nils-herrmann/clean_scirepeval huggingface dataset:
Memory for serial order across domains: An overview of the literature and directions for future research.

From vocabulary learning to imitating sequences of motor actions, the ability to plan, represent, and recall a novel sequence of items in the correct order is fundamental for many verbal and nonverbal higher level cognitive activities. Here we review phenomena of serial order documented across the verbal, visual, and spatial short-term memory domains and interpret them with reference to the principles of serial order and ancillary assumptions instantiated in contemporary computational theories of memory for serial order. We propose that functional similarities across domains buttress the notion that verbal, visual, and spatial sequences are planned and controlled by a competitive queuing (CQ) mechanism in which items are simultaneously active in parallel and the strongest item is chosen for output. Within the verbal short-term memory CQ system, evidence suggests that serial order is represented via a primacy gradient, position marking, response suppression, and cumulative matching. Evidence further indicates that output interference operates during recall and that item similarity effects manifest during both serial order encoding and retrieval. By contrast, the principles underlying the representation of serial order in the visual and spatial CQ systems are unclear, largely because the relevant studies have yet to be performed. In the spatial domain, there is some evidence for a primacy gradient and position marking, whereas in the visual domain there is no direct evidence for either of the principles of serial order. We conclude by proposing some directions for future research designed to bridge this and other theoretical gaps in the literature.
Emerging Updates on the Posterior Cruciate Ligament: A Review of the Current Literature.

The posterior cruciate ligament (PCL) is recognized as an essential stabilizer of the knee. However, the complexity of the ligament has generated controversy about its definitive role and the recommended treatment after injury. A proper understanding of the functional role of the PCL is necessary to minimize residual instability, osteoarthritic progression, and failure of additional concomitant ligament graft reconstructions or meniscal repairs after treatment. Recent anatomic and biomechanical studies have elucidated the surgically relevant quantitative anatomy and confirmed the codominant role of the anterolateral and posteromedial bundles of the PCL. Although nonoperative treatment has historically been the initial treatment of choice for isolated PCL injury, possibly biased by the historically poorer objective outcomes postoperatively compared with anterior cruciate ligament reconstructions, surgical intervention has been increasingly used for isolated and combined PCL injuries. Recent studies have more clearly elucidated the biomechanical and clinical effects after PCL tears and resultant treatments. This article presents a thorough review of updates on the clinically relevant anatomy, epidemiology, biomechanical function, diagnosis, and current treatments for the PCL, with an emphasis on the emerging clinical and biomechanical evidence regarding each of the treatment choices for PCL reconstruction surgery. It is recommended that future outcomes studies use PCL stress radiographs to determine objective outcomes and that evidence level 1 and 2 studies be performed to assess outcomes between transtibial and tibial inlay reconstructions and also between single- and double-bundle PCL reconstructions.
Lies, Damn Lies, and Fake News

In announcing the news that “post-truth” is the Oxford Dictionaries’ 2016 word of the year, the Chicago Tribune declared that “Truth is dead. Facts are passé.”1 Politicians have shoveled this mantra our direction for centuries, but during this past presidential election, they really rubbed our collective faces in it. To be fair, the word “post” isn’t to be taken to mean “after,” as in its normal sense, but rather as “irrelevant.” Careful observers of the recent US political campaigns came to appreciate this difference. Candidates spewed streams of rhetorical effluent that didn’t even pretend to pass the most perfunctory fact-checking smell test. As the Tribune noted, far too many voters either didn’t notice or didn’t care. That said, recognizing an unwelcome phenomenon isn’t the same as legitimizing it, and now the Oxford Dictionaries group has gone too far toward the latter. They say “post-truth” captures the “ethos, mood or preoccupations of [2016] to have lasting potential as a word of cultural significance.”1 I emphatically disagree. I don’t know what post-truth did capture, but it didn’t capture that. We need a phrase for the 2016 mood that’s a better fit. I propose the term “gaudy facts,” for it emphasizes the garish and tawdry nature of the recent political dialog. Further, “gaudy facts” has the advantage of avoiding the word truth altogether, since there’s precious little of that in political discourse anyway. I think our new term best captures the ethos and mood of today’s political delusionists. There’s no ground truth data in sight, all claims are imaginary and unsupported without pretense of facts, and distortion is reality. This seems to fit our present experience well. The only tangible remnant of reality that isn’t subsumed under our new term is the speakers’ underlying narcissism, but at least we’re closer than we were with “post-truth.” We need to forever banish the association of the word “truth” with “politics”—these two terms just don’t play well with each other. Lies, Damn Lies, and Fake News
Inducing Symbolic Rules from Entity Embeddings using Auto-encoders

