nils-herrmann/clean_scirepeval huggingface dataset:
A Unified Framework for Multimodal Domain Adaptation

Domain adaptation aims to train a model on labeled data from a source domain while minimizing test error on a target domain. Most of existing domain adaptation methods only focus on reducing domain shift of single-modal data. In this paper, we consider a new problem of multimodal domain adaptation and propose a unified framework to solve it. The proposed multimodal domain adaptation neural networks(MDANN) consist of three important modules. (1) A covariant multimodal attention is designed to learn a common feature representation for multiple modalities. (2) A fusion module adaptively fuses attended features of different modalities. (3) Hybrid domain constraints are proposed to comprehensively learn domain-invariant features by constraining single modal features, fused features, and attention scores. Through jointly attending and fusing under an adversarial objective, the most discriminative and domain-adaptive parts of the features are adaptively fused together. Extensive experimental results on two real-world cross-domain applications (emotion recognition and cross-media retrieval) demonstrate the effectiveness of the proposed method.
All-hex meshing using closed-form induced polycube

The polycube-based hexahedralization methods are robust to generate all-hex meshes without internal singularities. They avoid the difficulty to control the global singularity structure for a valid hexahedralization in frame-field based methods. To thoroughly utilize this advantage, we propose to use a frame field without internal singularities to guide the polycube construction. Theoretically, our method extends the vector fields associated with the polycube from exact forms to closed forms, which are curl free everywhere but may be not globally integrable. The closed forms give additional degrees of freedom to deal with the topological structure of high-genus models, and also provide better initial axis alignment for subsequent polycube generation. We demonstrate the advantages of our method on various models, ranging from genus-zero models to high-genus ones, and from single-boundary models to multiple-boundary ones.
Kill the Program Counter: Reconstructing Program Behavior in the Processor Cache Hierarchy

Data prefetching and cache replacement algorithms have been intensively studied in the design of high performance microprocessors. Typically, the data prefetcher operates in the private caches and does not interact with the replacement policy in the shared Last-Level Cache (LLC). Similarly, most replacement policies do not consider demand and prefetch requests as different types of requests. In particular, program counter (PC)-based replacement policies cannot learn from prefetch requests since the data prefetcher does not generate a PC value. PC-based policies can also be negatively affected by compiler optimizations. In this paper, we propose a holistic cache management technique called Kill-the-PC (KPC) that overcomes the weaknesses of traditional prefetching and replacement policy algorithms. KPC cache management has three novel contributions. First, a prefetcher which approximates the future use distance of prefetch requests based on its prediction confidence. Second, a simple replacement policy provides similar or better performance than current state-of-the-art PC-based prediction using global hysteresis. Third, KPC integrates prefetching and replacement policy into a whole system which is greater than the sum of its parts. Information from the prefetcher is used to improve the performance of the replacement policy and vice-versa. Finally, KPC removes the need to propagate the PC through entire on-chip cache hierarchy while providing a holistic cache management approach with better performance than state-of-the-art PC-, and non-PC-based schemes. Our evaluation shows that KPC provides 8% better performance than the best combination of existing prefetcher and replacement policy for multi-core workloads.
IEEE 802 . 11 ah : A Long Range 802 . 11 WLAN at Sub 1 GHz

IEEE 802.11ah is an emerging Wireless LAN (WLAN) standard that defines a WLAN system operating at sub 1 GHz license-exempt bands. Thanks to the favorable propagation characteristics of the low frequency spectra, 802.11ah can provide much improved transmission range compared with the conventional 802.11 WLANs operating at 2.4 GHz and 5 GHz bands. 802.11ah can be used for various purposes including large scale sensor networks, extended range hotspot, and outdoor Wi-Fi for cellular traffic offloading, whereas the available bandwidth is relatively narrow. In this paper, we give a technical overview of 802.11ah Physical (PHY) layer and Medium Access Control (MAC) layer. For the 802.11ah PHY, which is designed based on the down-clocked operation of IEEE 802.11ac’s PHY layer, we describe its channelization and transmission modes. Besides, 802.11ah MAC layer has adopted some enhancements to fulfill the expected system requirements. These enhancements include the improvement of power saving features, support of large number of stations, efficient medium access mechanisms and throughput enhancements by greater compactness of various frame formats. Through the numerical analysis, we evaluate the transmission range for indoor and outdoor environments and the theoretical throughput with newly defined channel access mechanisms.
Medical Concept Embeddings via Labeled Background Corpora

