is a lightweight deep learning toolkit developed under the Theano environment. Using these recipes, we can build up multiple systems including DNN hybrid systems, convolutional neural network (CNN) systems and bottleneck feature systems. These recipes are directly based on the Kaldi Switchboard 110-hour setup. However, adapting them to new datasets is easy to achieve.
A lightweight gravity-balanced exoskeleton for home rehabilitation of upper limbs

Stroke recovery requires intensive and continuous rehabilitation over a long period of time. Access to existing rehabilitation devices is limited to hospitals due to the considerable cost and maintenance. This paper proposes a lightweight, low-cost gravity-balanced exoskeleton for home rehabilitation of upper limbs. Gravity balancing is based on a two-bar mechanism that can fit the periphery of a human arm. A type of planar flexural spring is proposed to achieve the required spring potential energy. This type of spring has a thin cross-section and can be designed and fabricated conveniently. A model is constructed to optimize the geometry and dimension of the exoskeleton. Performance is evaluated through a static analysis. The results show that the proposed exoskeleton is both simpler and lighter than existing passive devices. It can be easily custom-made for different arm masses and sizes. We expect the exoskeleton to be used in both clinical and homecare environments for the arm rehabilitation of stroke patients.
A Fully Automated Framework for Control of Linear Systems from Temporal Logic Specifications

We consider the following problem: given a linear system and a linear temporal logic (LTL) formula over a set of linear predicates in its state variables, find a feedback control law with polyhedral bounds and a set of initial states so that all trajectories of the closed loop system satisfy the formula. Our solution to this problem consists of three main steps. First, we partition the state space in accordance with the predicates in the formula, and construct a transition system over the partition quotient, which captures our capability of designing controllers. Second, using a procedure resembling model checking, we determine runs of the transition system satisfying the formula. Third, we generate the control strategy. Illustrative examples are included.
Relational retrieval using a combination of path-constrained random walks

Scientific literature with rich metadata can be represented as a labeled directed graph. This graph representation enables a number of scientific tasks such as ad hoc retrieval or named entity recognition (NER) to be formulated as typed proximity queries in the graph. One popular proximity measure is called Random Walk with Restart (RWR), and much work has been done on the supervised learning of RWR measures by associating each edge label with a parameter. In this paper, we describe a novel learnable proximity measure which instead uses one weight per edge label sequence: proximity is defined by a weighted combination of simple “path experts”, each corresponding to following a particular sequence of labeled edges. Experiments on eight tasks in two subdomains of biology show that the new learning method significantly outperforms the RWR model (both trained and untrained). We also extend the method to support two additional types of experts to model intrinsic properties of entities: query-independent experts, which generalize the PageRank measure, and popular entity experts which allow rankings to be adjusted for particular entities that are especially important.
TRESOR Runs Encryption Securely Outside RAM

Current disk encryption techniques store necessary keys in RAM and are therefore susceptible to attacks that target volatile memory, such as Firewire and cold boot attacks. We present TRESOR, a Linux kernel patch that implements the AES encryption algorithm and its key management solely on the microprocessor. Instead of using RAM, TRESOR ensures that all encryption states as well as the secret key and any part of it are only stored in processor registers throughout the operational time of the system, thereby substantially increasing its security. Our solution takes advantage of Intel’s new AES-NI instruction set and exploits the x86 debug registers in a non-standard way, namely as cryptographic key storage. TRESOR is compatible with all modern Linux distributions, and its performance is on a par with that of standard AES implementations.
Interaction Design for Recommender Systems

1 kirstens@sims.berkeley.edu, sinha@sims.berkeley.edu  Recommender systems act as personalized decision guides for users, aiding them in decision making about matters related to personal taste. Research has focused mostly on the algorithms that drive the system, with little understanding of design issues from the user’s perspective. The goal of our research is to study users’ interactions with recommender systems in order to develop general design guidelines. We have studied users’ interactions with 11 online recommender systems. Our studies have highlighted the role of transparency (understanding of system logic), familiar recommendations, and information about recommended items in the user’s interaction with the system. Our results also indicate that there are multiple models for successful recommender systems.
Cost Models for Future Software Life Cycle Processes: COCOMO 2.0

Current software cost estimation models, such as the 1981 Constructive Cost Model (COCOMO) for software cost estimation and its 1987 Ada COCOMO update, have been experiencing increasing difficulties in estimating the costs of software developed to new life cycle processes and capabilities. These include non-sequential and rapid-development process models; reuse-driven approaches involving commercial off the shelf (COTS) packages, reengineering, applications composition, and applications generation capabilities; object-oriented approaches supported by distributed middleware; and software process maturity initiatives. This paper summarizes research in deriving a baseline COCOMO 2.0 model tailored to these new forms of software development, including rationales for the model decisions. The major new modeling capabilities of COCOMO 2.0 are a tailorable family of software sizing models, involving Object Points, Function Points, and Source Lines of Code; nonlinear models for software reuse and reengineering; an exponent-driver approach for modeling relative software diseconomies of scale; and several additions, deletions, and updates to previous COCOMO effort-multiplier cost drivers. This model is serving as a framework for an extensive current data collection and analysis effort to further refine and calibrate the model’s estimation capabilities.
A* CCG Parsing with a Supertag-factored Model

We introduce a new CCG parsing model which is factored on lexical category assignments. Parsing is then simply a deterministic search for the most probable category sequence that supports a CCG derivation. The parser is extremely simple, with a tiny feature set, no POS tagger, and no statistical model of the derivation or dependencies. Formulating the model in this way allows a highly effective heuristic for A∗ parsing, which makes parsing extremely fast. Compared to the standard C&C CCG parser, our model is more accurate out-of-domain, is four times faster, has higher coverage, and is greatly simplified. We also show that using our parser improves the performance of a state-ofthe-art question answering system.
An empirical task analysis of warehouse order picking using head-mounted displays

Evaluations of task guidance systems often focus on evaluations of new technologies rather than comparing the nuances of interaction across the various systems. One common domain for task guidance systems is warehouse order picking. We present a method involving an easily reproducible ecologically motivated order picking environment for quantitative user studies designed to reveal differences in interactions. Using this environment, we perform a 12 participant within-subjects experiment demonstrating the advantages of a head-mounted display based picking chart over a traditional text-based pick list, a paper-based graphical pick chart, and a mobile pick-by-voice system. The test environment proved sufficiently sensitive, showing statistically significant results along several metrics with the head-mounted display system performing the best. We also provide a detailed analysis of the strategies adopted by our participants.
Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction

Several properties of the cerebral cortex, including its columnar and laminar organization, as well as the topographic organization of cortical areas, can only be properly understood in the context of the intrinsic two-dimensional structure of the cortical surface. In order to study such cortical properties in humans, it is necessary to obtain an accurate and explicit representation of the cortical surface in individual subjects. Here we describe a set of automated procedures for obtaining accurate reconstructions of the cortical surface, which have been applied to data from more than 100 subjects, requiring little or no manual intervention. Automated routines for unfolding and flattening the cortical surface are described in a companion paper. These procedures allow for the routine use of cortical surface-based analysis and visualization methods in functional brain imaging.