nils-herrmann/clean_scirepeval huggingface dataset:
Highly-Integrated Quad-Channel Transimpedance Amplifier for Next Generation Coherent Optical Receiver

This paper presents a highly integrated, high performance four channel linear transimpedance amplifier (TIA) RFIC with a footprint of 2mmx3.5mm towards next generation 100G/400G miniaturized coherent receivers. A TIA of such form may become indispensable as the size, complexity and cost of receivers continue to reduce. The design has been realized in a 130nm SiGe BiCMOS process for a low cost, high performance solution towards long- haul/metro applications. The TIA is capable of providing control functions either digitally through an on-chip 4-wire serial-peripheral interface (SPI) or in analog mode. Analog mode is provided as an alternative control for real-time control and monitoring. To provide high input dynamic range, a variable gain control block is integrated for each channel, which can be used in automatic or manual mode. The TIA has a differential input, differential output configuration that exhibits state-of-the-art THD of <;0.9% up to 500mVpp output voltage swing for input currents up to 2mApp and high isolation > 40dB between adjacent channels. A high transimpedance gain (Zt) up to ~7KΩ with a large dynamic range up to 37dB and variable bandwidth up to 34GHz together with low average input noise density of 20pA/√Hz has been achieved. To the authors' knowledge, these metrics combined with diverse functionality and high integration have not been exhibited so far. This paper intends to report a state-of-the-art high-baud rate TIA and provide insight into possibilities for further integration.
Survey of Research on Software Clones

This report summarizes my overview talk on software clone detection research. It first discusses the notion of software redundancy, cloning, duplication, and similarity. Then, it describes various categorizations of clone types, empirical studies on the root causes for cloning, current opinions and wisdom of consequences of cloning, empirical studies on the evolution of clones, ways to remove, to avoid, and to detect them, empirical evaluations of existing automatic clone detector performance (such as recall, precision, time and space consumption) and their fitness for a particular purpose, benchmarks for clone detector evaluations, presentation issues, and last but not least application of clone detection in other related fields. After each summary of a subarea, I am listing open research questions.
The costs and benefits of writing, talking, and thinking about life's triumphs and defeats.

Three studies considered the consequences of writing, talking, and thinking about significant events. In Studies 1 and 2, students wrote, talked into a tape recorder, or thought privately about their worst (N = 96) or happiest experience (N = 111) for 15 min each during 3 consecutive days. In Study 3 (N = 112), students wrote or thought about their happiest day; half systematically analyzed, and half repetitively replayed this day. Well-being and health measures were administered before each study's manipulation and 4 weeks after. As predicted, in Study 1, participants who processed a negative experience through writing or talking reported improved life satisfaction and enhanced mental and physical health relative to those who thought about it. The reverse effect for life satisfaction was observed in Study 2, which focused on positive experiences. Study 3 examined possible mechanisms underlying these effects. Students who wrote about their happiest moments--especially when analyzing them--experienced reduced well-being and physical health relative to those who replayed these moments. Results are discussed in light of current understanding of the effects of processing life events.
A Convolutional Neural Network for Fault Classification and Diagnosis in Semiconductor Manufacturing Processes

Many studies on the prediction of manufacturing results using sensor signals have been conducted in the field of fault detection and classification (FDC) for semiconductor manufacturing processes. However, fault diagnosis used to find clues as to root causes remains a challenging area. In particular, process monitoring using neural networks has been employed to only a limited extent because it is a black box model, making the relationships between input data and output results difficult to interpret in actual manufacturing settings, despite its high classification performance. In this paper, we propose a convolutional neural network (CNN) model, named FDC-CNN, in which a receptive field tailored to multivariate sensor signals slides along the time axis, to extract fault features. This approach enables the association of the output of the first convolutional layer with the structural meaning of the raw data, making it possible to locate the variable and time information that represents process faults. In an experiment on a chemical vapor deposition process, the proposed method outperformed other deep learning models.
Semi-supervised Spectral Clustering for Image Set Classification

We present an image set classification algorithm based on unsupervised clustering of labeled training and unlabeled test data where labels are only used in the stopping criterion. The probability distribution of each class over the set of clusters is used to define a true set based similarity measure. To this end, we propose an iterative sparse spectral clustering algorithm. In each iteration, a proximity matrix is efficiently recomputed to better represent the local subspace structure. Initial clusters capture the global data structure and finer clusters at the later stages capture the subtle class differences not visible at the global scale. Image sets are compactly represented with multiple Grassmannian manifolds which are subsequently embedded in Euclidean space with the proposed spectral clustering algorithm. We also propose an efficient eigenvector solver which not only reduces the computational cost of spectral clustering by many folds but also improves the clustering quality and final classification results. Experiments on five standard datasets and comparison with seven existing techniques show the efficacy of our algorithm.
Semi-supervised Vocabulary-Informed Learning

