Miracle-dz/UniFAR-FTUs huggingface dataset:
A Rule-Based Approach to Extracting Relations from Music Tidbits

This paper presents a rule based approach to extracting relations from unstructured music text sources. The proposed approach identifies and disambiguates musical entities in text, such as songs, bands, persons, albums and music genres. Candidate relations are then obtained by traversing the dependency parsing tree of each sentence in the text with at least two identified entities. A set of syntactic rules based on part of speech tags are defined to filter out spurious and irrelevant relations. The extracted entities and relations are finally represented as a knowledge graph. We test our method on texts from songfacts.com, a website that provides tidbits with facts and stories about songs. The extracted relations are evaluated intrinsically by assessing their linguistic quality, as well as extrinsically by assessing the extent to which they map an existing music knowledge base. Our system produces a vast percentage of linguistically correct relations between entities, and is able to replicate a significant part of the knowledge base.
Towards a realistic dielectric tissue model: A multiscale approach

In the past, mainly analytical mixing formulas were used for modeling of dielectric properties of biological cells. General drawbacks of such formulas are the restriction to simple shapes and small cellular volume fractions. Assuming cell suspensions or tissues being quasi-periodic the problem size can be reduced to a cubic unit cell containing a single biological cell. Under this assumption numerical, e.g. Finite- Element models of such unit cells provide effective dielectric parameters for the entire tissue or cell suspension. In this work a flexible shape parametrization method allowing for a realistic representation of biological cells is applied to eight different cell types. A non-axisymmetric columnar epithelium cell occurring e.g. in the epidermis is chosen as an example. Numerical simulations of the columnar cell exposed to a time-harmonic electric field are performed for two different, high volume fractions, followed by the extraction of effective dielectric parameters of the bulk material. The simulation results are compared to two analytical approximations for ellipsoidal particles. The results suggest, that the calculation of effective dielectric properties of arbitrarily shaped cells in the frequency range between 100kHz and 1GHz requires at least a numerical cell model.
Biomarkers in Lung Diseases : from Pathogenesis to Prediction to New Therapies Macrophage migration inhibitory factor deficiency in chronic obstructive pulmonary disease

Maor Sauler, Lin Leng, Mark Trentalange, Maria Haslip, Peiying Shan, Marta Piecychna, Yi Zhang, Nathaniel Andrews, Praveen Mannam, Heather Allore, Terri Fried, Richard Bucala, and Patty J. Lee Section of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Yale University School of Medicine, New Haven, Connecticut; Section of Rheumatology, Department of Medicine, Yale University, New Haven, Connecticut; and Section of Geriatric Medicine: Claude D. Pepper Older Americans Independent Center, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut
Dynamic interdiction games on PERT networks

In a PERT network interdiction game one player tries to get a project, represented as a PERT graph, finished as quickly as possible by allocating resources, expediting the project’s tasks and choosing between different technologies to use. At the same time a second player aims to maximally delay the project’s completion date by carefully choosing which tasks to disrupt. We develop a novel continuous time model of the game, showing that it can be solved as an MDP. We then implement an efficient exact dynamic programming algorithm for it and propose extensions involving implementation uncertainty and crashing with renewable resources. In addition, we demonstrate that approximate techniques for solving our models can alleviate the ‘the curse of dimensionality’.
Stationary Vehicles Detection on Smart Highways and Roads using Spatio-temporal Tracking

Where traditional motorways contain hard shoulders to provide refuge for broken-down vehicles, smart motorways instead use live outer lane to ease congestion. The live lanes can be closed due to accidents or breakdowns which is communicated to other road users through overhead gantry signs. This can only occur if the traffic management control center is made aware of the stationary vehicle(s), through either notification via phone call or Motorway Incident Detection and Automatic Signaling (MIDAS) induction loop technology. Alternatively, radar-based stopped vehicle detection is used to identify non-moving objects on the highway. However, this technology is unable to recognize objects or distinguish between congestion and break down etc., which leads to generate false alarms. For the first time, we propose a fully autonomous computer vision and deep learning-based solution to detect stationary vehicles on highways and local roads. We employ deep transfer learning to build a custom-trained vehicle detection model using a newly prepared dataset comprising over 105,000 annotated vehicle instances. DeepSort algorithm is employed for real-time vehicle tracking through associating instances between time series frames, followed by a rule-based algorithm to identify the current state of detected vehicles. Experimental outcomes show our approach as outperforming the state-of-the-art methods in terms of efficient and reliable detection of stationary vehicles (with 98.3% accuracy) as well as distinguish them from congestions when evaluated over video streams captured in realistic dynamic and diverse conditions.
Balancing new product quality and innovativeness through learning and knowledge sharing in NPD teams

