Miracle-dz/UniFAR-FTUs huggingface dataset:
Regulation by Shaming : Direct and Spillover Effects of Publicizing Violations of Workplace Safety and Health Laws

Ratings, scores and other forms of information provision are increasingly seen as a tool to incentivize firms to improve their quality or attributes, but less is understood about policies which exclusively publicize firms discovered to have the lowest quality attributes–i.e. “shaming. ” Shaming may affect the decisions of publicized firms, and perhaps of greater policy relevance is its deterrent effect on not-yet publicized firms. After a 2009 policy change, the Occupational Safety and Health Administration (OSHA) began issuing press releases about workplaces found to be in violation of safety and health regulations if the penalties levied for those violations exceeded a cutoff. Using quasi-random variation induced by this cutoff, I estimate the effect of publicizing violations by one workplace on its own subsequent compliance with OSHA regulations, and also the spillover effect onto the compliance of “peer” workplaces most likely exposed to the publicity. Workplaces with a peer whose violations were publicized significantly improve compliance with safety and health regulations. These spillover effects decrease with geographic and industry “distance” to the publicized workplace, and the response appears to be driven by employers acting to avoid their own future publicity, rather than by updating beliefs over the probability of future regulatory enforcement. Finally, the magnitude of spillover effects associated with a press release diminish in the years after the policy’s introduction, suggesting the information content relevant to employers was strongest for the earliest press releases. JEL: J28, J81, L51 ∗Email msjohn@bu.edu I wish to thank Kevin Lang, David I. Levine, and Johannes Schmieder for invaluable guidance, as well as Kavan Kucko, Michael Lipsitz, John Mendeloff, Alison Morantz, Benjamin Ogden, Claudia Olivetti, Mike Toffel, David Weil, Russell Weinstein, and seminar participants at Boston University and the 2014 Alliance for Research on Corporate Sustainability (ARCS) Annual Meeting for helpful discussion. I would also like to thank the staff at OSHA’s headquarters and each of OSHA’s regional Office of Public Affairs for providing me with details on OSHA’s press release policies and patiently answering my unending questions. Keith Ma provided invaluable guidance with geocoding in ArcGIS. Jing Li provided excellent research assistance. All errors are mine and mine alone.
CloCom: Mining existing source code for automatic comment generation

Code comments are an integral part of software development. They improve program comprehension and software maintainability. The lack of code comments is a common problem in the software industry. Therefore, it is beneficial to generate code comments automatically. In this paper, we propose a general approach to generate code comments automatically by analyzing existing software repositories. We apply code clone detection techniques to discover similar code segments and use the comments from some code segments to describe the other similar code segments. We leverage natural language processing techniques to select relevant comment sentences. In our evaluation, we analyze 42 million lines of code from 1,005 open source projects from GitHub, and use them to generate 359 code comments for 21 Java projects. We manually evaluate the generated code comments and find that only 23.7% of the generated code comments are good. We report to the developers the good code comments, whose code segments do not have an existing code comment. Amongst the reported code comments, seven have been confirmed by the developers as good and committable to the software repository while the rest await for developers' confirmation. Although our approach can generate good and committable comments, we still have to improve the yield and accuracy of the proposed approach before it can be used in practice with full automation.
Towards the identification of tissue-based proxy biomarkers

Accurate patient population stratification is a key requirement for a personalized medicine and more precise biomarkers are expected to be obtained by better exploiting the available data. We introduce a novel computational framework that exploits both the information from gene expression data and histopathology images for constructing a tissue-based biomarker, which can be used for identifying a high-risk patient population. Its utility is demonstrated in the context of colorectal cancer data and we show that the resulting biomarker can be used as a proxy for a prognostic gene expression signature. These results are important for both the computational discovery of new biomarkers and clinical practice, as they demonstrate a possible approach for multimodal biomedical data mining and since the new tissue-based biomarker could easily be implemented in the routine pathology practice.
Basis pursuit-based intelligent diagnosis of bearing faults

