nils-herrmann/clean_scirepeval huggingface dataset:
Neuronal factors determining high intelligence.

Many attempts have been made to correlate degrees of both animal and human intelligence with brain properties. With respect to mammals, a much-discussed trait concerns absolute and relative brain size, either uncorrected or corrected for body size. However, the correlation of both with degrees of intelligence yields large inconsistencies, because although they are regarded as the most intelligent mammals, monkeys and apes, including humans, have neither the absolutely nor the relatively largest brains. The best fit between brain traits and degrees of intelligence among mammals is reached by a combination of the number of cortical neurons, neuron packing density, interneuronal distance and axonal conduction velocity--factors that determine general information processing capacity (IPC), as reflected by general intelligence. The highest IPC is found in humans, followed by the great apes, Old World and New World monkeys. The IPC of cetaceans and elephants is much lower because of a thin cortex, low neuron packing density and low axonal conduction velocity. By contrast, corvid and psittacid birds have very small and densely packed pallial neurons and relatively many neurons, which, despite very small brain volumes, might explain their high intelligence. The evolution of a syntactical and grammatical language in humans most probably has served as an additional intelligence amplifier, which may have happened in songbirds and psittacids in a convergent manner.
Contrast-Guided Image Interpolation

In this paper a contrast-guided image interpolation method is proposed that incorporates contrast information into the image interpolation process. Given the image under interpolation, four binary contrast-guided decision maps (CDMs) are generated and used to guide the interpolation filtering through two sequential stages: 1) the 45° and 135° CDMs for interpolating the diagonal pixels and 2) the 0° and 90° CDMs for interpolating the row and column pixels. After applying edge detection to the input image, the generation of a CDM lies in evaluating those nearby non-edge pixels of each detected edge for re-classifying them possibly as edge pixels. This decision is realized by solving two generalized diffusion equations over the computed directional variation (DV) fields using a derived numerical approach to diffuse or spread the contrast boundaries or edges, respectively. The amount of diffusion or spreading is proportional to the amount of local contrast measured at each detected edge. The diffused DV fields are then thresholded for yielding the binary CDMs, respectively. Therefore, the decision bands with variable widths will be created on each CDM. The two CDMs generated in each stage will be exploited as the guidance maps to conduct the interpolation process: for each declared edge pixel on the CDM, a 1-D directional filtering will be applied to estimate its associated to-be-interpolated pixel along the direction as indicated by the respective CDM; otherwise, a 2-D directionless or isotropic filtering will be used instead to estimate the associated missing pixels for each declared non-edge pixel. Extensive simulation results have clearly shown that the proposed contrast-guided image interpolation is superior to other state-of-the-art edge-guided image interpolation methods. In addition, the computational complexity is relatively low when compared with existing methods; hence, it is fairly attractive for real-time image applications.
Receiver Design for a Bionic Nervous System: Modeling the Dendritic Processing Power

Intrabody nanonetworks for nervous system monitoring are envisioned as a key application of the Internet of Nano-Things (IoNT) paradigm, with the aim of developing radically new medical diagnosis and treatment techniques. Indeed, very recently, bionic devices have been implanted inside a living human brain as innovative treatment for drug-resistant epilepsy. In this context, this paper proposes a systems-theoretic communication model to capture the actual behavior of biological neurons. Specifically, biological neurons exhibit physical extension due to their projections called dendrites, which propagate the electrochemical stimulation received via synapses to the soma. Experimental evidences show that the dendrites exhibit two main features: 1) the compartmentalization at the level of the dendritic branches of the neuronal processes and 2) the location-dependent preference for different frequencies. Stemming from these experimental evidences, we propose to model the dendritic tree as a spatiotemporal filter bank, where each filter models the behavior in both space and time of a dendritic branch. Each filter is fully characterized along with the overall neuronal response. Furthermore, sufficient conditions on the incoming stimulus for inducing a null-neuronal response are derived. The conducted theoretical analysis shows that: 1) the neuronal information is encoded in the stimulus temporal pattern, i.e., it is possible to select the neuron to affect by changing the stimulus frequency content; in this sense, the communication among neurons is frequency-selective and 2) the spatial distribution of the dendrites affects the neuronal response; in this sense, the communication among neurons is spatial-selective. The theoretical analysis is validated through a real neuron morphology.
Telemanipulation with Time Delays

In this paper we survey the development of the wave variable concept and examine wave-based teleoperation. We study the behavior of force reflecting systems under unknown but constant transmission delays, ranging from periods less than the human reaction time to several seconds. Passive transmission procedures guarantee system stability, but wave reflections and spurious dynamics may interfere with normal operation. Using wave variables for the analysis and implementation, and making appropriate design choices, a system with consistent and predictable behavior is constructed. This design methodology aims to create a virtual tool which accounts for the implicit limitations imposed by the delay. These developments also form the basis for extensions to wave-based prediction and application to variable delays, such as those inherent to Internet-based telemanipulation. teleoperation, force reflection, time delay, wave variables, passivity, impedence matching, virtual tool
MIMO Performance of Realistic UE Antennas in LTE Scenarios at 750 MHz

