nils-herrmann/clean_scirepeval huggingface dataset:
VLocNet++: Deep Multitask Learning for Semantic Visual Localization and Odometry

Semantic understanding and localization are fundamental enablers of robot autonomy that have been tackled as disjoint problems for the most part. While deep learning has enabled recent breakthroughs across a wide spectrum of scene understanding tasks, its applicability to state estimation tasks has been limited due to the direct formulation that renders it incapable of encoding scene-specific constrains. In this letter, we propose the VLocNet++ architecture that employs a multitask learning approach to exploit the inter-task relationship between learning semantics, regressing 6-DoF global pose and odometry, for the mutual benefit of each of these tasks. Our network overcomes the aforementioned limitation by simultaneously embedding geometric and semantic knowledge of the world into the pose regression network. We propose a novel adaptive weighted fusion layer to aggregate motion-specific temporal information and to fuse semantic features into the localization stream based on region activations. Furthermore, we propose a self-supervised warping technique that uses the relative motion to warp intermediate network representations in the segmentation stream for learning consistent semantics. Finally, we introduce a first-of-a-kind urban outdoor localization dataset with pixel-level semantic labels and multiple loops for training deep networks. Extensive experiments on the challenging Microsoft 7-Scenes benchmark and our DeepLoc dataset demonstrate that our approach exceeds the state-of-the-art outperforming local feature-based methods while simultaneously performing multiple tasks and exhibiting substantial robustness in challenging scenarios.
Transformational leadership: relations to the five-factor model and team performance in typical and maximum contexts.

This study examined the 5-factor model of personality, transformational leadership, and team performance under conditions similar to typical and maximum performance contexts. Data were collected from 39 combat teams from an Asian military sample (N = 276). Results found that neuroticism and agreeableness were negatively related to transformational leadership ratings. Team performance ratings correlated at only.18 across the typical and maximum contexts. Furthermore, transformational leadership related more strongly to team performance in the maximum rather than the typical context. Finally, transformational leadership fully mediated the relationship between leader personality and team performance in the maximum context but only partially mediated the relationship between leader personality and team performance in the typical context. The Discussion section focuses on how these findings, although interesting, need to be replicated with different designs, contexts, and measures.
Thinking in circuits: toward neurobiological explanation in cognitive neuroscience

Cognitive theory has decomposed human mental abilities into cognitive (sub) systems, and cognitive neuroscience succeeded in disclosing a host of relationships between cognitive systems and specific structures of the human brain. However, an explanation of why specific functions are located in specific brain loci had still been missing, along with a neurobiological model that makes concrete the neuronal circuits that carry thoughts and meaning. Brain theory, in particular the Hebb-inspired neurocybernetic proposals by Braitenberg, now offers an avenue toward explaining brain–mind relationships and to spell out cognition in terms of neuron circuits in a neuromechanistic sense. Central to this endeavor is the theoretical construct of an elementary functional neuronal unit above the level of individual neurons and below that of whole brain areas and systems: the distributed neuronal assembly (DNA) or thought circuit (TC). It is shown that DNA/TC theory of cognition offers an integrated explanatory perspective on brain mechanisms of perception, action, language, attention, memory, decision and conceptual thought. We argue that DNAs carry all of these functions and that their inner structure (e.g., core and halo subcomponents), and their functional activation dynamics (e.g., ignition and reverberation processes) answer crucial localist questions, such as why memory and decisions draw on prefrontal areas although memory formation is normally driven by information in the senses and in the motor system. We suggest that the ability of building DNAs/TCs spread out over different cortical areas is the key mechanism for a range of specifically human sensorimotor, linguistic and conceptual capacities and that the cell assembly mechanism of overlap reduction is crucial for differentiating a vocabulary of actions, symbols and concepts.
Semi-supervised Learning with Constraints for Person Identification in Multimedia Data

We address the problem of person identification in TV series. We propose a unified learning framework for multi-class classification which incorporates labeled and unlabeled data, and constraints between pairs of features in the training. We apply the framework to train multinomial logistic regression classifiers for multi-class face recognition. The method is completely automatic, as the labeled data is obtained by tagging speaking faces using subtitles and fan transcripts of the videos. We demonstrate our approach on six episodes each of two diverse TV series and achieve state-of-the-art performance.
Knowledge-enabled Recommender Systems: Models, Challenges, Solutions