Purpose – The purpose of this article is to present a new application of pursuit-based analysis for diagnosing rolling element bearing faults. Design/methodology/approach – Intelligent diagnosis of rolling element bearing faults in rotating machinery involves the procedure of feature extraction using modern signal processing techniques and artificial intelligence technique-based fault detection and identification. This paper presents a comparative study of both the basis and matching pursuits when applied to fault diagnosis of rolling element bearings using vibration analysis. Findings – Fault features were extracted from vibration acceleration signals and subsequently fed to a feed forward neural network (FFNN) for classification. The classification rate and mean square error (MSE) were calculated to evaluate the performance of the intelligent diagnostic procedure. Results from the basis pursuit fault diagnosis procedure were compared with the classification result of a matching pursuit feature-based diagnostic procedure. The comparison clearly illustrates that basis pursuit feature-based fault diagnosis is significantly more accurate than matching pursuit feature-based fault diagnosis in detecting these faults. Practical implications – Intelligent diagnosis can reduce the reliance on experienced personnel to make expert judgements on the state of the integrity of machines. The proposed method has the potential to be extensively applied in various industrial scenarios, although this application concerned rolling element bearings only. The principles of the application are directly translatable to other parts of complex machinery. Originality/value – This work presents a novel intelligent diagnosis strategy using pursuit features and feed forward neural networks. The value of the work is to ease the burden of making decisions on the integrity of plant through a manual program in condition monitoring and diagnostics particularly of complex pieces of plant.
A literature review: Exploring organizational learning orientation as antecedent of Information Technology (IT) infrastructure capability to achieve organizational agility

This research-in-progress paper explores how organizational learning orientation can affect Information Technology (IT) infrastructure capability. Four dimensions of organizational learning orientation are analyzed; committed to learning; share vision; open-mindedness; and intra-organizational knowledge sharing. The relationship between IT infrastructure and two types of organizational agility (market capitalizing agility and operational adjustment agility) is also measured. Drawing upon Resource-based view (RBV) and dynamic capabilities theory, six hypotheses are developed to better understand how can organizations can be agile by preparing its IT capability with the right organizational learning orientation. Structural Equation Modeling (SEM) will be employed to analyze whether the collected data support our hypotheses. The propose research model will be empirically validated in the future stages of this research.
Optimized Face Recognition Algorithm using Spatial and Transform Domain Techniques

The biometrics is used to identify or verify persons effectively in the real time scenario. In this paper, we propose Optimized Face Recognition Algorithm using Spatial and Transform Domain Techniques. The face images are preprocessed using Discrete Wavelet Transform (DWT), resize and filtering. The Compound Local Binary Pattern (CLBF) is used to generate magnitude and sign components from preprocessed face images. The histogram is applied on sign and magnitude components of CLBF to compress number of features. The generated histogram features are concatenated to form CLBP-Histogram features. The Fast Fourier Transformation (FFT) is applied on preprocessed image and FFT magnitude features are generated. The CLBP-Histogram features are fused with FFT magnitude features to generate final feature set. The final feature sets of test image and data base images are compared using Euclidian Distance (ED) to recognise a person. It is observed that the performance parameter of the proposed algorithm is better compared to existing algorithms.
Convex ` 2 Synthesis for Multidimensional SystemsRa aello

This paper deals with the design of control systems for multidimensional systems in the Roesser state space format 14] with an`2 induced gain performance criterion. The region of analyticity of the closed loop system can be pre-speciied, corresponding to the relaxation of the standard causal-ity requirement (or equivalently, stability requirement) found in H 1 optimization. These techniques may be used for designing spatially distributed controllers for spatially distributed systems. The main technical contribution of the paper is the extension of the synthesis results of Packard in 11] and of Apkarian and Gahinet in 1] to non-deenite scal-ings. The solution to the problem takes the form of a linear matrix inequality.
13C-13C Homonuclear Recoupling in Solid-State Nuclear Magnetic Resonance at a Moderately High Magic-Angle-Spinning Frequency

Two-dimensional 13C-13C correlation experiments are widely employed in structure determination of protein assemblies using solid-state nuclear magnetic resonance. Here, we investigate the process of 13C-13C magnetisation transfer at a moderate magic-angle-spinning frequency of 30 kHz using some of the prominent second-order dipolar recoupling schemes. The effect of isotropic chemical-shift difference and spatial distance between two carbons and amplitude of radio frequency on 1H channel on the magnetisation transfer efficiency of these schemes is discussed in detail.
2C: A New Approach to Design the Second Level Buffer Cache

Multi-level cache hierarchies are very common in today's networked storage environments. Dedicated data servers or file servers are simultaneously accessed by multi-applications of multi-clients. Workload shift results in cache pollution and degrade of hit ratio. Widely used cache policies work well on the case when a single level cache separates the data provider and data consumer, but poorly on the second level buffer cache scenarios. To avoid the threshing induced by improper cache policies, Lustre file system even abandons the idea of employing server-side buffer caches. However, it suffers from poor performance on random access. We propose a novel cache replacement policy 2C, namely the 2nd level Cache replacement policy, which provides fast access to random pages without extra degrade of hit ratio. 2C detects sequential pages and reclaims memory for random access, so hit ratio of random access is increased significantly. Because 2C reduces the overhead of seeking and rotating time mainly induced by disk head movements of random access, it improves the effective access time of overall storage systems. Our simulation results show that 2C performs better than LRU for a wide range of cache sizes and random/sequential workloads.
Control Strategy Improving Fault Ride-Through Capability of Cascade Multilevel Inverter Based STATCOM