In recent years, we have seen an increasing amount of interest in low-dimensional vector representations of words. Among other things, these facilitate computing word similarity and relatedness scores. The most well-known example of algorithms to produce representations of this sort are the word2vec approaches. In this paper, we investigate a new model to induce such vector spaces for medical concepts, based on a joint objective that exploits not only word co-occurrences but also manually labeled documents, as available from sources such as PubMed. Our extensive experimental analysis shows that our embeddings lead to significantly higher correlations with human similarity and relatedness assessments than previous work. Due to the simplicity and versatility of vector representations, these findings suggest that our resource can easily be used as a drop-in replacement to improve any systems relying on medical concept similarity measures.
A decade of security research in ubiquitous computing: results of a systematic literature review

Ever since Mark Weiser coined the term “ubiquitous computing” (ubicomp) in 1988, there has been a general interest in proposing various solutions that would support his vision. However, attacks targeting devices and services of a ubicomp environment have demonstrated not only different privacy issues, but also a risk of endangering user’s life (e.g. by modifying medical sensor readings). Thus, the aim of this paper is to provide a comprehensive overview of security challenges of ubicomp environments and the corresponding countermeasures proposed over the last decade.  The results of this paper are based on a literature review method originally used in evidence-based medicine, called systematic literature review (SLR), which identifies, filters, classifies, and summarizes the findings.  Starting from the bibliometric results that clearly show an increasing interest in the topic of ubicomp security worldwide, the findings reveal specific types of attacks and vulnerabilities that have motivated the research over the last decade. This review describes most commonly proposed countermeasures context-aware access control and authentication mechanisms, cryptographic protocols that account for device’s resource constraints, privacy-preserving mechanisms, and trust mechanisms for wireless ad hoc and sensor networks.  To the best of our knowledge, this is the first SLR on security challenges in ubicomp. The findings should serve as a reference to an extensive list of scientific contributions, as well as a guiding point for the researchers novel to the security research in ubicomp.
Individualism and Collectivism in Trade Agents

Agent-Based Modeling can contribute to the understanding of international trade processes. Models for the effects of culture and cultural differences on agent behavior are required for realistic agent-based simulation of international trade. This paper makes a step toward modeling of culture in agents. The focus is one of the five dimensions of culture according to Hofstede: individualism versus collectivism. The paper presents an analysis based on social science literature about national cultures. For cultural differentiation of agent behavior, rules are formulated for individualist versus collectivist agent behavior with respect to negotiations, cooperation or defection in the delivery phase of transactions, trade partner selection, and trust. Example computations demonstrate the feasibility in multi-agent simulations.
Analysis of multistage amplifier-frequency compensation

Frequency-compensation techniques of single-, twoand three-stage amplifiers based on Miller pole splitting and pole–zero cancellation are reanalyzed. The assumptions made, transfer functions, stability criteria, bandwidths, and important design issues of most of the reported topologies are included. Several proposed methods to improve the published topologies are given. In addition, simulations and experimental results are provided to verify the analysis and to prove the effectiveness of the proposed methods.
Learning Face Hallucination in the Wild

Face hallucination method is proposed to generate highresolution images from low-resolution ones for better visualization. However, conventional hallucination methods are often designed for controlled settings and cannot handle varying conditions of pose, resolution degree, and blur. In this paper, we present a new method of face hallucination, which can consistently improve the resolution of face images even with large appearance variations. Our method is based on a novel network architecture called Bi-channel Convolutional Neural Network (Bi-channel CNN). It extracts robust face representations from raw input by using deep convolutional network, then adaptively integrates two channels of information (the raw input image and face representations) to predict the high-resolution image. Experimental results show our system outperforms the prior stateof-the-art methods.
Adaptive sequential Monte Carlo by means of mixture of experts

Selecting appropriately the proposal kernel of particle filters is an issue of significant importance, since a bad choice may lead to deterioration of the particle sample and, consequently, waste of computational power. In this paper we introduce a novel algorithm approximating adaptively the so-called optimal proposal kernel by a mixture of integrated curved exponential distributions with logistic weights. This family of distributions, referred to as mixtures of experts, is broad enough to be used in the presence of multi-modality or strongly skewed distributions. The mixtures are fitted, via Monte Carlo EM or online-EM methods, to the optimal kernel through minimization of the Kullback-Leibler divergence between the auxiliary target and instrumental distributions of the particle filter. At each iteration of the particle filter, the algorithm is required to solve only a single optimization problem for the whole particle sample, as opposed to existing methods solving one problem per particle. In addition, we illustrate in a simulation study how the method can be successfully applied to optimal filtering in nonlinear state-space models.
Translation Prediction with Source Dependency-Based Context Representation

