Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparing a Hand-crafted to an Automatically Generated Feature Set for Deep Learning: Pairwise Translation Evaluation

The automatic evaluation of machine translation (MT) has proven to be a very significant research topic. Most automatic evaluation methods focus on the evaluation of the output of MT as they compute similarity scores that represent translation quality. This work targets on the performance of MT evaluation. We present a general scheme for learning to classify parallel translations, using linguistic information, of two MT model outputs and one human (reference) translation. We present three experiments to this scheme using neural networks (NN). One using string based hand-crafted features (Exp1), the second using automatically trained embeddings from the reference and the two MT outputs (one from a statistical machine translation (SMT) model and the other from a neural ma-chine translation (NMT) model), which are learned using NN (Exp2), and the third experiment (Exp3) that combines information from the other two experiments. The languages involved are English (EN), Greek (GR) and Italian (IT) segments are educational in domain. The proposed language-independent learning scheme which combines information from the two experiments (experiment 3) achieves higher classification accuracy compared with models using BLEU score information as well as other classification approaches, such as Random Forest (RF) and Support Vector Machine (SVM).
Proteccionism under R & D Policy : Felipa de Mello-Sampayo Innovation Rate and Welfare

The pressure from national lobbies may lead governments to shift from an optimal into a non-optimal innovation policy. This paper examines the growth and welfare effects of optimal and non-optimal innovation policies. The non-optimal policy corresponds to a subsidy for national innovators that is equivalent to an optimal policy of incentives (tax cuts) to foreign investors. Since we are assessing what can nationals do with the support that could be oriented to foreign firms, we are measuring what the economy loses for not supporting foreign firms. We find welfare loss when supporting national R&D instead of foreign R&D. We conclude that the same support given to innovation can produce strikingly different outcomes depending on who receives the support. JEL Classification: F21; H21; O40.
Lunar cycle, seasonal variation, and prevalence of emergency urological presentations: correlation or coincidence?—A preliminary report

Abstract Objective: The objective of this study was to conduct a pilot study to determine the prevalence and patterns of emergency urological presentations and to evaluate their relationship with the lunar cycle and seasonal variation. Methods: Medical records of subjects that presented with urological pathology to the Emergency Department during the 2017 calendar year were retrospectively reviewed. The data extracted included demographic details, date and day of presentation, presenting complaints, investigations, radiological findings, and final diagnosis. Associations between emergent presentations and the lunar phase and seasonal variation were determined. Results: A total of 199 subjects were enrolled. The median participant age was 49 (interquartile range 31–64) years with the majority (n = 136, 68.3%) being male. Cystitis (n = 55, 27.6%), prostate cancer (n = 30, 15.1%), benign prostatic hypertrophy (n = 29, 14.6%), and urolithiasis (n = 29, 14.6%) were the most common clinical diagnosis. There were 96 (48.2%) patients who presented during the waxing moon phase, whereas 85 (42.7%) presented during the waning moon phase, 11 (5.6%) presented on the day of full moon, and 7 (3.5%) patients presented on the day of the new moon. Most patients presented during the summer months (n = 61, 30.7%). There was no significant association between the lunar cycle and emergent urological presentations (p = 0.99). Conclusion: In this pilot study, there was no significant association between the lunar cycle and emergent urological presentations. However, during the summer months more urology-related emergency presentations to the Emergency Department were observed.
Phylogenomics — principles, opportunities and pitfalls of big‐data phylogenetics

Phylogenetics is the science of reconstructing the evolutionary history of life on Earth. Traditionally, phylogenies were constructed using morphological data only, but the introduction of Sanger sequencing and PCR in the late 1970s enabled genetic information to be incorporated into phylogenetic analyses. Early phylogenetic studies employing multilocus analyses contributed greatly to our knowledge of phylogenetic history and challenged some well-established views of the relationships among many groups of plants and animals. Since the publication of these pioneering studies, significant methodological advances in both sequencing and analytical techniques have been made, and molecular phylogenies are now broadly accepted to represent robust hypotheses of organismal relationships. Next-generation sequencing techniques, developed in the mid-2000s, revolutionized DNA sequencing and led to a dramatic reduction in sequencing cost per nucleotide and a sharp increase in data generation speed. As a result, the generation of unprecedented amounts of sequence data for both model and nonmodel organisms has become affordable. This development has transformed the field of molecular phylogenetics into phylogenomics – where genome-scale data are obtained from multiple samples at once at a much reduced cost (Mardis, 2011). The phylogenomic pipeline can be very complex, presenting an overwhelming array of methodologies available for the acquisition, manipulation, analysis and interpretation of massive datasets. Researchers also have to overcome the challenges of sequencing strategy design, identification of orthologous loci, model selection and phylogeny estimation. This can be particularly daunting for researchers new to the field – both students and established scientists – who wish to delve into novel methods and data to reconstruct the evolution of their study group. Here we present an entry-level overview of the theory
Stakeholder Pressures and Corporate Environmental Strategies: A Meta-Analysis