Together with the rapid growing of information we daily produce, we have assisted to the flourishing of new tools and techniques whose aim is to help users in accessing such information in a personalized way. In the Information Overload era we are living in, the amount of information exceeds the users capability of processing and using it [39]. Huge and fast growing number of possibilities overwhelms users, leading them to make poor decisions and feel anxiety and unsatisfaction [37]. Recommender Systems (RSs) [32] are a family of information filtering tools which have proven to be valuable means in assisting users to find, in a personalized manner, what is relevant for them in such overflowing complex information spaces. They provide users with personalized access to large collections of resources. The main task of a recommendation engine is typically to estimate the relevance of unknown items for a target user and recommend the Top-N items by considering for each user the best N items with highest utility [32]. Typically, the utility of an item is represented by a numerical rating, which indicates how much a particular user liked such item. However, the utility is not defined for each pair of users and items and usually it is available only for a small subset of them. Such subset represents the input of a generic recommender system, whose objective is to estimate the utility of all the remaining pairs. A formal definition of the recommendation problem has been given in  and defined as follows.
The line of action: an intuitive interface for expressive character posing

The line of action is a conceptual tool often used by cartoonists and illustrators to help make their figures more consistent and more dramatic. We often see the expression of characters---may it be the dynamism of a super hero, or the elegance of a fashion model---well captured and amplified by a single aesthetic line. Usually this line is laid down in early stages of the drawing and used to describe the body's principal shape. By focusing on this simple abstraction, the person drawing can quickly adjust and refine the overall pose of his or her character from a given viewpoint. In this paper, we propose a mathematical definition of the line of action (LOA), which allows us to automatically align a 3D virtual character to a user-specified LOA by solving an optimization problem. We generalize this framework to other types of lines found in the drawing literature, such as secondary lines used to place arms. Finally, we show a wide range of poses and animations that were rapidly created using our system.
Design and implementation of a single-phase buck-boost inverter

In order to simplify the configuration of photovoltaic (PV) grid-connection system, this paper proposes to adopt a buck-boost dc-dc converter, and then develops a single-phase inverter via the connection with an H-bridge unfolding circuit with line-commutated. Depending on the conditions of dc input-voltage and ac output-voltage, the proposed circuit can work functionally as either a step-down or step-up inverter. It is suitable for the applications with wide voltage-variation range. Since only one switch is operated with high-frequency, the switching loss can be significantly reduced to improve the efficiency. Finally, a laboratory prototype with 110 Vrms / 60 Hz output voltage is then simulated and implemented accordingly to verify the feasibility of the proposed inverter.
Answering General Time-Sensitive Queries

Time is an important dimension of relevance for a large number of searches, such as over blogs and news archives. So far, research on searching over such collections has largely focused on locating topically similar documents for a query. Unfortunately, topic similarity alone is not always sufficient for document ranking. In this paper, we observe that, for an important class of queries that we call time-sensitive queries, the publication time of the documents in a news archive is important and should be considered in conjunction with the topic similarity to derive the final document ranking. Earlier work has focused on improving retrieval for "recency" queries that target recent documents. We propose a more general framework for handling time-sensitive queries and we automatically identify the important time intervals that are likely to be of interest for a query. Then, we build scoring techniques that seamlessly integrate the temporal aspect into the overall ranking mechanism. We extensively evaluated our techniques using a variety of news article data sets, including TREC data as well as real web data analyzed using the Amazon Mechanical Turk. We examined several alternatives for detecting the important time intervals for a query over a news archive and for incorporating this information in the retrieval process. Our techniques are robust and significantly improve result quality for time-sensitive queries compared to state-of-the-art retrieval techniques.
Metagenomics - a guide from sampling to data analysis

Metagenomics applies a suite of genomic technologies and bioinformatics tools to directly access the genetic content of entire communities of organisms. The field of metagenomics has been responsible for substantial advances in microbial ecology, evolution, and diversity over the past 5 to 10 years, and many research laboratories are actively engaged in it now. With the growing numbers of activities also comes a plethora of methodological knowledge and expertise that should guide future developments in the field. This review summarizes the current opinions in metagenomics, and provides practical guidance and advice on sample processing, sequencing technology, assembly, binning, annotation, experimental design, statistical analysis, data storage, and data sharing. As more metagenomic datasets are generated, the availability of standardized procedures and shared data storage and analysis becomes increasingly important to ensure that output of individual projects can be assessed and compared.
Action Observation and Motor Imagery: Innovative Cognitive Tools in the Rehabilitation of Parkinson's Disease

