Miracle-dz/UniFAR-FTUs huggingface dataset:
The Knowledge Filter and Economic Growth: The Role of Scientist Entrepreneurship

This paper examines the prevalence and determinants of the commercialization of research by university scientists funded by grants from the National Cancer Institute (NCI). Because the two publicly available modes of scientist commercialization – patents and Small Business Innovation Research (SBIR) grants – do not cover the full spectrum of commercializing activities undertaken by university scientists, the study also includes two additional measures obtained from detailed scientist interviews: licensing of intellectual property and starting a new firm. These measures are used to assess both the prevalence and determinants of scientist commercialization of research. In particular, the empirical findings suggest seven important insights: 1) Scientists receiving funding from the National Cancer Institute exhibit a robust propensity to commercialize their research. However, the prevalence of commercialization depends highly upon the actual mode of commercialization. Some modes of commercialization, such as patents, are more prevalent, while other modes, such as funding by the SBIR program are rarely used. 2) Scientist entrepreneurship is the sleeping giant of commercializing university research. More than one in four patenting NCI scientists have started a new firm. 3) Two paths for commercialization of scientist research are identified - the TTO route and the entrepreneurial route. Scientists who select the TTO route by commercializing their research through assigning all patents to their university TTO account for 70 percent of NCI patenting scientists. Scientists who choose the entrepreneurial route to commercialize their research, in that they do not assign patents to their university TTO, comprise 30 percent of patenting NCI scientists. 4) Social capital enhances the propensity for scientists to commercialize their research. The impact of social capital is particularly high for the commercialization mode of scientist entrepreneurship. 5) Technology Transfer Offices are found to be helpful for the mode of commercialization involving licenses. There is less evidence suggesting that they promote scientist entrepreneurship.6) For scientists who perceive that they are helped by their Technology Transfer Office, licensing is not only the most prevalent mode of commercialization, but it also is a substitute for entrepreneurship. For scientists who perceive that they are not helped by their Technology Transfer Office, entrepreneurship emergences as a much more important mode of commercialization and is complementary to licensing. 7) Scientists choosing the entrepreneurial route to commercialize their research, by not assigning patents to their university to commercialize research, tend to rely on the commercialization mode of entrepreneurship. By contrast, scientists who select the TTO route by assigning their patents to the university tend to rely on the commercialization mode of licensing.
From the Administrator Prevention of Serious and Violent Juvenile Offending

While most youth who enter the juvenile justice system never return, a small number of serious, violent juvenile offenders reoffend repeatedly , accounting for a disproportionate amount of juvenile crime. To enhance our understanding of these troubled and often dangerous youth, the Office of Juvenile Justice and Delinquency Prevention established its Study Group on Serious and Violent Juvenile Offenders. Expanding upon OJJDP's formative work on the Comprehensive Strategy for Serious, Violent, and Chronic Juvenile Offenders , the 22 juvenile justice experts who constituted the Study Group analyzed research on the development of serious and violent juvenile offending and effective interventions to prevent its recurrence. This Bulletin describes some of the developmental precursors to such offending and offers effective approaches to its prevention that are family, parent, and child focused. Examples of well-designed intervention programs are also provided. It is my hope that the information contained in this Bulletin will help communities better understand and address serious and violent juvenile offending. proximal risk factors, reviews the early developmental precursors to violent offending , and summarizes approaches to prevention. It also discusses components of intervention programs; limitations of single-focus preventions; examples of well-designed, multisystemic intervention programs that target proximal risk factors; and limitations of prevention strategies. Serious and violent juvenile (SVJ) offenders , although few in number, are responsible for a disproportionate number of crimes. The Office of Juvenile Justice and Delinquency Prevention (OJJDP) has built a research base to increase understanding of this group of offenders among policy-makers, youth service providers, and the public. OJJDP's Study Group on Serious and Violent Juvenile Offenders expanded on the Comprehensive Strategy for Serious, Violent, and Chronic Juvenile Offenders (Wilson and Howell, 1993). For 2 years, the Study Group of 22 researchers collabora-tively analyzed current research on risk factors, protective factors, and the development of SVJ offenders. This Bulletin focuses on one aspect of this work—prevention. The best predictor of antisocial adolescent behavior is early conduct problems. Most serious offenders have a history of childhood misbehavior, including antisocial behaviors such as physical aggression , conduct disorders, and disruptive, covert, oppositional, and defiant behaviors. Identifying the risk factors for these behaviors is important in developing strategies to prevent violent offending. Risk factors may include distal or community-level risk factors, such as poverty or access to guns or drugs, and proximal risk factors, such as parent management practices, deviant peer groups, or low intelligence. This Bulletin explores …
The spatial resolving power of earth resources satellites