Multiple-input-multiple-output (MIMO) is a technique to achieve high data rates in mobile communication networks. Simulations are performed at both the antenna level and Long-Term Evolution (LTE) system level to assess the performance of realistic handheld devices with dual antennas at 750 MHz. It is shown that MIMO works very well and gives substantial performance gain in user devices with a quarter-wavelength antenna separation.
Multi-swarm Optimization in Dynamic Environments

Many real-world problems are dynamic, requiring an optimization algorithm which is able to continuously track a changing optimum over time. In this paper, we present new variants of Particle Swarm Optimization (PSO) specifically designed to work well in dynamic environments. The main idea is to extend the single population PSO and Charged Particle Swarm Optimization (CPSO) methods by constructing interacting multi-swarms. In addition, a new algorithmic variant, which broadens the implicit atomic analogy of CPSO to a quantum model, is introduced. The multi-swarm algorithms are tested on a multi-modal dynamic function – the moving peaks benchmark – and results are compared to the single population approach of PSO and CPSO, and to results obtained by a state-of-the-art evolutionary algorithm, namely self-organizing scouts (SOS). We show that our multi-swarm optimizer significantly outperforms single population PSO on this problem, and that multi-quantum swarms are superior to multi-charged swarms and SOS.
A Practical and Provably Secure Coalition-Resistant Group Signature Scheme

A group signature scheme allows a group member to sign messages anonymously on behalf of the group. However, in the case of a dispute, the identity of a signature’s originator can be revealed (only) by a designated entity. The interactive counterparts of group signatures are identity escrow schemes or group identification scheme with revocable anonymity. This work introduces a new provably secure group signature and a companion identity escrow scheme that are significantly more efficient than the state of the art. In its interactive, identity escrow form, our scheme is proven secure and coalition-resistant under the strong RSA and the decisional Diffie-Hellman assumptions. The security of the noninteractive variant, i.e., the group signature scheme, relies additionally on the Fiat-Shamir heuristic (also known as the random oracle model).
Autoencoder node saliency: Selecting relevant latent representations

The autoencoder is an artificial neural network that learns hidden representations of unlabeled data. With a linear transfer function it is similar to the principal component analysis (PCA). While both methods use weight vectors for linear transformations, the autoencoder does not come with any indication similar to the eigenvalues in PCA that are paired with eigenvectors. We propose a novel supervised node saliency (SNS) method that ranks the hidden nodes, which contain weight vectors for transformations. SNS is able to indicate the nodes specialized in a learning task. The latent representations of a hidden node can be described using a one-dimensional histogram. We apply normalized entropy difference (NED) to measure the ”interestingness” of the histograms, and conclude a property for NED values to identify a good classifying node. By applying our methods to real datasets, we demonstrate their ability to find valuable nodes and explain the learned tasks in autoencoders.
Unsupervised Sentence Simplification Using Deep Semantics

We present a novel approach to sentence simplification which departs from previous work in two main ways. First, it requires neither hand written rules nor a training corpus of aligned standard and simplified sentences. Second, sentence splitting operates on deep semantic structure. We show (i) that the unsupervised framework we propose is competitive with four state-of-the-art supervised systems and (ii) that our semantic based approach allows for a principled and effective handling of sentence splitting.
Microrobots for minimally invasive medicine.

Microrobots have the potential to revolutionize many aspects of medicine. These untethered, wirelessly controlled and powered devices will make existing therapeutic and diagnostic procedures less invasive and will enable new procedures never before possible. The aim of this review is threefold: first, to provide a comprehensive survey of the technological state of the art in medical microrobots; second, to explore the potential impact of medical microrobots and inspire future research in this field; and third, to provide a collection of valuable information and engineering tools for the design of medical microrobots.
Interactive painterly stylization of images, videos and 3D animations

We introduce a real-time system that converts images, video, or 3D animation sequences to artistic renderings in various painterly styles. The algorithm, which is entirely executed on the GPU, can efficiently process 512 resolution frames containing 60,000 individual strokes at over 30 fps. In order to exploit the parallel nature of GPUs, our algorithm determines the placement of strokes entirely from local pixel neighborhood information. The strokes are rendered as point sprites with textures. Temporal coherence is achieved by treating the brush strokes as particles and moving them based on optical flow. Our system renders high quality results while allowing the user interactive control over many stylistic parameters such as stroke size, texture and density.
Aspect and Entity Extraction for Opinion Mining