Despite significant progress in object categorization, in recent years, a number of important challenges remain, mainly, ability to learn from limited labeled data and ability to recognize object classes within large, potentially open, set of labels. Zero-shot learning is one way of addressing these challenges, but it has only been shown to work with limited sized class vocabularies and typically requires separation between supervised and unsupervised classes, allowing former to inform the latter but not vice versa. We propose the notion of semi-supervised vocabulary-informed learning to alleviate the above mentioned challenges and address problems of supervised, zero-shot and open set recognition using a unified framework. Specifically, we propose a maximum margin framework for semantic manifold-based recognition that incorporates distance constraints from (both supervised and unsupervised) vocabulary atoms, ensuring that labeled samples are projected closest to their correct prototypes, in the embedding space, than to others. We show that resulting model shows improvements in supervised, zero-shot, and large open set recognition, with up to 310K class vocabulary on AwA and ImageNet datasets.
Approximate continuous querying over distributed streams

While traditional database systems optimize for performance on one-shot query processing, emerging large-scale monitoring applications require continuous tracking of complex data-analysis queries over collections of physically distributed streams. Thus, effective solutions have to be simultaneously space/time efficient (at each remote monitor site), communication efficient (across the underlying communication network), and provide continuous, guaranteed-quality approximate query answers. In this paper, we propose novel algorithmic solutions for the problem of continuously tracking a broad class of complex aggregate queries in such a distributed-streams setting. Our tracking schemes maintain approximate query answers with provable error guarantees, while simultaneously optimizing the storage space and processing time at each remote site, and the communication cost across the network. In a nutshell, our algorithms rely on tracking general-purpose randomized sketch summaries of local streams at remote sites along with concise prediction models of local site behavior in order to produce highly communication- and space/time-efficient solutions. The end result is a powerful approximate query tracking framework that readily incorporates several complex analysis queries (including distributed join and multi-join aggregates, and approximate wavelet representations), thus giving the first known low-overhead tracking solution for such queries in the distributed-streams model. Experiments with real data validate our approach, revealing significant savings over naive solutions as well as our analytical worst-case guarantees.
The CHAMPS system: change management with planning and scheduling

Change management is a process by which IT systems are modified to accommodate considerations such as software fixes, hardware upgrades and performance enhancements. This paper discusses the CHAMPS system, a prototype under development at IBM Research for Change Management with Planning and Scheduling. The CHAMPS system is able to achieve a very high degree of parallelism for a set of tasks by exploiting detailed factual knowledge about the structure of a distributed system from dependency information at runtime. In contrast, today's systems expect an administrator to provide such insights, which is often not the case. Furthermore, the optimization techniques we employ allow the CHAMPS system to come up with a very high quality solution for a mathematically intractable problem in a time which scales nicely with the problem size. We have implemented the CHAMPS system and have applied it in a TPC-W environment that implements an on-line book store application.
Recurrent neural network based language model

A new recurrent neural network based language model (RNN LM) with applications to speech recognition is presented. Results indicate that it is possible to obtain around 50% reduction of perplexity by using mixture of several RNN LMs, compared to a state of the art backoff language model. Speech recognition experiments show around 18% reduction of word error rate on the Wall Street Journal task when comparing models trained on the same amount of data, and around 5% on the much harder NIST RT05 task, even when the backoff model is trained on much more data than the RNN LM. We provide ample empirical evidence to suggest that connectionist language models are superior to standard n-gram techniques, except their high computational (training) complexity.
A Novel Bayesian Similarity Measure for Recommender Systems

Collaborative filtering, a widely-used user-centric recommendation technique, predicts an item’s rating by aggregating its ratings from similar users. User similarity is usually calculated by cosine similarity or Pearson correlation coefficient. However, both of them consider only the direction of rating vectors, and suffer from a range of drawbacks. To solve these issues, we propose a novel Bayesian similarity measure based on the Dirichlet distribution, taking into consideration both the direction and length of rating vectors. Further, our principled method reduces correlation due to chance. Experimental results on six real-world data sets show that our method achieves superior accuracy.
The Viable Governance Model - A Theoretical Model for the Governance of IT