The significance of spatial resolving power on the utility of current and future Earth resources satellites is critically discussed and the relative merits of different approaches in defining and estimating spatial resolution are outlined. It is shown that choice of a particular measure of spatial resolution depends strongly on the particular needs of the user. Several experiments have simulated the capabilities of future satellite systems by degradation of aircraft images. Surprisingly, many of these indicated that improvements in resolution may lead to a reduction in the classification accuracy of land cover types using computer assisted methods. However, where the frequency of boundary pixels is high, the converse relationship is found. Use of imagery dependent upon visual interpretation is likely to benefit more consistently from higher resolutions. Extraction of information from images will depend upon several other factors apart from spatial resolving power: these include characteristics of the terrain being sensed, the image processing methods that are applied as well as certain sensor characteristics.
Robust data driven discovery of a seismic wave equation

Despite the fact that our physical observations can often be described by derived physical laws, such as the wave equation, in many cases, we observe data that do not match the laws or have not been described physically yet. Therefore recently, a branch of machine learning has been devoted to the discovery of physical laws from data. We test this approach for discovering the wave equation from the observed spatial-temporal wavefields. The algorithm first pretrains a neural network (NN) in a supervised fashion to establish the mapping between the spatial-temporal locations (x, y, z, t) and the observation displacement wavefield function u(x, y, z, t). The trained NN serves to generate meta-data and provide the time and spatial derivatives of the wavefield (e.g., utt and uxx) by automatic differentiation. Then, a preliminary library of potential terms for the wave equation is optimized from an overcomplete library by using a genetic algorithm. We, then, use a physics-informed information criterion to evaluate the precision and parsimony of potential equations in the preliminary library and determine the best structure of the wave equation. Finally, we train the ”physics-informed” neural network to identify the corresponding coefficients of each functional term. Examples in discovering the 2D acoustic wave equation validate the feasibility and effectiveness of our implementation. We also verify the robustness of this method by testing it on noisy and sparsely acquired wavefield data.
Can I Trust the Explanations? Investigating Explainable Machine Learning Methods for Monotonic Models

In recent years, explainable machine learning methods have been very successful. Despite their success, most explainable machine learning methods are applied to black-box models without any domain knowledge. By incorporating domain knowledge, science-informed machine learning models have demonstrated better generalization and interpretation. But do we obtain consistent scientific explanations if we apply explainable machine learning methods to science-informed machine learning models? This question is addressed in the context of monotonic models that exhibit three different types of monotonicity. To demonstrate monotonicity, we propose three axioms. Accordingly, this study shows that when only individual monotonicity is involved, the baseline Shapley value provides good explanations; however, when strong pairwise monotonicity is involved, the Integrated gradients method provides reasonable explanations on average.
Ambient Processed , Water-Stable , Transistor

