Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating Sustainability of Cropland Use in Yuanzhou County of the Loess Plateau, China Using an Emergy-Based Ecological Footprint

Evaluating the sustainability of cropland use is essential for guaranteeing a secure food supply and accomplishing agriculture sustainable development. This study was conducted in the ecologically vulnerable Loess Plateau region of China to evaluate the sustainability of cropland use based on an ecological footprint model that integrates emergy analysis. One modified method proposed in 2005 is known as the emergetic ecological footprint (EEF). We enhanced the method by accounting for both the surface soil energy in the carrying capacity calculation and the net topsoil loss for human consumption in the EF calculation. This paper evaluates whether the cropland of the study area was overloaded or sustainably managed during the period from 1981 to 2009. Toward this end, the final results obtained from EEF were compared to conventional EF and previous methods. The results showed that the cropland of Yuanzhou County has not been used sustainably since 1983, and the conventional EF analysis provided similar results. In contrast, a deficit did not appear during this time period when previous calculation methods of others were used. Additionally, the ecological sustainable index (ESI) from three models indicated that the recently used cropland system is unlikely to be unsustainable.
An Information Retrieval System for Improving Efficiency in Scientific Literature Searches : Progress Report # 1

This reports details the work completed in the first three months of development of the QCS (Query, Cluster, Summarize) information retrieval (IR) system. In the first section, the project as it was originally proposed is briefly presented. (For a more detailed account of the proposal, an interested reader is directed to the original proposal [Dun].) Sections 3, 4, and 5 give the details of the algorithms implemented in the current QCS system. Implementation issues are discussed in Section 6, highlighting work completed to date on the QCS system, a user interface to the system, and accompanying software tools created or utilized in facilitating the development of the QCS system. As with many software projects of this scale, some of the ideas originally proposed for the QCS system in [Dun] have been phased out for various reasons. The specific changes and the rationale behind these changes are discussed in Section 7. Finally, a plan for the completion of Version 1.0 of the QCS IR system is presented in Section 8.
Nearest Neighbors with Learned Distances for Phonetic Frame Classification

Nearest neighbor-based techniques provide an approach to acoustic modeling that avoids the often lengthy and heuristic process of training traditional Gaussian mixturebased models. Here we study the problem of choosing the distance metric for a k-nearest neighbor (k-NN) phonetic frame classifier. We compare the standard Euclidean distance to two learned Mahalanobis distances, based on large-margin nearest neighbors (LMNN) and locality preserving projections (LPP). We use locality sensitive hashing for approximate nearest neighbor search to reduce the test time of k-NN classification. We compare the error rates of these approaches, as well as of baseline Gaussian mixture-based and multilayer perceptron classifiers, on the task of phonetic frame classification of speech from the TIMIT database. The k-NN classifiers outperform Gaussian mixture models, but not multilayer perceptrons. We find that the best k-NN classification performance is obtained using LPP, while LMNN is close behind.
VI-17-1 MULTIDIMENSIONAL FUNCTION APPROXIMATION USING NEURAL NETWORKS

Solving a problem with a neural network a primordial task is establishing the network topology. Generally neural network topology determination is a complex problem and cannot be easily solved. When the number of trainable layers and processor units is too low, the network is not able to learn the proposed problem. When the number of layers and neurons is too high, the learning process becomes too slow. Learning from examples means being able to infer the functional dependence between input and output spaces X and Z, given the knowledge of the set of examples T. It means that, after we have “learned” N examples, when a new input variable x comes in, we need to be able to estimate, according to some criterion that we will specify, a corresponding value of z. From this point of view learning is equivalent to a function approximation.
On GitHub's Programming Languages