To satisfy customers’ and other stakeholders’ requirements, firms must develop new products that are at the same time innovative and of high quality. A common belief among researchers and practitioners is that a strong quality focus has an adverse effect on innovativeness, but recent research seems to point in a different direction. This article challenges this common belief and investigates the role o f cognitive team processes in developing high quality products without jeopardizing their innovativeness. Using the idea of the ambidextrous organization, we argue that simultaneously achieving high product quality (through exploitation of existing knowledge) and high product innovativeness (through exploration of new knowledge) is indeed possible when cross-functional new product development (NPD) teams are encouraged to concurrently learn and share knowledge. In a field study, data were collected by means of an online survey. Data obtained from members of 105 NPD teams were analyzed using structural equation modeling in the SmartPLS implementation. The study shows that in a NPD context, team learning, which has traditionally been associated with exploration, has significant positive effects on new product quality and innovativeness. Furthermore, knowledge sharing was found to positively affect quality in a new product development context. Surprisingly, knowledge sharing, which is often exclusively associated with the exploitation o f existing knowledge, was also found to have a weak but positive effect on product innovativeness. No inverse effects were found between learning and knowledge sharing. Knowing that is possible to develop high quality products that are innovative at the 2
A Method for Calibrating Diffusion Gradients in Diffusion Tensor Imaging

Objective: To calibrate and correct the gradient errors including gradient amplitude scaling errors, background/imaging gradients, and residual gradients in diffusion tensor imaging (DTI). Methods: A calibration protocol using an isotropic phantom was proposed. Gradient errors were estimated by using linear regression analyses on quadratic functions of diffusion gradients along 3 orthogonal directions. A 6-element total effective scaling vector is generated from the calibration protocol to retrospectively correct gradient errors in DTI experiments. Results: The accuracy of the calibration protocol was within 1% or less in estimating gradient scaling errors. On both the brain study and the computer simulations, the retrospective correction minimized undesirable estimate biases of DTI measurements due to gradient errors. Conclusion: The protocol and retrospective correction are shown to be effective. The method may be used for prospective correction if actual diffusion-gradient waveforms are available. The methodology is expandable to general diffusion imaging schemes.
Image classification with a deep network model based on compressive sensing

To simplify the parameter of the deep learning network, a cascaded compressive sensing model “CSNet” is implemented for image classification. Firstly, we use cascaded compressive sensing network to learn feature from the data. Secondly, CSNet generates the feature by binary hashing and block-wise histograms. Finally, a linear SVM classifier is used to classify these features. The experiments on the MNIST dataset indicate that higher classification accuracy can be obtained by this algorithm.
Towards Situation‐Aware Affordances: An Experimental Study

In the future interactive environments will offer more and more functionality. With this increased functionality the need for mechanisms enabling easy system exploration will grow. Designing object affordances is a classic way of influencing explorability. However, affordances can not give clues about the combined use of objects in tasks with higher complexity. Equally, they can not adapt themselves to the situation at hand. In this paper we present and evaluate a system that extends the idea of classic affordances by presenting small amounts of instructions at the right time and at the right place. We evaluate this concept using the example of a standard flatpack wardrobe extended with light emitting diodes (LED’s) on each board. These LED’s display the assembly instructions in a proactive way by adapting the information with each movement the user makes. The experiment presented compares the use of paper instructions with the proposed LED based instructions. As result, LED’s proved sufficient to even produce a measurable time gain. To test intuitivity and usability of the instructions a comparison between instructions in different modalities is carried out.
Geographies of Trust

Generalized social trust is an important component of social capital and has been linked to a variety of individual- and community-level outcomes, including low crime rates, effective government, and healthy and happy citizens. Drawing on a multicommunity survey conducted in several American towns and cities in 2002, the authors examine the individual and contextual origins of general social trust using the techniques of Hierarchical Linear Modeling. Based on prevailing theoretical understandings of social trust, the authors posit a comprehensive model to account for the contextual variation that remains after controlling for individual-level variables. Two community-level variables, voter turnout and commute times, emerge as important contextual predictors of social trust. The authors explore community attachment as a potential mediator of these effects, finding that it partially mediates the impact of commuting but not voter turnout, results consistent with their distinction between “experiential” and “cultural” theories of social trust formation.
Non-White, No More: Effect Coding as an Alternative to Dummy Coding With Implications for Higher Education Researchers