Stakeholder pressures and corporate environmental strategies continue to be important topics of corporate sustainability. Limited by sample size, there is a lack of general conclusions on which groups of stakeholder pressures are the main drivers of environmental strategies. Amassing a database of 58 empirical studies, the authors divided stakeholder pressures into four groups—internal, coercive, market, and social pressure—and explored the relationship between different pressures and environmental strategies by conducting a meta-analysis. The main result shows that internal pressure is the main driver of environmental strategies. Further empirical results show that stakeholder pressures could have a larger effect on corporate environmental strategies in developed countries and that non-manufacturing firms could change their environmental strategies more easily than manufacturing firms. The results provide the practical implication that a green industry transition is strongly needed in the manufacturing industry, especially for polluting industries, and that firms in polluting industries should implement environmental strategy changes in the future. This paper contributes to clarifying the relationship between stakeholder pressures and corporate environmental strategies based on a meta-analysis.
Teaching Critical Thinking Skills: Literature Review.

Critical Thinking (CT) has been recognized as one of the most important thinking skills and one of the most important indicators of student learning quality. In order to develop successful critical thinkers, CT must be incorporated into the curriculum content and teaching approaches and sequenced at all grade levels. This research provides a systematic review of the extant literature on teaching CT skills. The comprehensive review led to the building of a conceptual framework that discusses the four main debates among the researchers engaged in the field of teaching CT. One of these debates; can technology promote students CT skills? Overall, the study of actual practices indicates that teaching approaches tend to focus on subject content rather than CT development. The results indicate a gap in teaching CT skills in terms of innovative methods and particularly in the use of new technologies. They also highlight the need for further research that investigates new approaches for teaching CT skills.
Topological Characterization of the Multi-feature based Network in Patients with Alzheimer's Disease and Mild Cognitive Impairment

Characterization of morphological organization pattern in individual brain has long been an open question. Recently, a novel single-subject network that built upon multiple morphological features (MFN) was introduced [1], which exhibited extraordinary power in diagnosing patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI). This study aims to characterize the cortico-cortical topological organization in MCI and AD cohorts via the MFN, and uncover the structural substrate that leads to the high classification accuracy. The MFNs were constructed for 165 normal controls (NCs), 221 patients with MCI, and 142 patients with AD from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Results from graph theoretical analysis showed ‘small-world’ property and modular organization of the MFN in three groups; as well as increased local clustering and characteristic path length, and altered hub regions in patients with AD and MCI. More importantly, we found the primary atrophic regions were accompanied by increased number of in-flow connections; whereas, the connections that linked to the less atrophic regions were mostly out-flow connections. This phenomenon, we speculated, might indirectly reflect the trophic supporting effects in the brain. Our results demonstrated the basic organizational principles of the MFN and its changes in patients with AD and MCI, providing important implications of what makes the MFN powerful in auto-diagnosis of mental disorders.
Correlation of Laser Speckle Image with Metal Surface Changes due to Corrosion Process

When a surface is illuminated by a laser beam, a speckle pattern will appear in the form of dark and bright spots which are randomly distributed according to the surface roughness. Hence, surface deformation will produce a different speckle pattern. This study aims to see the correlation of surface deformation with changes in speckle pattern, so that it can qualitatively estimate metal resistance and observe surface characteristics due to corrosion process. The metal used were iron and carbon steel which were immersed in 0.1 M HNO3 acid solution. Every 10 minutes period, speckle images were captured using a CMOS camera and processed using 2-D Coefficient Correlation method and Feature Extraction of GLCM. The results showed a correlation between corroded metal and speckle pattern with precision rate of 2-D Coefficient Correlation was not less than 97.3% and 91.1% for GLCM Method. Analysis of 2-D Coefficient Correlation can be used to estimate that carbon steel was more resistant than iron. Meanwhile, the GLCM contrast and homogeneity features can be used to represent changing metal surface characteristics, but it was difficult to estimate metal resistance since the type of corrosion occurred in this study was different for each type of metal.
AGARNet: Adaptively Gated JPEG Compression Artifacts Removal Network for a Wide Range Quality Factor