GitHub is the most widely used social, distributed version control system. It has around 10 million registered users and hosts over 16 million public repositories. Its user base is also very active as GitHub ranks in the top 100 Alexa most popular websites. In this study, we collect GitHub's state in its entirety. Doing so, allows us to study new aspects of the ecosystem. Although GitHub is the home to millions of users and repositories, the analysis of users' activity time-series reveals that only around 10% of them can be considered active. The collected dataset allows us to investigate the popularity of programming languages and existence of pattens in the relations between users, repositories, and programming languages. By, applying a k-means clustering method to the users-repositories commits matrix, we find that two clear clusters of programming languages separate from the remaining. One cluster forms for "web programming" languages (Java Script, Ruby, PHP, CSS), and a second for "system oriented programming" languages (C, C++, Python). Further classification, allow us to build a phylogenetic tree of the use of programming languages in GitHub. Additionally, we study the main and the auxiliary programming languages of the top 1000 repositories in more detail. We provide a ranking of these auxiliary programming languages using various metrics, such as percentage of lines of code, and PageRank.
Differential Privacy Under Fire Andreas Haeberlen

Anonymizing private data before release is not enough to reliably protect privacy, as Netflix and AOL have learned to their cost. Recent research on differential privacy opens a way to obtain robust, provable privacy guarantees, and systems like PINQ and Airavat now offer convenient frameworks for processing arbitrary userspecified queries in a differentially private way. However, these systems are vulnerable to a variety of covertchannel attacks that can be exploited by an adversarial querier. We describe several different kinds of attacks, all feasible in PINQ and some in Airavat. We discuss the space of possible countermeasures, and we present a detailed design for one specific solution, based on a new primitive we call predictable transactions and a simple differentially private programming language. Our evaluation, which relies on a proof-of-concept implementation based on the Caml Light runtime, shows that our design is effective against remotely exploitable covert channels, at the expense of a higher query completion time. Disciplines Computer Sciences Comments Haeberlen, A. , Pierce, B. , & Narayan, A. , Differential Privacy Under Fire, 20th USENIX Security Symposium, Aug. 2011, http://static.usenix. org/events/sec11/tech/full_papers/Haeberlen.pdf This conference paper is available at ScholarlyCommons: http://repository.upenn. edu/cis_papers/609 Differential Privacy Under Fire Andreas Haeberlen Benjamin C. Pierce Arjun Narayan University of Pennsylvania
New sum rules for nucleon tensor charges

Two new sum rules for the quark tensor charges of the nucleon are proposed, based on a relation connecting the quark transversity distributions to the quark helicity distributions and the quark model spin distributions, and on the sum rules for the quark helicity distributions. The two sum rules are useful for an estimate of the values of the quark tensor charges and from the measured quantities of , , and , and two model correction factors with limited uncertainties. We predict a small value for the sum of the quark tensor charges compared with most other predictions, in analogy to the unexpectedly small quark helicity sum which gave rise to the proton `spin puzzle'.
Learning coupled dynamic models of underwater vehicles using Support Vector Regression

This work addresses a data driven approach which employs a machine learning technique known as Support Vector Regression (SVR), to identify the coupled dynamical model of an autonomous underwater vehicle. To train the regressor, we use a dataset collected from the robot's on-board navigation sensors and actuators. To achieve a better fit to the experimental data, a variant of a radial-basis-function kernel is used in combination with the SVR which accounts for the different complexities of each of the contributing input features of the model. We compare our method to other explicit hydrodynamic damping models that were identified using the total least squares method and with less complex SVR methods. To analyze the transferability, we clearly separate training and testing data obtained in real-world experiments. Our presented method shows much better results especially compared to classical approaches.
Relative Errors for Deterministic Low-Rank Matrix Approximations

We consider processing an n x d matrix A in a stream with row-wise updates according to a recent algorithm called Frequent Directions (Liberty, KDD 2013). This algorithm maintains an l x d matrix Q deterministically, processing each row in O(d l^2) time; the processing time can be decreased to O(d l) with a slight modification in the algorithm and a constant increase in space. We show that if one sets l = k+ k/eps and returns Q_k, a k x d matrix that is the best rank k approximation to Q, then we achieve the following properties: ||A - A_k||_F^2 <= ||A||_F^2 - ||Q_k||_F^2 <= (1+eps) ||A - A_k||_F^2 and where pi_{Q_k}(A) is the projection of A onto the rowspace of Q_k then ||A - pi_{Q_k}(A)||_F^2 <= (1+eps) ||A - A_k||_F^2. We also show that Frequent Directions cannot be adapted to a sparse version in an obvious way that retains the l original rows of the matrix, as opposed to a linear combination or sketch of the rows.
Uptake of the Necrotic Serpin in Drosophila melanogaster via the Lipophorin Receptor-1

