nils-herrmann/clean_scirepeval huggingface dataset:
Multi-task Learning for Financial Forecasting

Financial forecasting is challenging and attractive in machine learning. There are many classic solutions, as well as many deep learning based methods, proposed to deal with it yielding encouraging performance. Stock time series forecasting is the most representative problem in financial forecasting. Due to the strong connections among stocks, the information valuable for forecasting is not only included in individual stocks, but also included in the stocks related to them. However, most previous works focus on one single stock, which easily ignore the valuable information in others. To leverage more information, in this paper, we propose a jointly forecasting approach to process multiple time series of related stocks simultaneously, using multi-task learning framework. Compared to the previous works, we use multiple networks to forecast multiple related stocks, using the shared and private information of them simultaneously through multi-task learning. Moreover, we propose an attention method learning an optimized weighted combination of shared and private information based on the idea of Capital Asset Pricing Model (CAPM) to help forecast. Experimental results on various data show improved forecasting performance over baseline methods.
LPV subspace identification of the edgewise vibrational dynamics of a wind turbine rotor

In this paper we apply a state-of-the-art algorithm for subspace identification of linear parameter-varying (LPV) systems to identify the coupled dynamics of the drive-train and the edgewise bending motion of the rotor blades of three-bladed wind turbines. These dynamics are varying with the rotor speed. The identification algorithm uses a factorization which makes it possible to form predictors based on past inputs, outputs, and the known rotor speed. The predictors contain the LPV equivalent of the Markov parameters. Using the predictors, ideas from Predictor Based Subspace IDentification (PBSID) were developed to estimate the state sequence from which the LPV system matrices can be constructed. The algorithm was applied not only to synthetic data generated by a computer simulation of a reference wind turbine, but also to data measured from the CART3 research wind turbine at the National Wind Technology Center of the National Renewable Energy Laboratory (NREL). This paper demonstrates that the linear time-varying behavior of the aeroelastic dynamics of the wind turbine rotor can be captured in an LPV model identified with measured input-output data.
Motif-Based Classification of Time Series with Bayesian Networks and SVMs

Classification of time series is an important task with many challenging applications like brain wave (EEG) analysis, signature verification or speech recognition. In this paper we show how characteristic local patterns (motifs) can improve the classification accuracy. We introduce a new motif class, generalized semi-continuous motifs. To allow flexibility and noise robustness, these motifs may include gaps of various lengths, generic and more specific wildcards. We propose an efficient algorithm for mining generalized sequential motifs. In experiments on real medical data, we show how generalized semi-continuous motifs improve the accuracy of SVMs and Bayesian Networks for time series classificiation.
Patients with hippocampal amnesia cannot imagine new experiences.

Amnesic patients have a well established deficit in remembering their past experiences. Surprisingly, however, the question as to whether such patients can imagine new experiences has not been formally addressed to our knowledge. We tested whether a group of amnesic patients with primary damage to the hippocampus bilaterally could construct new imagined experiences in response to short verbal cues that outlined a range of simple commonplace scenarios. Our results revealed that patients were markedly impaired relative to matched control subjects at imagining new experiences. Moreover, we identified a possible source for this deficit. The patients' imagined experiences lacked spatial coherence, consisting instead of fragmented images in the absence of a holistic representation of the environmental setting. The hippocampus, therefore, may make a critical contribution to the creation of new experiences by providing the spatial context into which the disparate elements of an experience can be bound. Given how closely imagined experiences match episodic memories, the absence of this function mediated by the hippocampus, may also fundamentally affect the ability to vividly re-experience the past.
Ceiling continuum arm with extensible pneumatic actuators for desktop workspace

We propose an extensible pneumatic continuum arm that elongates to perform reaching movements and object grasping, and is suspended on the ceiling to prevent interference with human workers in a desktop workspace. The selected actuators with bellows aim to enhance the arm motion capabilities. A single actuator can provide a maximum tension force of 150 N, and the proposed arm has a three-segment structure with a bundle of three actuators per segment. We measured the three-dimensional motion at the arm tip by using an optical motion-capture system. The corresponding results show that the arm can grasp objects with approximate radius of 80 mm and reach any point on the desktop. Furthermore, the maximum elongation ratio is 180%, with length varying between 0.75 m and 2.1 m. Experiments verified that the arm can grasp objects of various sizes and shapes. Moreover, we demonstrate the vertical transportation of objects taking advantage of the arm extensibility. We expect to apply the proposed arm for tasks such as grasping objects, illuminating desktops, and physically interacting with users.
Modeling and power conditioning for thermoelectric generation

