nils-herrmann/clean_scirepeval huggingface dataset:
An unusual presentation of a customs importation seizure containing amphetamine, possibly synthesized by the APAAN-P2P-Leuckart route.

During the analysis of an Irish customs seizure (14 packages each containing approximately one kilogram of a white wet paste) were analysed for the suspected presence of controlled drugs. The samples were found to contain amphetamine and also characteristic by-products including benzyl cyanide, phenylacetone (P2P), methyl-phenyl-pyrimidines, N-formylamphetamine, naphthalene derivatives and amphetamine dimers. The analytical results corresponded with the impurity profile observed and recently reported for the synthesis of 4-methylamphetamine from 4-methylphenylacetoacetonitrile . The synthesis of amphetamine from alpha-phenylacetoacetonitrile (APAAN) was performed (via an acid hydrolysis and subsequent Leuckart reaction) and the impurity profile of the product obtained was compared to those observed in the customs seizure. Observations are made regarding the route specificity of these by-products.
Is Foreign Direct Investment a Channel of Knowledge Spillovers ? Evidence from Japan ’ s FDI in the United States ∗

Recent empirical work has examined the extent to which international trade fosters international “spillovers” of technological information. FDI is an alternate, potentially equally important channel for the mediation of such knowledge spillovers. I introduce a framework for measuring international knowledge spillovers at the firm level, and I use this framework to directly test the hypothesis that FDI is a channel of knowledge spillovers for Japanese multinationals undertaking direct investments in the United States. Using an original firm-level panel data set on Japanese firms’ FDI and innovative activity, I find evidence that FDI increases the flow of knowledge spillovers both from and to the investing Japanese firms. ∗ This paper is a revision of Branstetter (2000a). I would like to thank Natasha Hsieh, Masami Imai,Yoko Kusaka, Grace Lin, Kentaro Minato, Kaoru Nabeshima, and Yoshiaki Ogura for excellent research assistance. I also thank Paul Almeida, Jonathan Eaton, Bronwyn Hall, Takatoshi Ito, Adam Jaffe, Wolfgang Keller, Yoshiaki Nakamura, James Rauch, Mariko Sakakibara, Ryuhei Wakasugi, two anonymous referees, and seminar participants at UC-Davis, UC-Berkeley, Boston University, UC-Boulder, Brandeis University, Columbia University, Cornell University, Northwestern University, UC-San Diego, the World Bank, the University of Michigan, the Research Institute of Economy, Trade, and Industry, and the NBER for valuable comments. Funding was provided by a University of California Faculty Research Grant, a grant from the Japan Foundation Center for Global Partnership, and the NBER Project on Industrial Technology and Productivity. Note that parts of this paper borrow from Branstetter (2000b) and from Branstetter and Nakamura (2003). I am solely responsible for any errors.  Lee Branstetter, Columbia Business School, Uris Hall 815, 3022 Broadway, New York, NY 10027; TEL 212-854-2722; FAX 212-854-9895; E-mail
Reconfigurable Path Planning for an Autonomous Unmanned Aerial Vehicle

In this paper, we present a motion planning framework for a fully deployed autonomous unmanned aerial vehicle which integrates two sample-based motion planning techniques, Probabilistic Roadmaps and Rapidly Exploring Random Trees. Additionally, we incorporate dynamic reconfigurability into the framework by integrating the motion planners with the control kernel of the UAV in a novel manner with little modification to the original algorithms. The framework has been verified through simulation and in actual flight. Empirical results show that these techniques used with such a framework offer a surprisingly efficient method for dynamically reconfiguring a motion plan based on unforeseen contingencies which may arise during the execution of a plan. The framework is generic and can be used for additional platforms.
Learning Discriminative Stein Kernel for SPD Matrices and Its Applications

Stein kernel (SK) has recently shown promising performance on classifying images represented by symmetric positive definite (SPD) matrices. It evaluates the similarity between two SPD matrices through their eigenvalues. In this paper, we argue that directly using the original eigenvalues may be problematic because: 1) eigenvalue estimation becomes biased when the number of samples is inadequate, which may lead to unreliable kernel evaluation, and 2) more importantly, eigenvalues reflect only the property of an individual SPD matrix. They are not necessarily optimal for computing SK when the goal is to discriminate different classes of SPD matrices. To address the two issues, we propose a discriminative SK (DSK), in which an extra parameter vector is defined to adjust the eigenvalues of input SPD matrices. The optimal parameter values are sought by optimizing a proxy of classification performance. To show the generality of the proposed method, three kernel learning criteria that are commonly used in the literature are employed as a proxy. A comprehensive experimental study is conducted on a variety of image classification tasks to compare the proposed DSK with the original SK and other methods for evaluating the similarity between SPD matrices. The results demonstrate that the DSK can attain greater discrimination and better align with classification tasks by altering the eigenvalues. This makes it produce higher classification performance than the original SK and other commonly used methods.
GraphTwist: Fast Iterative Graph Computation with Two-tier Optimizations