The humoral response to fungal and Gram-positive infections is regulated by the serpin-family inhibitor, Necrotic. Following immune-challenge, a proteolytic cascade is activated which signals through the Toll receptor. Toll activation results in a range of antibiotic peptides being synthesised in the fat-body and exported to the haemolymph. As with mammalian serpins, Necrotic turnover in Drosophila is rapid. This serpin is synthesised in the fat-body, but its site of degradation has been unclear. By “freezing” endocytosis with a temperature sensitive Dynamin mutation, we demonstrate that Necrotic is removed from the haemolymph in two groups of giant cells: the garland and pericardial athrocytes. Necrotic uptake responds rapidly to infection, being visibly increased after 30 mins and peaking at 6–8 hours. Co-localisation of anti-Nec with anti-AP50, Rab5, and Rab7 antibodies establishes that the serpin is processed through multi-vesicular bodies and delivered to the lysosome, where it co-localises with the ubiquitin-binding protein, HRS. Nec does not co-localise with Rab11, indicating that the serpin is not re-exported from athrocytes. Instead, mutations which block late endosome/lysosome fusion (dor, hk, and car) cause accumulation of Necrotic-positive endosomes, even in the absence of infection. Knockdown of the 6 Drosophila orthologues of the mammalian LDL receptor family with dsRNA identifies LpR1 as an enhancer of the immune response. Uptake of Necrotic from the haemolymph is blocked by a chromosomal deletion of LpR1. In conclusion, we identify the cells and the receptor molecule responsible for the uptake and degradation of the Necrotic serpin in Drosophila melanogaster. The scavenging of serpin/proteinase complexes may be a critical step in the regulation of proteolytic cascades.
Multi-rate LDPC decoder implementation for china digital television terrestrial broadcasting standard

The China national standard of digital television terrestrial broadcasting employs Low-Density Parity-Check (LDPC) codes of three different rates as the inner Forward Error Correction (FEC) Codes. In this paper, according to the structure of the quasi-cyclic LDPC (QC-LDPC) code, an implementation of multi-rate LDPC decoder is proposed using semi-parallel structure, achieving the appropriate balance between decoding throughput and the utilization of hardware resource. Meanwhile, a novel multiplex model of storage space and processing units is proposed to increase the resource efficiency of this decoder, which is much better than that of simple combination of three single-rate decoders. Simulation results demonstrate that this multi-rate decoder's performance under AWGN channel is the same as that of single rate decoders, while it has higher resource utilization efficiency. Therefore it could help reduce the chip size of the decoder greatly. In addition, the multi-rate strategy can also be applied to other QC-LDPC codes.
Tight control is important in patients with rheumatoid arthritis treated with an anti-tumor necrosis factor biological agent: prospective study of 91 cases who used a biological agent for more than 1 year

The purpose of this study is to identify the factors that require arthroplasty due to the progression of joint destruction, even when an anti-tumor necrosis factor (TNF) biological agent is used for rheumatoid arthritis (RA). Among 91 cases that used the anti-TNF biological agent for more than 1 year, two groups of 21 cases that resulted in arthroplasty (surgery group) and 70 cases that did not result in arthroplasty (non-surgery group) were compared and examined. When the anti-TNF biological agent was first administered, disease activity and internal use of glucocorticoid (PSL) were not different in these two groups. The average DAS28-CRP(4) (disease activity score including a 28-joint count/C-reactive protein) (p < 0.001) and the amount of internal use of PSL (p < 0.05) were significantly decreased in the non-surgery group compared with the surgery group at the final survey. To inhibit the need for joint surgery in patients using the anti-TNF biological agent, it is important to maintain tight control over RA activity.
Evaluation of a Supervised Learning Approach for Stock Market Operations