In this paper, the principle and basic structure of the thermoelectric module is introduced. The steady- state and dynamic behaviors of a single TE module are characterized. An electric model of TE modules is developed and can be embedded in the simulation software for circuit analysis and design. The issues associated with the application of the TEG models is analyzed and pointed out. Power electronic technologies provide solutions for thermoelectric generation with features such as load interfacing, maximum power point tracking, power conditioning and failed module bypassing. A maximum power point tracking algorithm is developed and implemented with a DC-DC converter and low cost microcontroller. Experimental results demonstrated that the power electronic circuit can extract the maximum electrical power from the thermoelectric modules and feed electric loads regardless of the thermoelectric module's heat flux and load impedance or conditions.
PN-Net: Conjoined Triple Deep Network for Learning Local Image Descriptors

In this paper we propose a new approach for learning local descriptors for matching image patches. It has recently been demonstrated that descriptors based on convolutional neural networks (CNN) can significantly improve the matching performance. Unfortunately their computational complexity is prohibitive for any practical application. We address this problem and propose a CNN based descriptor with improved matching performance, significantly reduced training and execution time, as well as low dimensionality. We propose to train the network with triplets of patches that include a positive and negative pairs. To that end we introduce a new loss function that exploits the relations within the triplets. We compare our approach to recently introduced MatchNet and DeepCompare and demonstrate the advantages of our descriptor in terms of performance, memory footprint and speed i.e. when run in GPU, the extraction time of our 128 dimensional feature is comparable to the fastest available binary descriptors such as BRIEF and ORB.
A Survey of Monte Carlo Tree Search Methods

Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work.
Design and fabrication of W-band SIW horn antenna using PCB process

A W-band SIW horn antenna is designed and fabricated using PCB process. Measured S11 reaches -15 dB at 84 GHz with bandwidth of 1 GHz and the maximum simulated gain is 9 dBi. The antenna is loaded with dielectric to increase gain and the effect of different loading lengths on gain is studied. The antenna is fed using a WR10 standard waveguide through a coupling slot in the SIW. The effect of changing the slot width on the return loss is studied. Good agreement is achieved between measured and simulated results. The proposed antenna is suitable for medical imaging and air traffic radar.
Inside packet sampling techniques: exploring modularity to enhance network measurements

Traffic sampling is viewed as a prominent strategy contributing to lightweight and scalable network measurements. Although multiple sampling techniques have been proposed and used to assist network engineering tasks, these techniques tend to address a single measurement purpose, without detailing the network overhead and computational costs involved. The lack of a modular approach when defining the components of traffic sampling techniques also makes difficult their analysis. Providing a modular view of sampling techniques and classifying their characteristics is, therefore, an important step to enlarge the sampling scope, improve the efficiency of measurement systems, and sustain forthcoming research in the area. Thus, this paper defines a taxonomy of traffic sampling techniques resorting to a comprehensive analysis of the inner components of existing proposals. After identifying granularity, selection scheme, and selection trigger as the main components differentiating sampling proposals, the study goes deeper on characterizing these components, including insights into their computational weight. Following this taxonomy, a general-purpose architecture is established to sustain the development of flexible sampling-based measurement systems. Traveling inside packet sampling techniques, this paper contributes to a clearer positioning and comparison of existing proposals, providing a road map to assist further research and deployments in the area.
Network In Network

We propose a novel deep network structure called “Network In Network”(NIN) to enhance model discriminability for local patches within the receptive field. The conventional convolutional layer uses linear filters followed by a nonlinear activation function to scan the input. Instead, we build micro neural networks with more complex structures to abstract the data within the receptive field. We instantiate the micro neural network with a multilayer perceptron, which is a potent function approximator. The feature maps are obtained by sliding the micro networks over the input in a similar manner as CNN; they are then fed into the next layer. Deep NIN can be implemented by stacking mutiple of the above described structure. With enhanced local modeling via the micro network, we are able to utilize global average pooling over feature maps in the classification layer, which is easier to interpret and less prone to overfitting than traditional fully connected layers. We demonstrated the state-of-the-art classification performances with NIN on CIFAR-10 and CIFAR-100, and reasonable performances on SVHN and MNIST datasets.
A survey of glove-based input