Large-scale real-world graphs are known to have highly skewed vertex degree distribution and highly skewed edge weight distribution. Existing vertex-centric iterative graph computation models suffer from a number of serious problems: (1) poor performance of parallel execution due to inherent workload imbalance at vertex level; (2) inefficient CPU resource utilization due to short execution time for low-degree vertices compared to the cost of in-memory or on-disk vertex access; and (3) incapability of pruning insignificant vertices or edges to improve the computational performance. In this paper, we address the above technical challenges by designing and implementing a scalable, efficient, and provably correct two-tier graph parallel processing system, GraphTwist. At storage and access tier, GraphTwist maximizes parallel efficiency by employing three graph parallel abstractions for partitioning a big graph by slice, strip or dice based partitioning techniques. At computation tier, GraphTwist presents two utility-aware pruning strategies: slice pruning and cut pruning, to further improve the computational performance while preserving the computational utility defined by graph applications. Theoretic analysis is provided to quantitatively prove that iterative graph computations powered by utility-aware pruning techniques can achieve a very good approximation with bounds on the introduced error.
Extracting semantics from audio-visual content: the final frontier in multimedia retrieval

Multimedia understanding is a fast emerging interdisciplinary research area. There is tremendous potential for effective use of multimedia content through intelligent analysis. Diverse application areas are increasingly relying on multimedia understanding systems. Advances in multimedia understanding are related directly to advances in signal processing, computer vision, pattern recognition, multimedia databases, and smart sensors. We review the state-of-the-art techniques in multimedia retrieval. In particular, we discuss how multimedia retrieval can be viewed as a pattern recognition problem. We discuss how reliance on powerful pattern recognition and machine learning techniques is increasing in the field of multimedia retrieval. We review the state-of-the-art multimedia understanding systems with particular emphasis on a system for semantic video indexing centered around multijects and multinets. We discuss how semantic retrieval is centered around concepts and context and the various mechanisms for modeling concepts and context.
Supplier Selection in the Construction Material Purchasing Function

Construction materials occupy a significant part of the construction’s value contributing nearly 50%. Thus when selecting construction materials, it is very important that painstaking decisions should be made. Past literature and anecdotal evidences show that the main issue with building materials purchasing comes with supplier selection, and depend on careful examination of supplier economics among other criteria. Supplier selection is the purchasing function that forms the foundation for the success or failure of projects. Therefore supplier selection criteria should be well defined. Supplier selection is a multi-criteria decision making problem which includes both qualitative and quantitative considerations. A trade-off between tangible and intangible criteria is important in selecting the best supplier. This paper presents a review of supplier selection processes and decision making methods reported in academic and other literature related to the construction industry. The study is a part of an on-going doctoral research study on construction material purchasing decisions aimed at determining how small to medium scale (SME) construction contractors could secure ‘best prices’ for their key material inputs in New Zealand construction industry. This is a first step towards a comprehensive empirical study of securing best construction material prices.
A New B7:CD28 Family Checkpoint Target for Cancer Immunotherapy: HHLA2.

HHLA2 is a newly identified B7 family member that modulates T-cell functions through interaction with TMIGD2 and possibly a second receptor, with coinhibition in two studies and costimulation in one study. HHLA2 is expressed on a variety of human cancers, and its coinhibitory function makes it a candidate for cancer immunotherapy.
Self-Compensation of the Commutation Angle Based on DC-Link Current for High-Speed Brushless DC Motors With Low Inductance

As low-resolution position sensors, a high placement accuracy of Hall-effect sensors is hard to achieve. Accordingly, a commutation angle error is generated. The commutation angle error will inevitably increase the loss of the low inductance motor and even cause serious consequence, which is the abnormal conduction of a freewheeling diode in the unexcited phase especially at high speed. In this paper, the influence of the commutation angle error on the power loss for the high-speed brushless dc motor with low inductance and nonideal back electromotive force in a magnetically suspended control moment gyro (MSCMG) is analyzed in detail. In order to achieve low steady-state loss of an MSCMG for space application, a straightforward method of self-compensation of commutation angle based on dc-link current is proposed. Both simulation and experimental results confirm the feasibility and effectiveness of the proposed method.
A mediating influence on customer loyalty : The role of perceived value

Loyal customers provide firms a consistent source of revenue (repeat and increased purchases) and for cost reduction (less promotional expenses) that leads to increased profits. Customer loyalty is the result of successful marketing strategy in competitive markets that creates value for consumers. This study examines the mediating role of consumer perceived value in the marketing strategy-customer loyalty relationship. A theoretical framework is established that is supported by empirical evidence. Based on the literature, the findings indicate an inconsistent measure for perceived value that does not fully explain its mediating role. The conclusion is to be valid perceived value should be measured by specific non-monetary scale items.
Model-based learning protects against forming habits.