Learning context representations is very promising to improve translation results, particularly through neural networks. Previous efforts process the context words sequentially and neglect their internal syntactic structure. In this paper, we propose a novel neural network based on bi-convolutional architecture to represent the source dependency-based context for translation prediction. The proposed model is able to not only encode the long-distance dependencies but also capture the functional similarities for better translation prediction (i.e., ambiguous words translation and word forms translation). Examined by a largescale Chinese-English translation task, the proposed approach achieves a significant improvement (of up to +1.9 BLEU points) over the baseline system, and meanwhile outperforms a number of context-enhanced comparison system.
Community detection with partially observable links and node attributes

Community detection has been an important task for social and information networks. Existing approaches usually assume the completeness of linkage and content information. However, the links and node attributes can usually be partially observable in many real-world networks. For example, users can specify their privacy settings to prevent non-friends from viewing their posts or connections. Such incompleteness poses additional challenges to community detection algorithms. In this paper, we aim to detect communities with partially observable link structure and node attributes. To fuse such incomplete information, we learn link-based and attribute-based representations via kernel alignment and a co-regularization approach is proposed to combine the information from both sources (i.e., links and attributes). The link-based and attribute-based representations can lend strength to each other via the partial consensus learning. We present two instantiations of this framework by enforcing hard and soft consensus constraint respectively. Experimental results on real-world datasets show the superiority of the proposed approaches over the baseline methods and its robustness under different observable levels.
AN ALTERNATIVE EXTENSION OF THE k-MEANS ALGORITHM FOR CLUSTERING CATEGORICAL DATA

Most of the earlier work on clustering has mainly been focused on numerical data whose inherent geometric properties can be exploited to naturally define distance functions between data points. Recently, the problem of clustering categorical data has started drawing interest. However, the computational cost makes most of the previous algorithms unacceptable for clustering very large databases. The k-means algorithm is well known for its efficiency in this respect. At the same time, working only on numerical data prohibits them from being used for clustering categorical data. The main contribution of this paper is to show how to apply the notion of “cluster centers” on a dataset of categorical objects and how to use this notion for formulating the clustering problem of categorical objects as a partitioning problem. Finally, a k-means-like algorithm for clustering categorical data is introduced. The clustering performance of the algorithm is demonstrated with two well-known data sets, namely, soybean disease andnurserydatabases.
Validating a tool to measure auxiliary nurse midwife and nurse motivation in rural Nepal

A global shortage of health workers in rural areas increases the salience of motivating and supporting existing health workers. Understandings of motivation may vary in different settings, and it is important to use measurement methods that are contextually appropriate. We identified a measurement tool, previously used in Kenya, and explored its validity and reliability to measure the motivation of auxiliary nurse midwives (ANM) and staff nurses (SN) in rural Nepal.



Qualitative and quantitative methods were used to assess the content validity, the construct validity, the internal consistency and the reliability of the tool. We translated the tool into Nepali and it was administered to 137 ANMs and SNs in three districts. We collected qualitative data from 78 nursing personnel and district- and central-level stakeholders using interviews and focus group discussions. We calculated motivation scores for ANMs and SNs using the quantitative data and conducted statistical tests for validity and reliability. Motivation scores were compared with qualitative data. Descriptive exploratory analysis compared mean motivation scores by ANM and SN sociodemographic characteristics.



The concept of self-efficacy was added to the tool before data collection. Motivation was revealed through conscientiousness. Teamwork and the exertion of extra effort were not adequately captured by the tool, but important in illustrating motivation. The statement on punctuality was problematic in quantitative analysis, and attendance was more expressive of motivation. The calculated motivation scores usually reflected ANM and SN interview data, with some variation in other stakeholder responses. The tool scored within acceptable limits in validity and reliability testing and was able to distinguish motivation of nursing personnel with different sociodemographic characteristics.