Most of existing compression artifacts reduction methods focused on the application for low-quality images and usually assumed a known compression quality factor. However, images compressed with high quality should also be manipulated because even small artifacts become noticeable when we enhance the compressed image. Also, the use of quality factor from the decoder is not practical because there are too many recompressed or transcoded images whose quality factor are not reliable and spatially varying. To address these issues, we propose a quality-adaptive artifacts removal network based on the gating scheme, with a quality estimator that works for a wide range of quality factor. Specifically, the estimator gives a pixel-wise quality factor, and our gating scheme generates gate-weights from the quality factor. Then, the gate-weights control the magnitudes of feature maps in our artifacts removal network. Thus, our gating scheme guarantees the proposed network to perform adaptively without changing the parameters according to the change of quality factor. Moreover, we exploit the Discrete Cosine Transform (DCT) scheme with 3D convolution for capturing both spatial and frequency dependencies of images. Experiments show that the proposed network provides better performance than the state-of-the-art methods over a wide range of quality factor. Also, the proposed method provides robust results in real-world scenarios such as the manipulation of transcoded images and videos.
Battery Batch Impedance Analysis for Pack Design

The impedance of four different battery cell model batches have been measured and analyzed. Impedance have been measured at four frequencies and at three states of charger. Various statistical methods have been used to analyze measurement results and provide insight in impedance variation and distribution characteristics. A separate correlation analysis between impedance and nominal capacity has been carried out. Such correlation information can significantly improve battery testing speed and performance estimation. The obtained measurement results are useful for further battery model development - to introduce manufacturing variance as well as for the development of cell sorting methodology which is performed prior to battery pack assembly.
Sub-stochiometric MoO3 for intermediate band solar cells

We report on surface topographical, chemical and optoelectronic properties of sub-stoichiometric MoO<inf>3</inf> thin films, MoO<inf>3-x</inf>, made by pulsed laser deposition. MoO<inf>3-x</inf> is a promising candidate for a sustainable intermediate band material. The absorption coefficient for photon energies below the MoO<inf>3</inf> bandgap increases for films made in oxygen poor environments, and is explained by the occurrence of states in the bandgap related to oxygen vacancies and to phase mixtures of MoO<inf>3-x</inf> compositions with 0<x<1 in the films. The MoO<inf>3-x</inf> film properties are closely linked to the film deposition parameters.
An Overlay for Rapid FPGA Debug of Machine Learning Applications

FPGAs show promise as machine learning accelerators for both training and inference. Designing these circuits on reconfigurable technology is challenging, especially due to bugs that only manifest on-chip when the circuit is running at speed. In this paper, we propose a flexible debug overlay family that provides software-like debug times for machine learning applications. At compile time, the overlay is added to the design and compiled. At debug time, the overlay can be configured to record statistical information about identified weight and activation matrices; this configuration can be changed between debug iterations allowing the user to record a different set of matrices, or record different information about the observed matrices. Importantly, no recompilation is required between debug iterations. Although the flexibility of our overlay suffers some overhead compared to fixed instrumentation, we argue that the ability to change the debugging scenario without requiring a recompilation may be compelling and outweigh the disadvantage of higher overhead for many applications.
Discovering the Influencing Factors of Physical Gig Economy Usage: Quantitative Approach on Clients9 Perception

Physical Gig Economy (PGE) in Indonesia has rapid growth in the last few years. Unfortunately, a large gap among PGE services occurred. Compared with ride-hailing services with highly frequent transactions, cleaning and mechanical services have had few transactions. This study aims to identify and analyze the factors that influence clients to use PGE services. Previous studies about users’ intention were synthesized to develop the research model and hypothesis. Factors that are thought to have an influence on client behavior and intention are platform quality, trust, social influence, perceived risk, hedonic motivation, and economic benefits. Furthermore, a quantitative approach with Partial Least Squares Structural Equation Modeling (PLS-SEM) and 318 valid respondents is demonstrated. The results show that hedonic motivation is the most influencing factor followed by economic benefits, trust, and perceived platform quality. This study also informs that social influence only affects client on the early usage of PGE. Having the knowledge of these factors, PGE operators could develop the right strategies to further expand their business and attract new clients.
Shrink It or Shed It! Minimize the Use of LSQs in Dataflow Designs

When applications have unpredictable memory accesses or irregular control flow, dataflow circuits overcome the limitations of statically scheduled high-level synthesis (HLS). If memory dependences cannot be determined at compile time, dataflow circuits rely on load-store queues (LSQs) to resolve the dependences dynamically, as the circuit runs. However, when employed on reconfigurable platforms, these LSQs are resource-expensive, slow, and power-consuming. In this work, we explore techniques for reducing the cost of the memory interface in dataflow designs. Apart from exploiting standard memory analysis techniques, we present a novel approach which relies on the topology of the control and dataflow graphs to infer memory order with the purpose of minimizing the LSQ size and complexity. On benchmarks obtained automatically from C code, we show that our approach results in significant area reductions, as well as increased performance, compared to naive solutions.
Modeling disease progression in Multiple Myeloma with Hopfield networks and single-cell RNA-seq