The purpose of this article is to describe effect coding as an alternative quantitative practice for analyzing and interpreting categorical, racebased independent variables in higher education research. Unlike indicator (dummy) codes that imply that one group will be a reference group, effect codes use average responses as a means for interpreting information. This technique is especially appropriate for examining race, as such a process enables raced subgroups to be compared to each other and does not position responses of any raced group as normative— the standard against which all other race effects are interpreted. The issues raised here apply in any research context where a categorical variable without a natural reference group (e.g., college major) is a potential predictor in a regression model.
Matrix-vector Multiplication on a Fixed Size Unidirectional Systolic Array

In this paper, the problem of multiplication of matrix A=(aik)nxn by vector b macr= (bk)nxl unidirectional linear systolic array (ULSA) comprised of ples[n/2] processing elements is considered. To match the dimension of matrix A to the ULSA size, the partitioning of the matrix A into quasidiagonal blocks is performed. Each block contains p quasidiagonals. In order to decrease the computation time the reordering of elements of block matrices and resulting vector c is performed. The global structure of memory interface subsystem (MIS) is proposed. The MIS, located between the host and ULSA, provides corresponding data transfers to/from ULSA. Finally, the performance of synthesized ULSA is discussed and compared with fixed size bidirectional systolic array.
p53 Family and Cellular Stress Responses in Cancer

p53 is an important tumor suppressor gene, which is stimulated by cellular stress like ionizing radiation, hypoxia, carcinogens, and oxidative stress. Upon activation, p53 leads to cell-cycle arrest and promotes DNA repair or induces apoptosis via several pathways. p63 and p73 are structural homologs of p53 that can act similarly to the protein and also hold functions distinct from p53. Today more than 40 different isoforms of the p53 family members are known. They result from transcription via different promoters and alternative splicing. Some isoforms have carcinogenic properties and mediate resistance to chemotherapy. Therefore, expression patterns of the p53 family genes can offer prognostic information in several malignant tumors. Furthermore, the p53 family constitutes a potential target for cancer therapy. Small molecules (e.g., Nutlins, RITA, PRIMA-1, and MIRA-1 among others) have been objects of intense research interest in recent years. They restore pro-apoptotic wild-type p53 function and were shown to break chemotherapeutic resistance. Due to p53 family interactions small molecules also influence p63 and p73 activity. Thus, the members of the p53 family are key players in the cellular stress response in cancer and are expected to grow in importance as therapeutic targets.
Influence of mating systems and selection intensity on the extent of inbreeding and genetic gain in the Slovak Pinzgau cattle.

The aim of the present paper was to simulate the scenarios for a maximum avoidance of inbreed- ing (MAI) mating strategy, and compare it with a random mating alternative, with the main focus on inbreed- ing and development of the genetic gain. The parameters of the simulation were based on the structure of the Slovak Pinzgau active population of 2868 animals (930 purebred cows). The selection under a total merit index (TMI) was simulated, covering the milk, survival, and live weight breeding value estimation results. The herit - ability of TMI (h 2 = 0.09) was estimated using a REML single trait animal model. Alternatives assumed a closed population structure, fixed number of mating per parent, and equal use of sires in insemination. Animals in generation 0 were set as founders without pedigree information. In separate simulation runs, the number of sires of sires was set at 2, 4, 5 or 10 mated with 40 dams of sires in all cases. The sex ratio of the offspring was assumed to be 50/50 male/female. Twenty consecutive generations were simulated for both random and maxi- mum avoidance of inbreeding mating, which resulted in a total of 8 scenarios. Significant positive differences in genetic gain were observed in the MAI mating system with 2 (0.74**), 4 (0.24**), 5 (0.13**) or 10 (0.09**) sires in comparison to random mating design. When using MAI, significantly lower inbreeding was observed with 2 (5.44**), 4 (3.18**), 5 (2.43**) or 10 (1.16**) sires. Simulation results showed that the use of a maximum avoid- ance of inbreeding mating strategy would lead to significantly decreased rates of inbreeding while maintaining suitable levels of genetic gain in the Slovak Pinzgau population.
Ieee/acm Transactions on Computational Biology and Bioinformatics 1 Network-based Drug-target Interaction Prediction with Probabilistic Soft Logic