Vector space embeddings can be used as a tool for learning semantic relationships from unstructured text documents. Among others, earlier work has shown how in a vector space of entities (e.g. different movies) fine-grained semantic relationships can be identified with directions (e.g. more violent than). In this paper, we use stacked denoising auto-encoders to obtain a sequence of entity embeddings that model increasingly abstract relationships. After identifying directions that model salient properties of entities in each of these vector spaces, we induce symbolic rules that relate specific properties to more general ones. We provide illustrative examples to demonstrate the potential of this ap-
Assessing canopy PRI for water stress detection with diurnal airborne imagery

A series of diurnal airborne campaigns were conducted over an orchard field to assess the canopy Photochemical Reflectance Index (PRI) as an indicator of water stress. Airborne campaigns over two years were conducted with the Airborne Hyperspectral Scanner (AHS) over an orchard field to investigate changes in PRI, in the Transformed Chlorophyll Absorption in Reflectance Index (TCARI) normalized by the Optimized SoilAdjusted Vegetation Index (OSAVI) (TCARI/OSAVI), and in the Normalized Difference Vegetation Index (NDVI) as function of field-measured physiological indicators of water stress, such as stomatal conductance, stem water potential, steady-state fluorescence, and crown temperature. The AHS sensor was flown at three times on each 2004 and 2005 years, collecting 2 m spatial resolution imagery in 80 spectral bands in the 0.43– 12.5 μm spectral range. Indices PRI, TCARI/OSAVI, and NDVI were calculated from reflectance bands, and thermal bands were assessed for the retrieval of land surface temperature, separating pure crowns from shadows and sunlit soil pixels. The Photochemical Reflectance Index, originally developed for xanthophyll cycle pigment change detection was calculated to assess its relationship with water stress at a canopy level, and more important, to assess canopy structural and viewing geometry effects for water stress detection in diurnal airborne experiments. The FLIGHT 3D canopy reflectance model was used to simulate the bi-directional reflectance changes as function of the viewing geometry, background and canopy structure. This manuscript demonstrates that the airborne-level PRI index is sensitive to the de-epoxidation of the xanthophyll pigment cycle caused by water stress levels, but affected diurnally by the confounding effects of BRDF. Among the three vegetation indices calculated, only airborne PRI demonstrated sensitivity to diurnal changes in physiological indicators of water stress, such as canopy temperature minus air temperature (Tc–Ta), stomatal conductance (G), and stem water potential (ψ) measured in the field at the time of each image acquisition. No relationships were found from the diurnal experiments between NDVI and TCARI/OSAVI with the tree-measured physiological measures. FLIGHT model simulations of PRI demonstrated that PRI is highly affected by the canopy structure and background.
Large scale reverse image search - A method comparison for almost identical image retrieval

In this paper, we presented our study and benchmark on Reverse Image Search (RIS) methods, with a special focus on finding almost similar images in a very large image collection. In our framework we concentrate our study on radius (threshold) based image search methods. We focused our study on perceptual hash based solutions for their scalability, but other solutions seem to give also good results. We studied the speed and the accuracy (precision/recall) of several existing image features. We also proposed a two-layer method that combines a fast but not very precise method with a slower but more accurate method to provide a scalable and precise RIS system. MOTS-CLES : recherche d’images inversé, pHash, optimisation, SI images
Role of melatonin in the regulation of human circadian rhythms and sleep.

The circadian rhythm of pineal melatonin is the best marker of internal time under low ambient light levels. The endogenous melatonin rhythm exhibits a close association with the endogenous circadian component of the sleep propensity rhythm. This has led to the idea that melatonin is an internal sleep "facilitator" in humans, and therefore useful in the treatment of insomnia and the readjustment of circadian rhythms. There is evidence that administration of melatonin is able: (i) to induce sleep when the homeostatic drive to sleep is insufficient; (ii) to inhibit the drive for wakefulness emanating from the circadian pacemaker; and (iii) induce phase shifts in the circadian clock such that the circadian phase of increased sleep propensity occurs at a new, desired time. Therefore, exogenous melatonin can act as soporific agent, a chronohypnotic, and/or a chronobiotic. We describe the role of melatonin in the regulation of sleep, and the use of exogenous melatonin to treat sleep or circadian rhythm disorders.
Spatial Predictive Control for Agile Semi-Autonomous Ground Vehicles

This paper presents a formulation to the obstacle avoidance problem for semi-autonomous ground vehicles. The planning and tracking problems have been divided into a two-level hierarchical controller. The high level solves a nonlinear model predictive control problem to generate a feasible and obstacle free path. It uses a nonlinear vehicle model and utilizes a coordinate transformation which uses vehicle position along a path as the independent variable. The low level uses a higher fidelity model and solves the MPC problem with a sequential quadratic programming approach to track the planned path. Simulations show the method’s ability to safely avoid multiple obstacles while tracking the lane centerline. Experimental tests on a semi-autonomous passenger vehicle driving at high speed on ice show the effectiveness of the approach.
Depthwise Separable Convolutions for Neural Machine Translation