Studies in humans and rodents have suggested that behavior can at times be "goal-directed"-that is, planned, and purposeful-and at times "habitual"-that is, inflexible and automatically evoked by stimuli. This distinction is central to conceptions of pathological compulsion, as in drug abuse and obsessive-compulsive disorder. Evidence for the distinction has primarily come from outcome devaluation studies, in which the sensitivity of a previously learned behavior to motivational change is used to assay the dominance of habits versus goal-directed actions. However, little is known about how habits and goal-directed control arise. Specifically, in the present study we sought to reveal the trial-by-trial dynamics of instrumental learning that would promote, and protect against, developing habits. In two complementary experiments with independent samples, participants completed a sequential decision task that dissociated two computational-learning mechanisms, model-based and model-free. We then tested for habits by devaluing one of the rewards that had reinforced behavior. In each case, we found that individual differences in model-based learning predicted the participants' subsequent sensitivity to outcome devaluation, suggesting that an associative mechanism underlies a bias toward habit formation in healthy individuals.
Phase II trial of 131I-B1 (anti-CD20) antibody therapy with autologous stem cell transplantation for relapsed B cell lymphomas.

25 patients with relapsed B-cell lymphomas were evaluated with trace labelled doses (2.5 mg/kg, 185-370 MBq [5-10 mCi]) of 131I-labelled anti-CD20 (B1) antibody in a phase II trial. 22 patients achieved 131I-B1 biodistributions delivering higher doses of radiation to tumour sites than to normal organs and 21 of these were treated with therapeutic infusions of 131I-B1 (12.765-29.045 GBq) followed by autologous haemopoietic stem cell reinfusion. 18 of the 21 treated patients had objective responses, including 16 complete remissions. One patient died of progressive lymphoma and one died of sepsis. Analysis of our phase I and II trials with 131I-labelled B1 reveal a progression-free survival of 62% and an overall survival of 93% with a median follow-up of 2 years. 131I-anti-CD20 (B1) antibody therapy produces complete responses of long duration in most patients with relapsed B-cell lymphomas when given at maximally tolerated doses with autologous stem cell rescue.
Fuzzy multiple criteria decision making: Recent developments

Multiple Criteria Decision Making (MCDM) shows signs of becoming a maturing field. There are four quite distinct families of methods: (i) the outranking, (ii) the value and utility theory based, (iii) the multiple objective programming, and (iv) group decision and negotiation theory based methods. Fuzzy MCDM has basically been developed along the same lines, although with the help of fuzzy set theory a number of innovations have been made possible; the most important methods are reviewed and a novel approach interdependence in MCDM is introduced.
A 65nm CMOS low-power, low-voltage bandgap reference with using self-biased composite cascode opamp

In this paper, we present a low-voltage low-power CMOS bandgap voltage reference (BVR) based on the self-cascode self-biased op-amp. The current mirror mismatch error resulting from the channel length modulation (CLM) effect has also been compensated by using composite transistors. This proposed circuit, implemented in 65nm IBM CMOS process which generates a reference of 364mV from a power supply of the 1.2V and consumed the 28 μW at room temperature. The power supply rejection ratio is greater than 60 dB for frequency below 10 kHz. The proposed bandgap achieves a temperature coefficient(TC) of 4.7ppm/°C without trimming for the temperature range(TR) from 0 C to 100°C and the 0.75mV/V of ±10% supply voltage variations.
KrishiPustak: A Social Networking System for Low-Literate Farmers

With the wide penetration of mobile internet, social networking (SN) systems are becoming increasingly popular in the developing world. However, most SN sites are text heavy, and are therefore unusable by low-literate populations. Here we ask what would an SN application for low-literate users look like and how would it be used? We designed and deployed KrishiPustak, an audio-visual SN mobile application for low-literate farming populations in rural India. Over a four month deployment, 306 farmers registered through the phones of eight agricultural mediators making 514 posts and 180 replies. We conducted interviews with farmers and mediators and analyzed the content to understand system usage and to drive iterative design. The context of mediated use and agricultural framing had a powerful impact on system understanding (what it was for) and usage. Overall, KrishiPustak was useful and usable, but none-the-less we identify a number of design recommendations for similar SN systems.
Linear Convergence of Accelerated Stochastic Gradient Descent for Nonconvex Nonsmooth Optimization