—Drug-target interaction studies are important because they can predict drugs' unexpected therapeutic or adverse side effects. In silico predictions of potential interactions are valuable and can focus effort on in vitro experiments. We propose a prediction framework that represents the problem using a bipartite graph of drug-target interactions augmented with drug-drug and target-target similarity measures and makes predictions using probabilistic soft logic (PSL). Using probabilistic rules in PSL, we predict interactions with models based on triad and tetrad structures. We apply (blocking) techniques that make link prediction in PSL more efficient for drug-target interaction prediction. We then perform extensive experimental studies to highlight different aspects of the model and the domain, first comparing the models with different structures and then measuring the effect of the proposed blocking on the prediction performance and efficiency. We demonstrate the importance of rule weight learning in the proposed PSL model and then show that PSL can effectively make use of a variety of similarity measures. We perform an experiment to validate the importance of collective inference and using multiple similarity measures for accurate predictions in contrast to non-collective and single similarity assumptions. Finally, we illustrate that our PSL model achieves state-of-the-art performance with simple, interpretable rules and evaluate our novel predictions using online datasets.
Predicting Shot-Level SRAM Read/Write Margin Based on Measured Transistor Characteristics

An SRAM-array test structure provides the capability of directly measuring the characteristics of each transistor and the read/write metrics for each static random access memory (SRAM) cell in the array. However, the total test time of measuring the read/write metrics takes longer than that of measuring each transistor's characteristics. This paper presents a model-fitting framework to predict the average read/write metrics of the SRAM cells in a lithography shot using only the measured transistor characteristics. The proposed framework is validated through the measurement result of 4750 samples of a 128-bit SRAM-array test structure implemented in a United Microelectronics Corporation 28-nm process technology. The experimental results show that the learned models can achieve at least 97.77% R-square on fitting the shot-level read static noise margin, write margin, and read current based on 2375-sample testing data.
Connectivity pattern modeling of motor imagery EEG

In this paper, the functional connectivity network of motor imagery based on EEG is investigated to understand brain function during motor imagery. In particular, partial directed coherence and directed transfer function measurements are applied to multi-channel EEG data to find out event related connectivity pattern with the direction and strength. The t-test is applied to these connectivity measurements to compare the network between motor imagery and the rest state. The possible relationship between this connectivity pattern and subjects performances are discussed. Based on the Granger causality analysis, a feature extraction method is proposed to compensate for nonstationarity in data. By attenuating the time-lagged correlation, this feature extraction method based on the multi-variate autoregression model is proposed to reduce the effects of noises caused by time propagation. The validity of the proposed method is verified through experimental studies with a two-class dataset, and significant improvement in term of classification accuracy is achieved.
Mining Pinyin-to-character conversion rules from large-scale corpus: a rough set approach

The paper introduces a rough set technique for solving the problem of mining Pinyin-to-character (PTC) conversion rules. It first presents a text-structuring method by constructing a language information table from a corpus for each pinyin, which it will then apply to a free-form textual corpus. Data generalization and rule extraction algorithms can then be used to eliminate redundant information and extract consistent PTC conversion rules. The design of our model also addresses a number of important issues such as the long-distance dependency problem, the storage requirements of the rule base, and the consistency of the extracted rules, while the performance of the extracted rules as well as the effects of different model parameters are evaluated experimentally. These results show that by the smoothing method, high precision conversion (0.947) and recall rates (0.84) can be achieved even for rules represented directly by pinyin rather than words. A comparison with the baseline tri-gram model also shows good complement between our method and the tri-gram language model.
Biochemical and Neuroimaging Studies in Subjective Cognitive Decline: Progress and Perspectives

Neurodegeneration due to Alzheimer's disease (AD) can progress over decades before dementia becomes apparent. Indeed, patients with mild cognitive impairment (MCI) already demonstrate significant lesion loads. In most cases, MCI is preceded by subjective cognitive decline (SCD), which is applied to individuals who have self‐reported memory‐related complaints and has been associated with a higher risk of future cognitive decline and conversion to dementia. Based on the schema of a well‐received model of biomarker dynamics in AD pathogenesis, it has been postulated that SCD symptoms may result from compensatory changes in response to β‐amyloid accumulation and neurodegeneration. Although SCD is considered a prodromal stage of MCI, it is also a common manifestation in old age, independent of AD, and the predictive value of SCD for AD pathology remains controversial. Here, we provide a review focused on the contributions of cross‐sectional and longitudinal analogical studies of biomarkers and neuroimaging evidence in disentangling under what conditions SCD may be attributable to AD pathology. In conclusion, there is promising evidence indicating that clinicians should be able to differentiate pre‐AD SCD based on the presence of pathophysiological biomarkers in cerebrospinal fluid (CSF) and neuroimaging. However, this neuroimaging approach is still at an immature stage without an established rubric of standards. A substantial amount of work remains in terms of replicating recent findings and validating the clinical utility of identifying SCD.