Over the past years carbon nanotubes (CNTs) have gathered significant attraction attributed to their unique chemical, mechanical and electronic properties1. In addition to the extraordinary electrical properties of carbon nanotubes, the ability to fabricate solution processable field effect transistors by low temperature processes has made them very appealing candidate in flexible electronics. Carbon nanotube field effect transistors (CNTFETs) which are gated by an electrolyte, are highly sought candidates for chemicaland biosensing applications. CNTs exhibit either metallic or semiconducting behavior, depending on their chirality2. When semiconducting CNTs are used as active element in the field-effect transistors (FETs)3, the as fabricated FETs behave as p-type due to the electron withdrawal effects by the adsorbed oxygen on nanotube surface4, hence an absence of electron current5. However, the capability to fabricate n-type CNTFETs along with the p-type is of high technological relevance, as this permits fabrication of complementary metal oxide semiconductor (CMOS) circuits that use both the p-type and n-type SWNTs6. The fabricated CMOS circuits offer several advantages including higher noise margin, lower power consumption, simpler circuit design and higher circuit yield7,8. Nevertheless, fabrication of n-type CNTFET preferably in a relatively simple process, and at the same time operation in aqueous environment has been a real challenge. Successful attempts have been made in the past to produce n-type CNTFETs involving removal of adsorbed oxygen by annealing in vacuum9 or an inert gas10 environment, even though n-type CNTFETs fabricated by such annealing process are reversible and on exposure to oxygen again turn to p-type. Towards finding a better solution, n-type CNTFETs were fabricated by n-doping the CNTs with electron donor materials10,11 and were further often protected by a passivation layer5. Compounds like poly(ethyleneimine) (PEI)12, benzyl viologens (BV)13,14, dimethyl-dihydro-benzoimi-dazoles (DMBI)6, dihydronicotinamide adenine dinucleotide (NADH)15, hydrazine16, decamethylcobaltocene17 have also been demonstrated in recent past as effective solution processable materials with the ability to convert p-type FETs to n-type after it is deposited on the surface of CNTs. Though these modified CNTFETs are air-stable, so far none of these systems have been demonstrated to be water stable such that the modified n-type CNTFETs can be used in aqueous-electrolyte gated configurations. The ability to use the device in aqueous solutions opens the possibility of using electrolyte-gated(EG) FETs for bio-chemical detection applications18. The main motivation to use EGFETs is their high capacitance value (μF/cm2) that exceeds any conventional high-K dielectric or any ultra-thin self assembled monolayer (SAM) serving as
Étude numérique et expérimentale du mécanisme de lubrification eX-Poro-HydroDynamique (XPHD)

La lubrification eX-Poro-HydroDynamique (XPHD) est un mecanisme de lubrification d’inspiration biomimetique. Il s’agit principalement d’un ecoulement dans un milieu poreux, dont la phase solide representee par des fibres, induit des forces elastiques de compression considerees comme negligeables par rapport aux forces hydrodynamiques generees a l’interieur du milieu poreux. L’idee essentielle de la lubrification XPHD consiste en remplacement du materiau antifriction et du film de fluide mince, traditionnellement utilise dans les solutions classiques, par une couche poreuse imbibee d’un fluide qui fournit plus grande capacite de charge. Ce type de lubrification represente une solution technologique totalement nouvelle (en rupture avec la solution classique) qui peut permettre de remplacer les lubrifiants petroliers, de creer des systemes tribologiques autolubrifiants et donc plus ecologiques et moins couteux. Ce travail de recherche est donc focalise sur l’evolution des performances de la lubrification XPHD dans le cadre d’un mouvement tangentiel, adapte a l’etude de butees pour faible et moyenne vitesses de rotation. Dans ce contexte scientifique, une etude approfondie d’un materiau poreux preselectionne (mousse en polyurethane) fut realisee avec le but de determiner les caracteristiques physiques et les parametres cruciaux pour la lubrification XPHD: la porosite et la permeabilite du materiau poreux. Les modeles theorique et numerique de lubrification XPHD proposes se basent sur l’equation de Darcy-Brinkman et les hypotheses de la lubrification classique, ainsi que l’ecoulement a l’interieur du milieu poreux sont predit avec une nouvelle forme de l’equation de Reynolds. Le banc d’essais specialement developpe permet d’etudier experimentalement le mecanisme de lubrification XPHD pour des differents types de dislocateur en combinaison avec des liquides newtoniens et nonnewtoniens. Une description detaillee du banc d’essais et de tous les dispositifs experimentaux utilises ainsi que la comparaison des resultats de modelisation et des resultats experimentaux sont presentes.
Influencia de las condiciones dinámicas en el diseño de medidas de estabilización de laderas: el caso del deslizamiento de Diezma (Granada)