A 10 MVA synchronous static compensation (STATCOM) is being designed for the wind farm owned by the Seawest and connected to the Bonneville power administration (BPA) network in U. S. The five-level cascade multilevel inverter based STATCOM will be used to mitigate the voltage fluctuation. The fault ride through is regarded as the main challenge not only to the wind-turbine, but also to the STATCOM. The common control strategy for the fault ride through is to limit the currents within the rating values. With this strategy, however, we find out big over currents that will trig the over current protections during transient processes. We also find out the over voltages on DC capacitors that will trip the system. In the paper, we propose the improved control strategy for boosting the fault ride through capability. By degrading the power rating during faults, the big over current is avoided. Moreover, by lowering the reference of DC capacitor voltages, the DC capacitors are safe during faults. Simulation results are given to verify the proposed control strategy.
Recognition accuracy and user acceptance of pen interfaces

The accuracy of handwriting recognition is often seen as a key factor in determining the acceptability of hand-held computers that employ a pen for user interaction. We report the results of a study in which the relationship between user satisfaction and recogniser performance was examined in the context of different types of target application. Subjects with no prior experience of pen computing evaluated the appropriateness of the pen interface for performing three different tasks that required translation of handwritten text. The results indicate that the influence of recogniser performance on user satisfaction depends on the task context. These findings are interpreted in terms of the task-related costs and benefits associated with handwriting recognition. Further analysis of recognition data showed that accuracy did not improve as subjects became more practised. However, substantial gains in accuracy could be achieved by selectively adapting the recogniser to deal with a small, user-specific subset of characters.
The price ripple effect in the Vancouver housing market

ABSTRACT Attempts to model the dynamic characteristics of a housing market include examining the spatial diffusion of price changes from an epicenter through a regional or national network of geographic units. Less common has been the study of a ripple effect of price changes within a single metropolitan area, with implications for the erosion of residential affordability. Such trends have particular salience within the Vancouver metropolitan area, the least affordable housing market in North America. Using quarterly price data from local real estate boards, we examine price changes through municipal regions from 2005–2017, a period including several externally-induced price shocks. Several techniques test for a ripple effect in price movements. A time lag of three months consistently exists in the communication of price shocks from an originating epicenter to other parts of the metropolitan region, with longer lags with several more distant municipalities, confirming the presence of an intra-metropolitan ripple effect.
Comparative Analysis of Approximate Blocking Techniques for Entity Resolution

Entity Resolution is a core task for merging data collections. Due to its quadratic complexity, it typically scales to large volumes of data through blocking: similar entities are clustered into blocks and pair-wise comparisons are executed only between co-occurring entities, at the cost of some missed matches. There are numerous blocking methods, and the aim of this work is to offer a comprehensive empirical survey, extending the dimensions of comparison beyond what is commonly available in the literature. We consider 17 state-of-the-art blocking methods and use 6 popular real datasets to examine the robustness of their internal configurations and their relative balance between effectiveness and time efficiency. We also investigate their scalability over a corpus of 7 established synthetic datasets that range from 10,000 to 2 million entities.
Neurotrophin-3 and Brain-Derived Neurotrophic Factor Induce Oligodendrocyte Proliferation and Myelination of Regenerating Axons in the Contused Adult Rat Spinal Cord

Functional loss after spinal cord injury (SCI) is caused, in part, by demyelination of axons surviving the trauma. Neurotrophins have been shown to induce oligodendrogliagenesis in vitro, but stimulation of oligodendrocyte proliferation and myelination by these factors in vivo has not been examined. We sought to determine whether neurotrophins can induce the formation of new oligodendrocytes and myelination of regenerating axons after SCI in adult rats. In this study, fibroblasts producing neurotrophin-3 (NT-3), brain-derived neurotrophic factor (BDNF), ciliary neurotrophic factor, nerve growth factor, basic fibroblast growth factor, or β-galactosidase (control grafts) were transplanted subacutely into the contused adult rat spinal cord. At 10 weeks after injury, all transplants contained axons. NT-3 and BDNF grafts, however, contained significantly more axons than control or other growth factor-producing grafts. In addition, significantly more myelin basic protein-positive profiles were detected in NT-3 and BDNF transplants, suggesting enhanced myelination of ingrowing axons within these neurotrophin-producing grafts. To determine whether augmented myelinogenesis was associated with increased proliferation of oligodendrocyte lineage cells, bromodeoxyuridine (BrdU) was used to label dividing cells. NT-3 and BDNF grafts contained significantly more BrdU-positive oligodendrocytes than controls. The association of these new oligodendrocytes with ingrowing myelinated axons suggests that NT-3- and BDNF-induced myelinogenesis resulted, at least in part, from expansion of oligodendrocyte lineage cells, most likely the endogenous oligodendrocyte progenitors. These findings may have significant implications for chronic demyelinating diseases or CNS injuries.
Safe Device Driver Model Based on Kernel-mode JVM