Parkinson's disease (PD) is characterized by a progressive impairment of motor skills with deterioration of autonomy in daily living activities. Physiotherapy is regarded as an adjuvant to pharmacological and neurosurgical treatment and may provide small and short-lasting clinical benefits in PD patients. However, the development of innovative rehabilitation approaches with greater long-term efficacy is a major unmet need. Motor imagery (MI) and action observation (AO) have been recently proposed as a promising rehabilitation tool. MI is the ability to imagine a movement without actual performance (or muscle activation). The same cortical-subcortical network active during motor execution is engaged in MI. The physiological basis of AO is represented by the activation of the "mirror neuron system." Both MI and AO are involved in motor learning and can induce improvements of motor performance, possibly mediated by the development of plastic changes in the motor cortex. The review of available evidences indicated that MI ability and AO feasibility are substantially preserved in PD subjects. A few preliminary studies suggested the possibility of using MI and AO as parts of rehabilitation protocols for PD patients.
Why-Question Answering using Intra- and Inter-Sentential Causal Relations

In this paper, we explore the utility of intraand inter-sentential causal relations between terms or clauses as evidence for answering why-questions. To the best of our knowledge, this is the first work that uses both intraand inter-sentential causal relations for why-QA. We also propose a method for assessing the appropriateness of causal relations as answers to a given question using the semantic orientation of excitation proposed by Hashimoto et al. (2012). By applying these ideas to Japanese why-QA, we improved precision by 4.4% against all the questions in our test set over the current state-of-theart system for Japanese why-QA. In addition, unlike the state-of-the-art system, our system could achieve very high precision (83.2%) for 25% of all the questions in the test set by restricting its output to the confident answers only.
The Fusiform Face Area is Part of a Network that Processes Faces at the Individual Level

According to modular models of cortical organization, many areas of the extrastriate cortex are dedicated to object categories. These models often assume an early processing stage for the detection of category membership. Can functional imaging isolate areas responsible for detection of members of a category, such as faces or letters? We consider whether responses in three different areas (two selective for faces and one selective for letters) support category detection. Activity in these areas habituates to the repeated presentation of one exemplar more than to the presentation of different exemplars of the same category, but only for the category for which the area is selective. Thus, these areas appear to play computational roles more complex than detection, processing stimuli at the individual level. Drawing from prior work, we suggest that face-selective areas may be involved in the perception of faces at the individual level, whereas letter-selective regions may be tuning themselves to font information in order to recognize letters more efficiently.
POSTER: Practical Fraud Transaction Prediction

Nowadays, online payment systems play more and more important roles in people's daily lives. A key component of these systems is to detect and prevent fraud transactions. In industrial practice, such a task is separated into two phases: 1) mining evidential features to describe users, 2) building an effective model based on these features. Generally speaking, the most popular fraud transaction detection systems use elaborately designed features to build tree based models, sometimes a subsequent linear model is added to improve the behaviour. However, the designed features usually contains only static features, while dynamic features are not considered. In addition, the subsequent model can only learn a linear combination, which may always be unsatisfactory. To address these issues, we present a systematic method, which extracts not only users' static features but also dynamic features based on their recent behaviors. Moreover, N-GRAM model is employed to handle the dynamic features so that time series information is addressed. Based on the extracted features, a tree based model is applied and the outputs of it are regarded as new generated feature representations, which will be further inputted into a Deep Neural Network (DNN) to learn the complex relationships and form the final classification model. Extensive experiments show that our proposed model (with both static and dynamic features) significantly outperforms the existing methods.
TRESNEI, a Matlab trust-region solver for systems of nonlinear equalities and inequalities

The Matlab implementation of a trust-region Gauss-Newton method for boundconstrained nonlinear least-squares problems is presented. The solver, called TRESNEI, is adequate for zero and small-residual problems and handles the solution of nonlinear systems of equalities and inequalities. The structure and the usage of the solver are described and an extensive numerical comparison with functions from the Matlab Optimization Toolbox is carried out.
Automatic Extraction of Useful Facet Hierarchies from Text Databases

Databases of text and text-annotated data constitute a significant fraction of the information available in electronic form. Searching and browsing are the typical ways that users locate items of interest in such databases. Faceted interfaces represent a new powerful paradigm that proved to be a successful complement to keyword searching. Thus far, the identification of the facets was either a manual procedure, or relied on apriori knowledge of the facets that can potentially appear in the underlying collection. In this paper, we present an unsupervised technique for automatic extraction of facets useful for browsing text databases. In particular, we observe, through a pilot study, that facet terms rarely appear in text documents, showing that we need external resources to identify useful facet terms. For this, we first identify important phrases in each document. Then, we expand each phrase with ";context"; phrases using external resources, such as WordNet and Wikipedia, causing facet terms to appear in the expanded database. Finally, we compare the term distributions in the original database and the expanded database to identify the terms that can be used to construct browsing facets. Our extensive user studies, using the Amazon Mechanical Turk service, show that our techniques produce facets with high precision and recall that are superior to existing approaches and help users locate interesting items faster.
CANTINA+: A Feature-Rich Machine Learning Framework for Detecting Phishing Web Sites