En este trabajo se ha analizado la eficacia de las medidas de estabilizacion de taludes en un area activa sismicamente. Este analisis se ha realizado tanto en condiciones estaticas como dinamicas, considerando las aceleraciones simicas mas probables para el emplazamiento. Para ello se ha realizado un analisis de estabilidad retrospectivo de un deslizamiento complejo (el deslizamiento Diezma) ubicado en el sur de Espana utilizando datos geotecnicos, geofisicos y geologicos de detalle. Los factores desencadenantes fueron la presencia de un nivel freatico somero y la reduccion de la resistencia al corte de los niveles de arcilla de alta plasticidad a valores residuales. En el 2010, tras casi 10 anos desde la instalacion de las medidas de estabilizacion, se produjo una nueva reactivacion del deslizamiento relacionada con un mal funcionamiento de la primera linea de pozos de drenaje profundo. La segunda y tercera linea de pozos y la barrera de pilotes anclados parecen funcionar con eficacia, deteniendo el avance del deslizamiento hacia el pie de la ladera. Sin embargo, la reactivacion del deslizamiento de Diezma es esperable en el caso de que un terremoto de baja magnitud, relativamente frecuentes en la zona, se produzca cerca del deslizamiento. [ABSTRACT]In this paper the effectiveness of slope stabilization measures in a seismic active area have been analysed. A complete failure analysis of a complex landslide located in Southern Spain (the Diezma landslide) has been performed using detailed geotechnical, geophysical and geological data. The triggering factors were a shallow water table and the reduction of the shear strength parameters of the high-plasticity clay levels to residual values. The 2010 landslide reactivation was related to a bad performance of the first line of deep drainage wells. The second and third line of wells and the anchored piles barrier seem to work effectively by stopping the landslide from reaching the toe of the slope. However, the reactivation of the Diezma landslide is expected in the case that a low magnitude earthquake, relatively common in the study area, occurs close to the landslide location.
The identification of macro nutrient status of superior teak plantation (Tectona grandis Lin.F) by means of DRIS norms (Diagnosis and Recommendation Integrated System) in Indonesia

Abstract The improvement in forest productivity by considering the balance of nutrient input-output is the proper choice. This research aimed at investigating the macro nutrient status of a teak plantation in Indonesia. The quantitative measurement of the nutritional status of a plantation was done by picking sample plantations and doing laboratory analysis. The determination of macronutrition status was conducted by means of Diagnosis and Recommendation Integrated System (DRIS), that is a diagnostic method to measure the balance of nutrients before designing silvicultural treatments. Index analysis on teak has resulted in a macronutrient index (N, P, K, Ca, and Mg); respectively –8.40, –1.70, –4.17, 4.98 and 9.28. The availability levels of nutrients from the highest to the lowest were Mg > Ca > P > K > N. Mg and Ca have shown positive index values; this implies that the nutrients are sufficient. N, K, and P have shown negative index values; this implies that the nutrients are less available, and thus growth-limiting nutrients for teak. It is recommended that silvicultural treatments be conducted on teak by means of balanced nutrient inputs, to be specific N, P, and K to achieve maximum growth of teak.
Provable Training for Graph Contrastive Learning