Clumsy intermediary devices constrain our interaction with computers and their applications. Glove-based input devices let us apply our manual dexterity to the task. We provide a basis for understanding the field by describing key hand-tracking technologies and applications using glove-based input. The bulk of development in glove-based input has taken place very recently, and not all of it is easily accessible in the literature. We present a cross-section of the field to date. Hand-tracking devices may use the following technologies: position tracking, optical tracking, marker systems, silhouette analysis, magnetic tracking or acoustic tracking. Actual glove technologies on the market include: Sayre glove, MIT LED glove, Digital Data Entry Glove, DataGlove, Dexterous HandMaster, Power Glove, CyberGlove and Space Glove. Various applications of glove technologies include projects into the pursuit of natural interfaces, systems for understanding signed languages, teleoperation and robotic control, computer-based puppetry, and musical performance.<<ETX>>
iVisDesigner: Expressive Interactive Design of Information Visualizations

We present the design, implementation and evaluation of iVisDesigner, a web-based system that enables users to design information visualizations for complex datasets interactively, without the need for textual programming. Our system achieves high interactive expressiveness through conceptual modularity, covering a broad information visualization design space. iVisDesigner supports the interactive design of interactive visualizations, such as provisioning for responsive graph layouts and different types of brushing and linking interactions. We present the system design and implementation, exemplify it through a variety of illustrative visualization designs and discuss its limitations. A performance analysis and an informal user study are presented to evaluate the system.
Graphene-based single-bacterium resolution biodevice and DNA transistor: interfacing graphene derivatives with nanoscale and microscale biocomponents.

Establishing "large-contact-area" interfaces of sensitive nanostructures with microbes and mammalian cells will lead to the development of valuable tools and devices for biodiagnostics and biomedicine. Chemically modified graphene (CMG) nanostructures with their microscale area, sensitive electrical properties, and modifiable chemical functionality are excellent candidates for such biodevices at both biocellular and biomolecular scale. Here, we report on the fabrication and functioning of a novel CMG-based (i) single-bacterium biodevice, (ii) label-free DNA sensor, and (iii) bacterial DNA/protein and polyelectrolyte chemical transistor. The bacteria biodevice was highly sensitive with a single-bacterium attachment generating approximately 1400 charge carriers in a p-type CMG. Similarly, single-stranded DNA tethered on graphene hybridizes with its complementary DNA strand to reversibly increase the hole density by 5.61 x 1012 cm(-2). We further demonstrate (a) a control on the device sensitivity by manipulating surface groups, (b) switching of polarity specificity by changing surface polarity, and (c) a preferential attachment of DNA on thicker CMG surfaces and sharp CMG wrinkles.
A Study of Filter Bank Smoothing in MFCC Features for Recognition of Children's Speech

In this paper, we study the effect of filter bank smoothing on the recognition performance of children's speech. Filter bank smoothing of spectra is done during the computation of the Mel filter bank cepstral coefficients (MFCCs). We study the effect of smoothing both for the case when there is vocal-tract length normalization (VTLN) as well as for the case when there is no VTLN. The results from our experiments indicate that unlike conventional VTLN implementation, it is better not to scale the bandwidths of the filters during VTLN - only the filter center frequencies need be scaled. Our interpretation of the above result is that while the formant center frequencies may approximately scale between speakers, the formant bandwidths do not change significantly. Therefore, the scaling of filter bandwidths by a warp-factor during conventional VTLN results in differences in spectral smoothing leading to degradation in recognition performance. Similarly, results from our experiments indicate that for telephone-based speech when there is no normalization it is better to use uniform-bandwidth filters instead of the constant- like filters that are used in the computation of conventional MFCC. Our interpretation is that with constant- filters there is excessive spectral smoothing at higher frequencies which leads to degradation in performance for children's speech. However, the use of constant- filters during VTLN does not create any additional performance degradation. As we will show, during VTLN it is only important that the filter bandwidths are not scaled irrespective of whether we use constant- or uniform-bandwidth filters. With our proposed changes in the filter bank implementation we get comparable performance for adults and about 6% improvement for children both for the case of using VTLN as well as the for the case of not using VTLN on a telephone-based digit recognition task.
Information propagation in the Bitcoin network

Bitcoin is a digital currency that unlike traditional currencies does not rely on a centralized authority. Instead Bitcoin relies on a network of volunteers that collectively implement a replicated ledger and verify transactions. In this paper we analyze how Bitcoin uses a multi-hop broadcast to propagate transactions and blocks through the network to update the ledger replicas. We then use the gathered information to verify the conjecture that the propagation delay in the network is the primary cause for blockchain forks. Blockchain forks should be avoided as they are symptomatic for inconsistencies among the replicas in the network. We then show what can be achieved by pushing the current protocol to its limit with unilateral changes to the client's behavior.
Emerging topic detection on Twitter based on temporal and social terms evaluation