Data mining methods have been widely applied in financial markets, with the purpose of providing suitable tools for prices forecasting and automatic trading. Particularly, learning methods aim to identify patterns in time series and, based on such patterns, to recommend buy/sell operations. The objective of this work is to evaluate the performance of Random Forests, a supervised learning method based on ensembles of decision trees, for decision support in stock markets. Preliminary results indicate good rates of successful operations and good rates of return per operation, providing a strong motivation for further research in this topic.
A C-Terminal Inhibitory Domain Controls the Activity of p63 by an Intramolecular Mechanism

ABSTRACT The human genome is far smaller than originally estimated, and one explanation is that alternative splicing creates greater proteomic complexity than a simple count of open reading frames would suggest. The p53 homologue p63, for example, is a tetrameric transcription factor implicated in epithelial development and expressed as at least six isoforms with widely differing transactivation potential. In particular, p63α isoforms contain a 27-kDa C-terminal region that drastically reduces their activity and is of clear biological importance, since patients with deletions in this C terminus have phenotypes very similar to patients with mutations in the DNA-binding domain. We have identified a novel domain within this C terminus that is necessary and sufficient for transcriptional inhibition and which acts by binding to a region in the N-terminal transactivation domain of p63 homologous to the MDM2 binding site in p53. Based on this mechanism, we provide a model that explains the transactivation potential of homo- and heterotetramers composed of different p63 isoforms and their effect on p53.
Application of Laplace transformation for digital redesign of continuous control systems

A frequency method of optimal digital re-design of an existing continuous-time system by a quadratic criterion is presented. The method is based on the use of Laplace transformation in continuous time and a parametrization of the set of admissible controllers. The proposed technique makes it possible to solve the optimization problem directly by employing input-output relations without an auxiliary passage to the state space. With such an approach the synthesis procedure is often greatly simplified. The method has the important feature that pure delays in the digital redesigned system can be taken into account in a simple way. It is shown that the presented method can be generalized onto different LQ-optimization problems for sampled-data systems.
A Quantitative and Qualitative Evaluation of Sentence Boundary Detection for the Clinical Domain

Sentence boundary detection (SBD) is a critical preprocessing task for many natural language processing (NLP) applications. However, there has been little work on evaluating how well existing methods for SBD perform in the clinical domain. We evaluate five popular off-the-shelf NLP toolkits on the task of SBD in various kinds of text using a diverse set of corpora, including the GENIA corpus of biomedical abstracts, a corpus of clinical notes used in the 2010 i2b2 shared task, and two general-domain corpora (the British National Corpus and Switchboard). We find that, with the exception of the cTAKES system, the toolkits we evaluate perform noticeably worse on clinical text than on general-domain text. We identify and discuss major classes of errors, and suggest directions for future work to improve SBD methods in the clinical domain. We also make the code used for SBD evaluation in this paper available for download at http://github.com/drgriffis/SBD-Evaluation.
Profile of netupitant/palonosetron (NEPA) fixed dose combination and its potential in the treatment of chemotherapy-induced nausea and vomiting (CINV)