In this paper, we study the stochastic gradient descent (SGD) method for the nonconvex nonsmooth optimization, and propose an accelerated SGD method by combining the variance reduction technique with Nesterov’s extrapolation technique. Moreover, based on the local error bound condition, we establish the linear convergence of our method to obtain a stationary point of the nonconvex optimization. In particular, we prove that not only the sequence generated linearly converges to a stationary point of the problem, but also the corresponding sequence of objective values is linearly convergent. Finally, some numerical experiments demonstrate the effectiveness of our method. To the best of our knowledge, it is first proved that the accelerated SGD method converges linearly to the local minimum of the nonconvex optimization.
Requirements Prioritization Challenges in Practice

Requirements prioritization is recognized as an important activity in product development. In this paper, we describe the current state of requirements prioritization practices in two case companies and present the practical challenges involved. Our study showed that requirements prioritization is an ambiguous concept and current practices in the companies are informal. Requirements prioritization requires complex context-specific decision-making and must be performed iteratively in many phases during development work. Practitioners are seeking more systematic ways to prioritize requirements but they find it difficult to pay attention to all the relevant factors that have an effect on priorities and explicitly to draw different stakeholder views together. In addition, practitioners need more information about real customer preferences.
Surgical treatment of Peyronie's disease: choosing the best approach to improve patient satisfaction.

To discuss important points on medical history, preoperative evaluation, real expectations, and selection of the appropriate surgical procedure to improve patient satisfaction after surgical procedures for Peyronie's disease.



Recent advances in approaches to Peyronie's disease are discussed based on the literature and personal experiences. Issues concerning surgical indication, patient selection, surgical techniques, and grafting are discussed. Lengthening procedures on the convex side of the penile curvature by means of grafting offer the best possible gain from a reconstruction standpoint. Penile rectification and rigidity are required to achieve a completely functional penis. Most patients experience associated erectile dysfunction (ED), and penile straightening alone may not be enough to restore complete function. Twenty-five patients were submitted to total penile reconstruction on length and girth with concomitant penile prosthesis implant. The maximum length restoration was possible and limited by the length of the dissected neurovascular bundle. The mean age was 55.4 years (32-69 years) and the mean angle of curvature 74.2+/-22.4 degree (0-100 degree). Pericardial grafting was used to cover the defect. The mean follow-up time was 11.2 +/- 5.9 months (3-22 months).



Mean functional penile length gain was 3.40+/-0.73 cm (2-5 cm). Penile prosthesis maintained the penis straight. No infections occurred. Sexual intercourse was restored in all patients and all reported recovered self-esteem.



Improving patient satisfaction with the surgical treatment includes proper preoperative evaluation on stable disease, penile shortening, vascular and erectile status, patient decision and selection as well as extensive discussion on surgical technique for restoring functional penis (length and rigidity). Length and girth restoration is very important for self-esteem and patient satisfaction.
Deep salience map guided arbitrary direction scene text recognition

Irregular scene text such as curved, rotated or perspective texts commonly appear in natural scene images due to different camera view points, special design purposes etc. In this work, we propose a text salience map guided model to recognize these arbitrary direction scene texts. We train a deep Fully Convolutional Network (FCN) to calculate the precise salience map for texts. Then we estimate the positions and rotations of the text and utilize this information to guide the generation of CNN sequence features. Finally the sequence is recognized with a Recurrent Neural Network (RNN) model. Experiments on various public datasets show that the proposed approach is robust to different distortions and performs superior or comparable to the state-of-the-art techniques.
Face Detection and Recognition in Office Environments Jul 151 Librariv Face Detection and Recognition in Office Environments Acknowledgements

The Gatekeeper is a vision-based door security system developed at the MIT Artificial Intelligence Laboratory. Faces are detected in a real-time video stream using an efficient algorithmic approach, and are recognized using principal component analysis with class specific linear projection. The system sends commands to an automatic sliding door, speech synthesizer, and touchscreen through a multi-client door control server. The software for the Gatekeeper was written using a set of tools created by the author to facilitate the development of real-time machine vision applications in Matlab, C, and Java.
Query-based Workload Forecasting for Self-Driving Database Management Systems

The first step towards an autonomous database management system (DBMS) is the ability to model the target application's workload. This is necessary to allow the system to anticipate future workload needs and select the proper optimizations in a timely manner. Previous forecasting techniques model the resource utilization of the queries. Such metrics, however, change whenever the physical design of the database and the hardware resources change, thereby rendering previous forecasting models useless.
 We present a robust forecasting framework called QueryBot 5000 that allows a DBMS to predict the expected arrival rate of queries in the future based on historical data. To better support highly dynamic environments, our approach uses the logical composition of queries in the workload rather than the amount of physical resources used for query execution. It provides multiple horizons (short- vs. long-term) with different aggregation intervals. We also present a clustering-based technique for reducing the total number of forecasting models to maintain. To evaluate our approach, we compare our forecasting models against other state-of-the-art models on three real-world database traces. We implemented our models in an external controller for PostgreSQL and MySQL and demonstrate their effectiveness in selecting indexes.