Depthwise separable convolutions reduce the number of parameters and computation used in convolutional operations while increasing representational efficiency. They have been shown to be successful in image classification models, both in obtaining better models than previously possible for a given parameter count (the Xception architecture) and considerably reducing the number of parameters required to perform at a given level (the MobileNets family of architectures). Recently, convolutional sequence-to-sequence networks have been applied to machine translation tasks with good results. In this work, we study how depthwise separable convolutions can be applied to neural machine translation. We introduce a new architecture inspired by Xception and ByteNet, called SliceNet, which enables a significant reduction of the parameter count and amount of computation needed to obtain results like ByteNet, and, with a similar parameter count, achieves better results. In addition to showing that depthwise separable convolutions perform well for machine translation, we investigate the architectural changes that they enable: we observe that thanks to depthwise separability, we can increase the length of convolution windows, removing the need for filter dilation. We also introduce a new "super-separable" convolution operation that further reduces the number of parameters and computational cost of the models.
Improved Word Embeddings with Implicit Structure Information

Distributed word representation is an efficient method for capturing semantic and syntactic word relations. In this work, we introduce an extension to the continuous bag-of-words model for learning word representations efficiently by using implicit structure information. Instead of relying on a syntactic parser which might be noisy and slow to build, we compute weights representing probabilities of syntactic relations based on the Huffman softmax tree in an efficient heuristic. The constructed “implicit graphs” from these weights show that these weights contain useful implicit structure information. Extensive experiments performed on several word similarity and word analogy tasks show gains compared to the basic continuous bag-of-words model.
A study on the fault diagnosis analysis of variable reluctance resolver for electric vehicle

Variable reluctance (VR) resolver is widely used in traction motor for battery electric vehicle as well as hybrid electric vehicle as a rotor position sensor. VR resolver generates absolute position signal by using resolver-to-digital converter (RDC) in order to deliver exact position of permanent magnets in a rotor of traction motor to motor controller. This paper deals with fault diagnosis of VR resolver by using co-simulation analysis with RDC for position angle detection. As fault conditions, eccentricity of VR resolver, short circuit condition of excitation coil and output signal coils, and material problem of silicon steel in a view point of permeability are considered. 2D FEM is used for the output signal waveforms of SIN, COS and these waveforms are converted into absolute position angle by using the algorithm of RDC. For the verification of proposed analysis results, experiment on fault conditions was conducted and compared with simulation ones.
EdgeChain: Blockchain-based Multi-vendor Mobile Edge Application Placement

The state-of-the-art mobile edge applications are generating intense traffic and posing rigorous latency requirements to service providers. While resource sharing across multiple service providers today requires a centralized, trusted repository maintained by all parties for service providers to share status. We propose EdgeChain, a blockchain-based architecture to make mobile edge application placement decisions for multiple service providers, based on a stochastic programming problem minimizing the placement cost for mobile edge application placement scenarios. All placement transactions are stored on the blockchain and are traceable by every mobile edge service provider and application vendor who consumes resources at the mobile edge.
CoLink: An Unsupervised Framework for User Identity Linkage

Nowadays, it is very common for one person to be in different social networks. Linking identical users across different social networks, also known as the User Identity Linkage (UIL) problem, is fundamental for many applications. There are two major challenges in the UIL problem. First, it’s extremely expensive to collect manually linked user pairs as training data. Second, the user attributes in different networks are usually defined and formatted very differently which makes attribute alignment very hard. In this paper we propose CoLink, a general unsupervised framework for the UIL problem. CoLink employs a co-training algorithm, which manipulates two independent models, the attribute-based model and the relationship-based model, and makes them reinforce each other iteratively in an unsupervised way. We also propose the sequence-to-sequence learning as a very effective implementation of the attribute-based model, which can well handle the challenge of the attribute alignment by treating it as a machine translation problem. We apply CoLink to a UIL task of mapping the employees in an enterprise network to their LinkedIn profiles. The experiment results show that CoLink generally outperforms the state-of-the-art unsupervised approaches by an F1 increase over 20%.
Exploring Text Links for Coherent Multi-Document Summarization

Summarization aims to represent source documents by a shortened passage. Existing methods focus on the extraction of key information, but often neglect coherence. Hence the generated summaries suffer from a lack of readability. To address this problem, we have developed a graph-based method by exploring the links between text to produce coherent summaries. Our approach involves finding a sequence of sentences that best represent the key information in a coherent way. In contrast to the previous methods that focus only on salience, the proposed method addresses both coherence and informativeness based on textual linkages. We conduct experiments on the DUC2004 summarization task data set. A performance comparison reveals that the summaries generated by the proposed system achieve comparable results in terms of the ROUGE metric, and show improvements in readability by human evaluation.
A Dataset for Inter-Sentence Relation Extraction using Distant Supervision