Researches have shown that device drivers account for most of the OS kernel crashes. In this paper we proposed a safe device driver model for the Linux OS, aiming at gaining an improvement on its reliability. Within this model, we are able to write device drivers in the type-safe language of Java, rather than the traditional C. Our main work includes the design of kernel-mode JVM which is suitable for running device drivers and the programming model of Java device driver. During the evaluation, by manually injecting bugs and observing the running results, we can see that Java device driver can effectively prevent various bugs from damaging the kernel and thus improve the reliability of operating system, only at the price of some degradation on system performance, which helps to demonstrate the viability of our approach. Mention that although we based our research on Linux, Java device driver model can also be applied to other operating systems, as long as the device drivers are accessed through standardized interfaces.
The application of the fractal antenna in the BSN system

In this paper, the performance of a loop-shaped fractal antenna in the BSN system was studied. The feasibility of the application of this fractal antenna in the BSN was discussed. The radiation pattern s both in free-space and in on-body environment of the suggested fractal antenna was compared. The radiation pattern of the proposed antenna was compared with that of the loop antenna which was considered as a typical antenna in BSN system. In time domain, the Second-order Gaussian pulse response of the proposed antenna was measured. The path loss for the on-body channel were studied and measured, the comparison between the modelled and measured results was shown. From the study, the proposed fractal antenna was suitable in the on-body UWB communication.
ARMAR-III : A HUMANOID PLATFORM FOR PERCEPTION-ACTION INTEGRATION

In this paper, we present a new humanoid robot currently being developed for applications in human-centered enviro ments. In order for humanoid robots to enter human-centered environments, it is indispensable to equip them with manipulative, perceptive and communicative skills necessary f o real-time interaction with the environment and humans. The goal of our work is to provide reliable and highly integrated humanoid platforms which on the one hand allow the implementation and tests of various research activities and on th e other hand the realization of service tasks in a household sc enario. We introduce the different subsystems of the robot. W e present the kinematics, sensors, and the hardware and softw are architecture and describe different skills related to re al-time object localization, and motor control
A new dual to the Gács-Körner common information defined via the Gray-Wyner system

We consider jointly distributed random variables X and Y. After describing the Gács-Körner common information between the random variables from the viewpoint of the capacity region of the Gray-Wyner system, we propose a new notion of common information between the random variables that is dual to the Gács-Körner common information from this viewpoint in a well-defined sense. We characterize this quantity explicitly in terms of two auxiliary quantities that are asymmetric in nature, and illustrate the operational significance of these new quantities by characterizing a corner point of the solution to a problem of source coding with side-information in terms of them. We also contrast this new concept of common information for a pair of random variables with the Wyner common information of the random variables, which is also a kind of dual to the Gács-Körner common information.
Robust State Space Filtering with an Incremental Relative Entropy Tolerance

This paper considers robust filtering for a nominal Gaussian state-space model, when an incremental relative entropy tolerance is applied to each dynamical model component. The problem is formulated as a dynamic minimax game which is shown to admit a saddle point. The structure of the saddle point is characterized by applying and extending results presented earlier in [1] for static least-squares estimation. The resulting minimax filter takes the form of a risk-sensitive filter with a time varying risk sensitivity parameter, which depends on the tolerance bound applied to the matching model component. The least-favorable model is constructed and used to evaluate the performance of alternative filters. Simulations comparing the proposed risk-sensitive filter to a standard Kalman filter show a significant performance advantage when applied to the least-favorable model, and only a small performance loss for the nominal model. Index Terms commitment, dynamic minimax game, least-favorable model, relative entropy, risk-sensitive filtering, robust filtering.
Power allocation for MIMO-OFDM based CR with spatial constraints and CSI uncertainty

This paper presents optimal power allocation strategies for a MIMO (Multiple Input Multiple Output) OFDM (Orthogonal Frequency Division Multiplexing) based cognitive radio (CR) system. The proposed power allocation schemes maximize the downlink transmission rate of the CR users under spatial interference constraints, considering both the availability and absence of the primary user (PU) Channel State Information (CSI). It is demonstrated that the isotropic interference minimization in the absence of PU CSI can be formulated as a semi-definite program (SDP) while it reduces to linear interference constraints based CR user sum-rate maximization in the presence of PU CSI. Closed form power allocation expressions are derived for the above scenarios under a sum-trace interference relaxation. Further, we also consider the MIMO-OFDM rate maximization with CSI uncertainty and formulate separate optimal power allocation schemes for the stochastic and worst case scenarios. Simulation results presented validate the performance of the proposed schemes.