Opinion mining or sentiment analysis is the computational study of people’s opinions, appraisals, attitudes, and emotions toward entities such as products, services, organizations, individuals, events, and their different aspects. It has been an active research area in natural language processing and Web mining in recent years. Researchers have studied opinion mining at the document, sentence and aspect levels. Aspect-level (called aspect-based opinion mining) is often desired in practical applications as it provides the detailed opinions or sentiments about different aspects of entities and entities themselves, which are usually required for action. Aspect extraction and entity extraction are thus two core tasks of aspect-based opinion mining. In this chapter, we provide a broad overview of the tasks and the current state-of-the-art extraction techniques.
Combining pixel domain and compressed domain index for sketch based image retrieval

Sketch-based image retrieval (SBIR) lets one express a precise visual query with simple and widespread means. In the SBIR approaches, the challenge consists in representing the image dataset features in a structure that allows one to efficiently and effectively retrieve images in a scalable system. We put forward a sketch-based image retrieval solution where sketches and natural image contours are represented and compared, in both, the compressed-domain of wavelet and in the pixel domain. The query is efficiently performed in the wavelet domain, while effectiveness refinements are achieved using the pixel domain to verify the spatial consistency between the sketch strokes and the natural image contours. Also, we present an efficient scheme of inverted lists for sketch-based image retrieval using the compressed-domain of wavelets. Our proposal of indexing presents two main advantages, the amount of the data to compute the query is smaller than the traditional method while it presents a better effectiveness.
Lichen Aureus : A Congenital Case ? Liquen aureus : ¿ un caso congénito ?

A 9-year-old boy, with Fitzpatrick skin phototype V, was seen for an asymptomatic lesion on his left knee, diagnosed at birth by the neonatologist as a café au lait spot. The lesion was described in the neonatal discharge report as a brownish macule measuring 1.5 × 1.5 cm, on the left knee, observed during the first examination of the newborn infant. However, there had been no subsequent follow-up and the child had not been seen in dermatology before his visit to our outpatient clinic. On examination of the child’s left knee, a number of shiny erythematous-purpuric plaques were observed in a linear distribution, with a slightly pigmented halo and fine superficial desquamation. Dermoscopy revealed lobules and pink dots on a diffuse coppery-brown background, and a fine brown network . With a diagnostic suspicion of a lichenoid rash, lichen aureus (LA), or nevus flammeus----because of its congenital nature----one of the plaques was biopsied. Minimal hyperkeratosis, mild acanthosis, and mild epidermal spongiosis were observed on histology, together with a dense, band-like lichenoid inflammatory infiltrate in the papillary dermis, with occasional apoptotic keratinocytes and extensive red cell extravasation .
Goal Evaluation of Segmentation Algorithms for Traffic Sign Recognition

This paper presents a quantitative comparison of several segmentation methods (including new ones) that have successfully been used in traffic sign recognition. The methods presented can be classified into color-space thresholding, edge detection, and chromatic/achromatic decomposition. Our support vector machine (SVM) segmentation method and speed enhancement using a lookup table (LUT) have also been tested. The best algorithm will be the one that yields the best global results throughout the whole recognition process, which comprises three stages: 1) segmentation; 2) detection; and 3) recognition. Thus, an evaluation method, which consists of applying the entire recognition system to a set of images with at least one traffic sign, is attempted while changing the segmentation method used. This way, it is possible to observe modifications in performance due to the kind of segmentation used. The results lead us to conclude that the best methods are those that are normalized with respect to illumination, such as RGB or Ohta Normalized, and there is no improvement in the use of Hue Saturation Intensity (HSI)-like spaces. In addition, an LUT with a reduction in the less-significant bits, such as that proposed here, improves speed while maintaining quality. SVMs used in color segmentation give good results, but some improvements are needed when applied to achromatic colors.
Cloud dependability analysis: Characterizing Google cluster infrastructure reliability

Cloud computing data centers offer high available and reliable infrastructures for hosting critical applications and data. These data centers host hundreds of thousands physical machines to response to incoming workload as job executing. In this paper, we analyze the Google cloud cluster properties to investigate the relationship among machine failures, updates, and job failures. We present the statistical properties of Google machines and job failures and attempt to correlate them during a 29-day period behave. We classify the machine and job failures per day and represent a reliability model for Google cluster machines using the Continues Time Markov Chains.
FURTHER ANALYSIS OF THE HIPPOCAMPAL AMNESIC SYNDROME : 14-YEAR FOLLOW-UP STUDY OF H . M . *