We found that with minor modifications, the tool provided valid and internally consistent measures of motivation among ANMs and SNs in this context. We recommend the use of this tool in similar contexts, with the addition of statements about self-efficacy, teamwork and exertion of extra effort. Absenteeism should replace the punctuality statement, and statements should be worded both positively and negatively to mitigate positive response bias. Collection of qualitative data on motivation creates a more nuanced understanding of quantitative scores.
A Tlreshold Selection Method from Gray-Level Histograms

A nonparametric and unsupervised method ofautomatic threshold selection for picture segmentation is presented. An optimal threshold is selected by the discriminant criterion, namely, so as to maximize the separability of the resultant classes in gray levels. The procedure is very simple, utilizing only the zerothand the first-order cumulative moments of the gray-level histogram. It is straightforward to extend the method to multithreshold problems. Several experimental results are also presented to support the validity of the method.
CellTree: an R/bioconductor package to infer the hierarchical structure of cell populations from single-cell RNA-seq data

Single-cell RNA sequencing is fast becoming one the standard method for gene expression measurement, providing unique insights into cellular processes. A number of methods, based on general dimensionality reduction techniques, have been suggested to help infer and visualise the underlying structure of cell populations from single-cell expression levels, yet their models generally lack proper biological grounding and struggle at identifying complex differentiation paths. Here we introduce cellTree: an R/Bioconductor package that uses a novel statistical approach, based on document analysis techniques, to produce tree structures outlining the hierarchical relationship between single-cell samples, while identifying latent groups of genes that can provide biological insights. With cellTree, we provide experimentalists with an easy-to-use tool, based on statistically and biologically-sound algorithms, to efficiently explore and visualise single-cell RNA data. The cellTree package is publicly available in the online Bionconductor repository at: http://bioconductor.org/packages/cellTree/ .
Robustness beyond shallowness: incremental deep parsing

Robustness is a key issue for natural language processing in general and parsing in particular, and many approaches have been explored in the last decade for the design of robust parsing systems. Among those approaches is shallow or partial parsing, which produces minimal and incomplete syntactic structures, often in an incremental way. We argue that with a systematic incremental methodology one can go beyond shallow parsing to deeper language analysis, while preserving robustness. We describe a generic system based on such methodology and designed for building robust analyzers that tackle deeper linguistic phenomena than those traditionally handled by the now widespread shallow parsers. The rule formalism allows the recognition of n-ary linguistic relations between words or constituents on the basis of global or local structural, topological and/or lexical conditions. It offers the advantage of accepting various types of inputs, ranging from raw to chunked or constituent-marked texts, so for instance it can be used to process existing annotated corpora, or to perform a deeper analysis on the output of an existing shallow parser. It has been successfully used to build a deep functional dependency parser, as well as for the task of co-reference resolution, in a modular way.
An Approximation Algorithm for the Noah's Ark Problem with Random Feature Loss

The phylogenetic diversity (PD) of a set of species is a measure of their evolutionary distinctness based on a phylogenetic tree. PD is increasingly being adopted as an index of biodiversity in ecological conservation projects. The Noah's Ark Problem (NAP) is an NP-Hard optimization problem that abstracts a fundamental conservation challenge in asking to maximize the expected PD of a set of taxa given a fixed budget, where each taxon is associated with a cost of conservation and a probability of extinction. Only simplified instances of the problem, where one or more parameters are fixed as constants, have as of yet been addressed in the literature. Furthermore, it has been argued that PD is not an appropriate metric for models that allow information to be lost along paths in the tree. We therefore generalize the NAP to incorporate a proposed model of feature loss according to an exponential distribution and term this problem NAP with Loss (NAPL). In this paper, we present a pseudopolynomial time approximation scheme for NAPL.
PROCEDURAL GENERATION OF IMAGINATIVE TREES USING A SPACE COLONIZATION ALGORITHM

The modeling of trees is challenging due to their complex branching structures. Three different ways to generate trees are using real world data for reconstruction, interactive modeling methods and modeling with procedural or rule-based systems. Procedural content generation is the idea of using algorithms to automate content creation processes, and it is useful in plant modeling since it can generate a wide variety of plants that can adapt and react to the environment and changing conditions. This thesis focuses on and extends a procedural tree generation technique that uses a space colonization algorithm to model the tree branches’ competition for space, and shifts the previous works’ focus from realism to fantasy. The technique satisfied the idea of using interaction between the tree’s internal and external factors to determine its final shape, by letting the designer control the where and the how of the tree’s growth process. The implementation resulted in a tree generation application where the user’s imagination decides the limit of what can be produced, and if that limit is reached can the application be used to randomly generate a wide variety of trees and tree-like structures. A motivation for many researchers in the procedural content generation area is how it can be used to augment human imagination. The result of this thesis can be used for that, by stepping away from the restrictions of realism, and with ease let the user generate widely diverse trees, that are not necessarily realistic but, in most cases, adapts to the idea of a tree.