The academic and professional literature offer many different definitions and models of IT governance (ITG). Considerable advancements have been made in identifying the components and mechanisms of ITG. However, much of the research to date has followed an empirical approach, using case studies to examine how contemporary organizations are implementing effective governance arrangements. This paper seeks to propose a theoretical model of the corporate governance of IT using the principles of cybernetics as embodied in Stafford Beer's Viable System Model (VSM) As this paper is primarily concerned with corporate governance of IT, only System 5 of the VSM is examined in detail.
Context-aware music recommendation based on latenttopic sequential patterns

Contextual factors can greatly influence the users' preferences in listening to music. Although it is hard to capture these factors directly, it is possible to see their effects on the sequence of songs liked by the user in his/her current interaction with the system. In this paper, we present a context-aware music recommender system which infers contextual information based on the most recent sequence of songs liked by the user. Our approach mines the top frequent tags for songs from social tagging Web sites and uses topic modeling to determine a set of latent topics for each song, representing different contexts. Using a database of human-compiled playlists, each playlist is mapped into a sequence of topics and frequent sequential patterns are discovered among these topics. These patterns represent frequent sequences of transitions between the latent topics representing contexts. Given a sequence of songs in a user's current interaction, the discovered patterns are used to predict the next topic in the playlist. The predicted topics are then used to post-filter the initial ranking produced by a traditional recommendation algorithm. Our experimental evaluation suggests that our system can help produce better recommendations in comparison to a conventional recommender system based on collaborative or content-based filtering. Furthermore, the topic modeling approach proposed here is also useful in providing better insight into the underlying reasons for song selection and in applications such as playlist construction and context prediction.
Inventive progress measured by multI-stage patent citation analysis

Studies of technological change constitute a field of growing importance and sophistication. In this paper we contribute to the discussion with a methodological reflection and application of multi-stage patent citation analysis for the mea surement of inventive progress. Investigating specific patterns of patent citation data, we conclude that single-stage citation analysis cannot reveal technological paths or linea ges. Therefore, one should also make use of indirect citations and bibliographical coupling. To measure aspects of cumulative inventive progress, we develop a “shared specialization measu r ” of patent families. We relate this measure to an expert rating of the technological va lue dded in the field of variable valve actuation for internal combustion engines. In sum, the study presents promising evidence for multi-stage patent citation analysis in order to ex plain aspects of technological change.
Revision of Neoclassical Facial Canons in Young Adult Afro-Americans

The validity of seven neoclassical facial canons was tested in 100 young, adult, Afro-American (A-A) males and females, and the results were compared with 103 North American Caucasians, producing the first reliable information about the differences of facial proportions in the two populations. Although the canons should not be regarded as strict directives in reconstructive and esthetic surgery, the data obtained from the study offered the first information about the facial proportion qualities of Afro-Americans in relation to the white population. In the absence of a detailed facial proportion study in A-As, the findings in canons can assist in planning the surgery. Comparison of the two populations revealed that the three sections of the facial profile were not equal in either population. In an A-A sample it showed the prevalence of the long lower face height also in relation to the height of the forehead. In horizontal canons of the orbital regions, the similarity between the two populations was demonstrated by the great frequency of intercanthal spaces to be wider than the length of the palpebral fissures . The facial canons, including the nose width, relatively wider  in A-A than in white subjects, prompted the difference between A-A and Caucasians in frequency and degree in the dominant canon variations. The greater inclination of the nasal bridge than that of the medial longitudinal axis of the ear was a very frequent canon variation in both populations. Generally, the frequency of valid canons was greatly surpassed by their variations.
A Framework for High-Level Feedback to Adaptive, Per-Pixel, Mixture-of-Gaussian Background Models

May 28-31, 2002, Copenhagen, Denmark. Personal use of this material is permitted. Permission to reprint/republish for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from Springer-Verlag. Time-Adaptive, Per-Pixel Mixtures Of Gaussians (TAPPMOGs) have recently become a popular choice for robust modeling and removal of complex and changing backgrounds at the pixel level. However, TAPPMOG-based methods cannot easily be made to model dynamic backgrounds with highly complex appearance, or to adapt promptly to sudden “uninteresting” scene changes such as the re-positioning of a static object or the turning on of a light, without further undermining their ability to segment foreground objects, such as people, where they occlude the background for too long. To alleviate tradeoffs such as these, and, more broadly, to allow TAPPMOG segmentation results to be tailored to the specific needs of an application, we introduce a general framework for guiding pixel-level TAPPMOG evolution with feedback from “high-level” modules. Each such module can use pixel-wise maps of positive and negative feedback to attempt to impress upon the TAPPMOG some definition of foreground that is best expressed through “higher-level” primitives such as image region properties or semantics of objects and events. By pooling the foreground error corrections of many high-level modules into a shared, pixel-level TAPPMOG model in this way, we improve the quality of the foreground segmentation and the performance of all modules that make use of it. We show an example of using this framework with a TAPPMOG method and high-level modules that all rely on dense depth data from a stereo camera.
Near-optimal dynamic trajectory generation and control of an omnidirectional vehicle