Twitter is a user-generated content system that allows its users to share short text messages, called tweets, for a variety of purposes, including daily conversations, URLs sharing and information news. Considering its world-wide distributed network of users of any age and social condition, it represents a low level news flashes portal that, in its impressive short response time, has the principal advantage.
 In this paper we recognize this primary role of Twitter and we propose a novel topic detection technique that permits to retrieve in real-time the most emergent topics expressed by the community. First, we extract the contents (set of terms) of the tweets and model the term life cycle according to a novel aging theory intended to mine the emerging ones. A term can be defined as emerging if it frequently occurs in the specified time interval and it was relatively rare in the past. Moreover, considering that the importance of a content also depends on its source, we analyze the social relationships in the network with the well-known Page Rank algorithm in order to determine the authority of the users. Finally, we leverage a navigable topic graph which connects the emerging terms with other semantically related keywords, allowing the detection of the emerging topics, under user-specified time constraints. We provide different case studies which show the validity of the proposed approach.
Experiential Avoidance in Clinical and Non-Clinical Samples : AAQ-II Portuguese Version

* Correspondence to: José Pinto-Gouveia, CINEICC, Faculdade de Psicologia e de Ciências da Educação, Universidade de Coimbra, Rua do Colégio Novo, Apartado 6153, 3001-802 Coimbra, Portugal. E-mail: jpgouveia@fpce.uc.pt This research has been supported by the second and third authors Ph.D. Grants (SFRH/BD/40290/2007; SFRH/ BD/36211/2007), sponsored by FCT (Portuguese Foundation for Science and Technology).
A compact-size microstrip spiral resonator and its application to microwave oscillator

This letter presents a compact size microstrip spiral resonator and its application to a low phase noise oscillator. This resonator has stopband characteristics to be used in the series feedback oscillator topology. The whole circuit area of the proposed resonator is within 1/10 wavelength, which results in the reduction of the circuit area and cost. A 10-GHz oscillator incorporated with this resonator was designed, fabricated and measured. It shows low phase noise performance of -95.4-dBc/Hz at 100 kHz offset.
An improved frequency compensation technique for CMOS operational amplifiers

The commonly used two-stage CMOS operational amplifier suffers from two basic performance limitations due to the RC compensation network around the second gain stage. First, it provides stable operation for only a limited range of capacitive loads, and second, the power supply rejection shows severe degradation above the open-loop pole frequency. The technique described provides stable operation for a much larger range of capacitive loads, as well as much improved V/SUB BB/ power supply rejection over very wide bandwidths for the same basic operational amplifier circuit. The author presents a mathematical analysis of this new technique in terms of its frequency and noise characteristics followed by its implementation in all n-well CMOS process. Experimental results show 70-dB negative power supply rejection at 100 kHz and an input noise density of 58 nV/(Hz)/SUP 1/2/ at 1 kHz.
Towards an Architecture for Knowledge Representation and Reasoning in Robotics

This paper describes an architecture that combines the complementary strengths of probabilistic graphical models and declarative programming to enable robots to represent and reason with qualitative and quantitative descriptions of uncertainty and domain knowledge. An action language is used for the architecture’s low-level (LL) and high-level (HL) system descriptions, and the HL definition of recorded history is expanded to allow prioritized defaults. For any given objective, tentative plans created in the HL using commonsense reasoning are implemented in the LL using probabilistic algorithms, and the corresponding observations are added to the HL history. Tight coupling between the levels helps automate the selection of relevant variables and the generation of policies in the LL for each HL action, and supports reasoning with violation of defaults, noisy observations and unreliable actions in complex domains. The architecture is evaluated in simulation and on robots moving objects in indoor domains.
Acceptance is in the eye of the beholder: self-esteem and motivated perceptions of acceptance from the opposite sex.

Social risk elicits self-esteem differences in signature social motivations and behaviors during the relationship-initiation process. In particular, the present research tested the hypothesis that lower self-esteem individuals' (LSEs) motivation to avoid rejection leads them to self-protectively underestimate acceptance from potential romantic partners, whereas higher self-esteem individuals' (HSEs) motivation to promote new relationships leads them to overestimate acceptance. The results of 5 experiments supported these predictions. Social risk increased activation of avoidance goals for LSEs on a word-recall task but increased activation of approach goals for HSEs, as evidenced by their increased use of likeable behaviors. Consistent with these patterns of goal activation, even though actual acceptance cues were held constant across all participants, social risk decreased the amount of acceptance that LSEs perceived from their interaction partner but increased the amount of acceptance that HSEs perceived from their interaction partner. It is important to note that such self-esteem differences in avoidance goals, approach behaviors, and perceptions of acceptance were completely eliminated when social risk was removed.