Graph Contrastive Learning (GCL) has emerged as a popular training approach for learning node embeddings from augmented graphs without labels. Despite the key principle that maximizing the similarity between positive node pairs while minimizing it between negative node pairs is well established, some fundamental problems are still unclear. Considering the complex graph structure, are some nodes consistently well-trained and following this principle even with different graph augmentations? Or are there some nodes more likely to be untrained across graph augmentations and violate the principle? How to distinguish these nodes and further guide the training of GCL? To answer these questions, we first present experimental evidence showing that the training of GCL is indeed imbalanced across all nodes. To address this problem, we propose the metric"node compactness", which is the lower bound of how a node follows the GCL principle related to the range of augmentations. We further derive the form of node compactness theoretically through bound propagation, which can be integrated into binary cross-entropy as a regularization. To this end, we propose the PrOvable Training (POT) for GCL, which regularizes the training of GCL to encode node embeddings that follows the GCL principle better. Through extensive experiments on various benchmarks, POT consistently improves the existing GCL approaches, serving as a friendly plugin.
Monitoring Crustal Deformations with Radar Interferometry： A Review

The crustal movements, probably motivating earthquakes, are considered as one of the main geodynamic sources. The quantitative measurements of ground surface deformations are vital for studying mechanisms of the buried faults or even estimating earthquake potential. A new space-geodetic technology, synthetic aperture radar interferometry (InSAR), can be applied to detect such large-area deformations, and has demonstrated some prominent advantages. This paper reviews the capacity and limitations of InSAR, and summarises the existing applications including some of our results in studying the earthquake-related crustal motions.Finally it gives the outlook for the future development of InSAR.
SuPerPM: A Large Deformation-Robust Surgical Perception Framework Based on Deep Point Matching Learned from Physical Constrained Simulation Data

Manipulation of tissue with surgical tools often results in large deformations that current methods in tracking and reconstructing algorithms have not effectively addressed. A major source of tracking errors during large deformations stems from wrong data association between observed sensor measurements with previously tracked scene. To mitigate this issue, we present a surgical perception framework, SuPerPM, that leverages learning-based non-rigid point cloud matching for data association, thus accommodating larger deformations. The learning models typically require training data with ground truth point cloud correspondences, which is challenging or even impractical to collect in surgical environments. Thus, for tuning the learning model, we gather endoscopic data of soft tissue being manipulated by a surgical robot and then establish correspondences between point clouds at different time points to serve as ground truth. This was achieved by employing a position-based dynamics (PBD) simulation to ensure that the correspondences adhered to physical constraints. The proposed framework is demonstrated on several challenging surgical datasets that are characterized by large deformations, achieving superior performance over state-of-the-art surgical scene tracking algorithms.
WikiMT++ Dataset Card

WikiMT++ is an expanded and refined version of WikiMusicText (WikiMT), featuring 1010 curated lead sheets in ABC notation. To expand application scenarios of WikiMT, we add both objective (album, lyrics, video) and subjective emotion (12 emotion adjectives) and emo\_4q (Russell 4Q) attributes, enhancing its usability for music information retrieval, conditional music generation, automatic composition, and emotion classification, etc. Additionally, CLaMP is implemented to correct the attributes inherited from WikiMT to reduce errors introduced during original data collection and enhance the accuracy and completeness of our dataset.
MiliPoint: A Point Cloud Dataset for mmWave Radar

Millimetre-wave (mmWave) radar has emerged as an attractive and cost-effective alternative for human activity sensing compared to traditional camera-based systems. mmWave radars are also non-intrusive, providing better protection for user privacy. However, as a Radio Frequency (RF) based technology, mmWave radars rely on capturing reflected signals from objects, making them more prone to noise compared to cameras. This raises an intriguing question for the deep learning community: Can we develop more effective point set-based deep learning methods for such attractive sensors? To answer this question, our work, termed MiliPoint, delves into this idea by providing a large-scale, open dataset for the community to explore how mmWave radars can be utilised for human activity recognition. Moreover, MiliPoint stands out as it is larger in size than existing datasets, has more diverse human actions represented, and encompasses all three key tasks in human activity recognition. We have also established a range of point-based deep neural networks such as DGCNN, PointNet++ and PointTransformer, on MiliPoint, which can serve to set the ground baseline for further development.