Chemotherapy-induced nausea and vomiting (CINV) is associated with a significant deterioration in quality of life. The emetogenicity of the chemotherapeutic agents, repeated chemotherapy cycles, and patient risk factors significantly influence CINV. The use of a combination of a 5-hydroxytryptamine-3 (5-HT3) receptor antagonists, dexamethasone, and a neurokinin-1 (NK-1) receptor antagonist has significantly improved the control of acute and delayed emesis in single-day chemotherapy. Palonosetron, a second generation 5-HT3 receptor antagonist with a different half-life, different binding capacity, and a different mechanism of action than the first generation 5-HT3 receptor antagonists, appears to be the most effective agent in its class. Netupitant, is a new NK-1 receptor antagonist with a high binding affinity, a long half-life of 90 hours, is metabolized by CYP3A4, and is an inhibitor of CYP3A4. NEPA is an oral fixed-dose combination of netupitant and palonosetron which has recently been employed in Phase II and Phase III clinical trials for the prevention of CINV in patients receiving moderately and highly emetogenic chemotherapy (MEC and HEC). The clinical trials demonstrated that NEPA (300 mg of netupitant plus 0.50 mg of palonosetron) significantly improved the prevention of CINV compared to the use of palonosetron alone in patients receiving either HEC or MEC. The clinical efficacy was maintained over multiple cycles of chemotherapy. NEPA (Akynzeo®) has recently been approved by the Food and Drug Administration (FDA) to treat nausea and vomiting in patients undergoing cancer chemotherapy.
Convective Heat Transfer Coefficient for High Pressure Water Jet

The scale on the surface of hot rolled steel is removed by high pressure hydraulic descaling in a rolling mill. If the impact pressure of the water jet is higher, the scale is removed well. However, the temperature drop of the hot rolled steel is increased owing to the flow rate of the water jet. On this account, the temperature distribution of the hot rolled steel should be analyzed during the process of the hydraulic descaling. However, the analysis of the temperature distribution is difficult due to lack of the literatures on the convective heat transfer coefficient for the high pressure water jet. In the present study, the hydraulic descaling system is manufactured and the impact pressure is deduced from the relationship between spray pressure and spray height. And then the equation of the convective heat transfer coefficient is induced by the function of the impact pressure. The convective heat transfer coefficient is obtained from the experimental values by the hydraulic descaling system and the values calculated by numerical analysis. The equation of the convective heat transfer coefficient will aid to establish the most suitable conditions for operations in the rolling mill.
A Probabilistic Classification Approach for Lexical Textual Entailment

The textual entailment task - determining if a given text entails a given hypothesis - provides an abstraction of applied semantic inference. This paper describes first a general generative probabilistic setting for textual entailment. We then focus on the sub-task of recognizing whether the lexical concepts present in the hypothesis are entailed from the text. This problem is recast as one of text categorization in which the classes are the vocabulary words. We make novel use of Naive Bayes to model the problem in an entirely unsupervised fashion. Empirical tests suggest that the method is effective and compares favorably with state-of-the-art heuristic scoring approaches.
Photorefractive grating formation with very short pulses: the bulk photovoltaic effect

We investigated the role of the photovoltaic effect on the photorefractive grating formation under illumination of weak ultrashort light pulses whose duration is much shorter than the time constants of the carrier and compared it with the cw case. In our experiments using mode-locked picosecond and cw Nd:YAG lasers, the bulk photovoltaic current in an iron- doped lithium niobate crystal were confirmed to be in the same order of magnitude for both light sources. The evolution of photorefractive gratings was also essentially the same. The Glass expression for the bulk photovoltaic effect is not compatible with the scheme of the band transport model for a very fast response. Therefore we developed a model in which the bulk photovoltaic current was implemented as a spatial shift in the initial carrier distribution. The resultant expression agreed with that of the quasi-cw approximation in the limit of a small spatial shift.
Towards Predicting the Likeability of Fashion Images

In this paper, we propose a method for ranking fashion images to find the ones which might be liked by more people. We collect two new datasets from image sharing websites (Pinterest and Polyvore). We represent fashion images based on attributes: semantic attributes and data-driven attributes. To learn semantic attributes from limited training data, we use an algorithm on multi-task convolutional neural networks to share visual knowledge among different semantic attribute categories. To discover data-driven attributes unsupervisedly, we propose an algorithm to simultaneously discover visual clusters and learn fashion-specific feature representations. Given attributes as representations, we propose to learn a ranking SPN (sum product networks) to rank pairs of fashion images. The proposed ranking SPN can capture the high-order correlations of the attributes. We show the effectiveness of our method on our two newly collected datasets.