This paper describes a computationally inexpensive, yet high performance trajectory generation algorithm for omnidirectional vehicles. It is shown that the associated nonlinear control problem can be made tractable by restricting the set of admissible control functions. The resulting problem is linear with coupled control efforts and a near-optimal control strategy is shown to be piecewise constant (bang-bang type). A very favorable trade-off between optimality and computational efficiency is achieved. The proposed algorithm is based on a small number of evaluations of simple closed-form expressions and is thus extremely efficient. The low computational cost makes this method ideal for path planning in dynamic environments.
Semantic Segmentation with Millions of Features: Integrating Multiple Cues in a Combined Random Forest Approach

In this paper, we present a new combined approach for feature extraction, classification, and context modeling in an iterative framework based on random decision trees and a huge amount of features. A major focus of this paper is to integrate different kinds of feature types like color, geometric context, and auto context features in a joint, flexible and fast manner. Furthermore, we perform an in-depth analysis of multiple feature extraction methods and different feature types. Extensive experiments are performed on challenging facade recognition datasets, where we show that our approach significantly outperforms previous approaches with a performance gain of more than 15% on the most difficult dataset.
Microservices: Yesterday, Today, and Tomorrow

Microservices is an architectural style inspired by service-oriented computing that has recently started gaining popularity. Before presenting the current state-of-the-art in the field, this chapter reviews the history of software architecture, the reasons that led to the diffusion of objects and services first, and microservices later. Finally, open problems and future challenges are introduced. This survey primarily addresses newcomers to the discipline, while offering an academic viewpoint on the topic. In addition, we investigate some practical issues and point out some potential solutions.
Budget Optimization for Sponsored Search: Censored Learning in MDPs

We consider the budget optimization problem faced by an advertiser participating in repeated sponsored search auctions, seeking to maximize the number of clicks attained under that budget. We cast the budget optimization problem as a Markov Decision Process (MDP) with censored observations, and propose a learning algorithm based on the wellknown Kaplan-Meier or product-limit estimator. We validate the performance of this algorithm by comparing it to several others on a large set of search auction data from Microsoft adCenter, demonstrating fast convergence to optimal performance.
FRAUD DETECTION AND MANAGEMENT IN MOBILE TELECOMMUNICATIONS NETWORKS

This paper discusses the status of research on detection of fraud undertaken as part of the European Commission-funded ACTS ASPeCT (Advanced Security for Personal Communications Technologies) project. A first task has been the identification of possible fraud scenarios and of typical fraud indicators which can be mapped to data in Toll Tickets. Currently, the project is exploring the detection of fraudulent behaviour based on a combination of absolute and differential usage. Three approaches are being investigated: a rule-based approach and two approaches based on neural networks, where both supervised and unsupervised learning are considered. Special attention is being paid to the feasibility of the implementations.
EVEX: A PubMed-Scale Resource for Homology-Based Generalization of Text Mining Predictions

In comparative genomics, functional annotations are transferred from one organism to another relying on sequence similarity. With more than 20 million citations in PubMed, text mining provides the ideal tool for generating additional large-scale homology-based predictions. To this end, we have refined a recent dataset of biomolecular events extracted from text, and integrated these predictions with records from public gene databases. Accounting for lexical variation of gene symbols, we have implemented a disambiguation algorithm that uniquely links the arguments of 11.2 million biomolecular events to well-defined gene families, providing interesting opportunities for query expansion and hypothesis generation. The resulting MySQL database, including all 19.2 million original events as well as their homology-based variants, is publicly available at http://bionlp.utu.fi/.
Label Partitioning For Sublinear Ranking

We consider the case of ranking a very large set of labels, items, or documents, which is common to information retrieval, recommendation, and large-scale annotation tasks. We present a general approach for converting an algorithm which has linear time in the size of the set to a sublinear one via label partitioning. Our method consists of learning an input partition and a label assignment to each partition of the space such that precision at k is optimized, which is the loss function of interest in this setting. Experiments on large-scale ranking and recommendation tasks show that our method not only makes the original linear time algorithm computationally tractable, but can also improve its performance.