Phishing is a plague in cyberspace. Typically, phish detection methods either use human-verified URL blacklists or exploit Web page features via machine learning techniques. However, the former is frail in terms of new phish, and the latter suffers from the scarcity of effective features and the high false positive rate (FP). To alleviate those problems, we propose a layered anti-phishing solution that aims at (1) exploiting the expressiveness of a rich set of features with machine learning to achieve a high true positive rate (TP) on novel phish, and (2) limiting the FP to a low level via filtering algorithms.
 Specifically, we proposed CANTINA+, the most comprehensive feature-based approach in the literature including eight novel features, which exploits the HTML Document Object Model (DOM), search engines and third party services with machine learning techniques to detect phish. Moreover, we designed two filters to help reduce FP and achieve runtime speedup. The first is a near-duplicate phish detector that uses hashing to catch highly similar phish. The second is a login form filter, which directly classifies Web pages with no identified login form as legitimate.
 We extensively evaluated CANTINA+ with two methods on a diverse spectrum of corpora with 8118 phish and 4883 legitimate Web pages. In the randomized evaluation, CANTINA+ achieved over 92% TP on unique testing phish and over 99% TP on near-duplicate testing phish, and about 0.4% FP with 10% training phish. In the time-based evaluation, CANTINA+ also achieved over 92% TP on unique testing phish, over 99% TP on near-duplicate testing phish, and about 1.4% FP under 20% training phish with a two-week sliding window. Capable of achieving 0.4% FP and over 92% TP, our CANTINA+ has been demonstrated to be a competitive anti-phishing solution.
Packet Routing in Dynamically Changing Networks: A Reinforcement Learning Approach

This paper describes the Q-routing algorithm for packet routing, in which a reinforcement learning module is embedded into each node of a switching network. Only local communication is used by each node to keep accurate statistics on which routing decisions lead to minimal delivery times. In simple experiments involving a 36-node, irregularly connected network, Q-routing proves superior to a nonadaptive algorithm based on precomputed shortest paths and is able to route e ciently even when critical aspects of the simulation, such as the network load, are allowed to vary dynamically. The paper concludes with a discussion of the tradeo between discovering shortcuts and maintaining stable policies.
A resistive TCAM accelerator for data-intensive computing

Power dissipation and off-chip bandwidth restrictions are critical challenges that limit microprocessor performance. Ternary content addressable memories (TCAM) hold the potential to address both problems in the context of a wide range of data-intensive workloads that benefit from associative search capability. Power dissipation is reduced by eliminating instruction processing and data movement overheads present in a purely RAM based system. Bandwidth demand is lowered by processing data directly on the TCAM chip, thereby decreasing off-chip traffic. Unfortunately, CMOS-based TCAM implementations are severely power- and area-limited, which restricts the capacity of commercial products to a few megabytes, and confines their use to niche networking applications.
 This paper explores a novel resistive TCAM cell and array architecture that has the potential to scale TCAM capacity from megabytes to gigabytes. High-density resistive TCAM chips are organized into a DDR3-compatible DIMM, and are accessed through a software library with zero modifications to the processor or the motherboard. On applications that do not benefit from associative search, the TCAM DIMM is configured to provide ordinary RAM functionality. By tightly integrating TCAM with conventional virtual memory, and by allowing a large fraction of the physical address space to be made content-addressable on demand, the proposed memory system improves average performance by 4 x and average energy consumption by 10 x on a set of evaluated data-intensive applications.
A probabilistic approach to spatiotemporal theme pattern mining on weblogs

Mining subtopics from weblogs and analyzing their spatiotemporal patterns have applications in multiple domains. In this paper, we define the novel problem of mining spatiotemporal theme patterns from weblogs and propose a novel probabilistic approach to model the subtopic themes and spatiotemporal theme patterns simultaneously. The proposed model discovers spatiotemporal theme patterns by (1) extracting common themes from weblogs; (2) generating theme life cycles for each given location; and (3) generating theme snapshots for each given time period. Evolution of patterns can be discovered by comparative analysis of theme life cycles and theme snapshots. Experiments on three different data sets show that the proposed approach can discover interesting spatiotemporal theme patterns effectively. The proposed probabilistic model is general and can be used for spatiotemporal text mining on any domain with time and location information.