This paper presents a benchmark dataset for the task of inter-sentence relation extraction. The paper explains the distant supervision method followed for creating the dataset for inter-sentence relation extraction, involving relations previously used for standard intrasentence relation extraction task. The study evaluates baseline models such as bag-of-words and sequence based recurrent neural network models on the developed dataset and shows that recurrent neural network models are more useful for the task of intra-sentence relation extraction. Comparing the results of the present work on iner-sentence relation extraction with previous work on intra-sentence relation extraction, the study suggests the need for more sophisticated models to handle long-range information between entities across sentences.
Automatic classification of patients with Alzheimer's disease from structural MRI: A comparison of ten methods using the ADNI database

Recently, several high dimensional classification methods have been proposed to automatically discriminate between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and elderly controls (CN) based on T1-weighted MRI. However, these methods were assessed on different populations, making it difficult to compare their performance. In this paper, we evaluated the performance of ten approaches (five voxel-based methods, three methods based on cortical thickness and two methods based on the hippocampus) using 509 subjects from the ADNI database. Three classification experiments were performed: CN vs AD, CN vs MCIc (MCI who had converted to AD within 18 months, MCI converters - MCIc) and MCIc vs MCInc (MCI who had not converted to AD within 18 months, MCI non-converters - MCInc). Data from 81 CN, 67 MCInc, 39 MCIc and 69 AD were used for training and hyperparameters optimization. The remaining independent samples of 81 CN, 67 MCInc, 37 MCIc and 68 AD were used to obtain an unbiased estimate of the performance of the methods. For AD vs CN, whole-brain methods (voxel-based or cortical thickness-based) achieved high accuracies (up to 81% sensitivity and 95% specificity). For the detection of prodromal AD (CN vs MCIc), the sensitivity was substantially lower. For the prediction of conversion, no classifier obtained significantly better results than chance. We also compared the results obtained using the DARTEL registration to that using SPM5 unified segmentation. DARTEL significantly improved six out of 20 classification experiments and led to lower results in only two cases. Overall, the use of feature selection did not improve the performance but substantially increased the computation times.
Low energy adaptive clustering hierarchy with deterministic cluster-head selection

This paper focuses on reducing the power consumption of wireless microsensor networks. Therefore, a communication protocol named LEACH (Low-Energy Adaptive Clustering Hierarchy) is modified. We extend LEACH’s stochastic clusterhead selection algorithm by a deterministic component. Depending on the network configuration an increase of network lifetime by about 30 % can be accomplished. Furthermore, we present a new approach to define lifetime of microsensor networks using three new metrics FND (First Node Dies), HNA (Half of the Nodes Alive), and LND (Last Node Dies).
MEASURING THE IMPLICATIONS OF SALES AND CONSUMER INVENTORY BEHAVIOR

Temporary price reductions (sales) are common for many goods and naturally result in large increases in the quantity sold. Demand estimation based on temporary price reductions may mismeasure the long-run responsiveness to prices. In this paper we quantify the extent of the problem and assess its economic implications. We structurally estimate a dynamic model of consumer choice using two years of scanner data on the purchasing behavior of a panel of households. The results suggest that static demand estimates, which neglect dynamics, (i) overestimate own-price elasticities by 30 percent, (ii) underestimate cross-price elasticities by up to a factor of 5, and (iii) overestimate the substitution to the no-purchase or outside option by over 200 percent. This suggests that policy analysis based on static elasticity estimates will underestimate price–cost margins and underpredict the effects of mergers.
Sex dimorphism of the brain in male-to-female transsexuals.

Gender dysphoria is suggested to be a consequence of sex atypical cerebral differentiation. We tested this hypothesis in a magnetic resonance study of voxel-based morphometry and structural volumetry in 48 heterosexual men (HeM) and women (HeW) and 24 gynephillic male to female transsexuals (MtF-TR). Specific interest was paid to gray matter (GM) and white matter (WM) fraction, hemispheric asymmetry, and volumes of the hippocampus, thalamus, caudate, and putamen. Like HeM, MtF-TR displayed larger GM volumes than HeW in the cerebellum and lingual gyrus and smaller GM and WM volumes in the precentral gyrus. Both male groups had smaller hippocampal volumes than HeW. As in HeM, but not HeW, the right cerebral hemisphere and thalamus volume was in MtF-TR lager than the left. None of these measures differed between HeM and MtF-TR. MtF-TR displayed also singular features and differed from both control groups by having reduced thalamus and putamen volumes and elevated GM volumes in the right insular and inferior frontal cortex and an area covering the right angular gyrus.The present data do not support the notion that brains of MtF-TR are feminized. The observed changes in MtF-TR bring attention to the networks inferred in processing of body perception.