-The report attempts to delineatecertain residual learning capacities of H.M., a young man who became amnesic in 1953 following a bilateral removal in the hippocampal zone. In addition to being able to acquire new motor skills (CoRKIN ), this patient shows some evidence of perceptual learning. He also achieves some retention of very simple visual and tactual mazes in which the sequence of required turns is short enough to fit into his immediate memory span; even then, the rate of acquisition is extremely slow. These vestigial abilities, which have their occasional parallels in the patient's everyday life, are assessed against the background of his continuing profound amnesia for most on-going events, an amnesia that persists in spite of above-average intelligence and superior performance on many perceptual tasks. THE PRESENT repor t has three aims. In the first place, it describes the persis tent features of a severe amnesic syndrome acquired 14 years ago, fol lowing bi la tera l mesial t empora l lobec tomy (ScoVILLE [28]). Secondly, the repor t a t tempts to give further substance to our previous ly held belief tha t the pa t ien t ' s perceptua l and intel lectual capacit ies remain intact , as mani fes ted by no rma l or super ior pe r tb rmance on a fair ly wide range o f exper imental tasks. Thirdly , we are explor ing the na ture o f the m e m o r y detect in some detai l by trying to discover which learning tasks the pat ient can master , as c o m p a r e d with those on which he always fails. I N T E R V A L H I S T O R Y Since the onset of his amnes ia in 1953, we have twice had the oppor tun i ty o f br inging this pa t ien t under intensive observat ion. In 1962, he spent one week at the Mont rea l Neuro log ica l Inst i tute, and most results o f these and earl ier examinat ions have a l ready been repor ted (CoRK~N ; MILNER [16, 18, 19]). Extensive testing was again carr ied out in 1966, dur ing a two-week admiss ion to the Clinical Research Center at M.I .T. Findings ob ta ined dur ing that per iod, supplemented later by visits to the pa t ien t ' s home, form the basis of the present report .  From the Montreal Neurological Institute and from the Psychophysiological Laboratory and Clinical Research Center, M.I.T. "Ilais work was supported in part by the Medical Research Council of Canada, and in part by grants to H.-L. TEtmER from the John A. Hartford Foundation, NASA, and the National Institutes of Health (under MH-05673, and a Clinical Center Grant, FR88). The follow-up study of K.M. was supported by United States Public Health Small Grant M5774A to BRENDA MILNER.
Guidance for the evaluation and treatment of hereditary and acquired thrombophilia

Thrombophilias are hereditary and/or acquired conditions that predispose patients to thrombosis. Testing for thrombophilia is commonly performed in patients with venous thrombosis and their relatives; however such testing usually does not provide information that impacts management and may result in harm. This manuscript, initiated by the Anticoagulation Forum, provides clinical guidance for thrombophilia testing in five clinical situations: following 1) provoked venous thromboembolism, 2) unprovoked venous thromboembolism; 3) in relatives of patients with thrombosis, 4) in female relatives of patients with thrombosis considering estrogen use; and 5) in female relatives of patients with thrombosis who are considering pregnancy. Additionally, guidance is provided regarding the timing of thrombophilia testing. The role of thrombophilia testing in arterial thrombosis and for evaluation of recurrent pregnancy loss is not addressed. Statements are based on existing guidelines and consensus expert opinion where guidelines are lacking. We recommend that thrombophilia testing not be performed in most situations. When performed, it should be used in a highly selective manner, and only in circumstances where the information obtained will influence a decision important to the patient, and outweigh the potential risks of testing. Testing should not be performed during acute thrombosis or during the initial (3-month) period of anticoagulation.
Design and implement of QPSK modem based on FPGA

This paper presents a method to designs QPSK modulator and demodulator of a spread spectrum system, which use field programmable device. The method uses the tool of Quartus II of American Altera Co. The whole system is divided into several small models based on top-down design method, and using VHDL hardware description language to design each model. The direct digital synthesis (DDS) principle is briefly presented and used to design orthogonal cosine signal module. In demodulator, we use the low pass FIR filtering to filter high frequency component. The QPSK module is ultimately implemented on the FPGA device. The whole system has been simulated in the Quartus II7.2 simulation environment and successfully downloaded to the chip of the Cyclone II EP2C5F256C6. The operating results and the theoretical results are consistent, so it's verified the correctness of this design. The results showed that the proposed method can greatly improve the developing efficiency, shorten developing period and reduce costs.
Borrower Decision Aid for people-to-people lending

a r t i c l e i n f o In setting up, and bidding in online auctions, people face difficult strategic decisions. In this study, a Borrower Decision Aid is introduced, which will help formalize the decision making process of the sellers, or borrowers in this case, in one particular P2P loan auction site, Prosper.com. The vast amount of real-life bidding data available in this online auction enables us to build new kinds of tools for decision makers. The Borrower Decision Aid helps the borrower to quantify her strategic options, such as starting interest rate, and the amount of loan requested. We identify which variables concerning the borrower are related to the probability of successfully securing a loan and the final interest rate.