Associative memories in Hopfield's neural networks are mapped to gene expression pattern to model different paths of disease progression towards Multiple Myeloma (MM). The model is built using single cell RNA-seq data from bone marrow aspirates of MM patients as well as patients diagnosed with Monoclonal Gammopathy of Undetermined Significance (MGUS) and Smoldering Multiple Myeloma (SMM), two medical conditions that often progress to full MM. Results: We identify different clusters of MGUS, SMM, and MM cells, map them to Hopfield associative memory patterns, and model the dynamics of transition between the different patterns. The model is then used to identify genes that are differentialy expressed across different MM stages and whose simultaneous inhibition is associated to a delayed disease progression.
A Sensitivity Tunable Accelerometer Based on Series-Parallel Electromechanically Coupled Resonators Using Mode Localization

This paper reports a novel mode-localized resonant accelerometer, which can keep high sensitivity even over a wide range. To improve the adjustability of sensitivity, a new four degree of freedom (4-DoF) series-parallel resonator array is proposed. Three sensing resonators are mechanically coupled together in series by folding beams and the fourth sensitivity-tuning resonator is electrically coupled in parallel with the sensing system. When the stiffness perturbation caused by acceleration occurs, a change of modal amplitude ratio due to the mode localization phenomenon can be detected. By tuning the voltage on the sensitivity-tuning resonator, the sensitivity of the accelerometer can be altered to keep the amplitude ratio within a reasonable range. The theoretical model of the accelerometer is established and analyzed by numerical method. To confirm the feasibility of the design, a device fabricated by SOI-MEMS technology is tested under open-loop circuit. The measured amplitude ratio sensitivity varies from 1.14/g to 23.37/g with the change of the tuning voltage, and the sensitivity adjustment range reaches 2050%. [2019-0199]
All-Pass Filter Design Using Blaschke Interpolation

Obtaining a rational all-pass filter that matches a target phase response is a common problem in signal processing. Typical approaches have generally focused on optimizing filter coefficients while minimizing the deviation from the target phase response. However, these approaches typically do not offer a guarantee of exact match of the phase or any conditions on group delay. In this letter, we propose a Blaschke interpolation based all-pass filter design, wherein, if the phase response is known at $n$ distinct frequencies, an all-pass filter that exactly satisfies the target values can be obtained. Moreover, the group delay at the interpolating points can be optimized or tuned to control the phase response for remaining frequencies. Simulations reveal that the design method is able to produce filters that closely match the target phase response accurately with much lower complexity than prior approaches.
Observer-Extended Direct Method for Collision Monitoring in Robot Manipulators Using Proprioception and IMU Sensing

In this letter a novel method for accurate and high-bandwidth real-time monitoring of robot collisions is presented. To the authors’ knowledge this is the first time the so called direct method, which is mathematically the simplest and theoretically the ideal one, has been realized at practically relevant levels. For this, joint velocity and acceleration of serial chain robots are initially estimated using observer techniques that fuse joint position, Cartesian acceleration and angular velocity measurements. Consequently, this algorithm, which also extends our previous work in velocity and acceleration estimation, together with the available robot dynamics model are utilized to algebraically monitor external forces applied to the robot. Specifically, the proposed sensor fusion setup increases estimation bandwidth and decreases detection uncertainties compared to existing methods. Moreover, since neither inversion of large matrices nor their derivatives are required, our approach also shows increased numerical stability. Finally, the developed algorithm is evaluated based on a realistic simulation with the consideration of all parasitic effects and experimentally with a 7-DoF flexible joint robot.
A Delay Sensitive Cluster Based Routing Protocol for Wireless Sensor Networks

There are different applications of Wireless Sensor Networks (WSNs). It can be deployed in various application areas such as monitoring of environment, surveillance, industrial automation, health-care, agriculture etc. A sensor node is usually a resource constrained device which has limited energy, processing power and memory. One of the important aspects of QoS aware routing protocols in WSNs is to send the delay sensitive data to a control station known as sink. In this paper, a delay sensitive cluster based routing protocol for wireless sensor networks is proposed. The purpose of clustering is to reduce energy consumption among the sensor nodes. The data forwarding mechanism of the protocol attempts to reduce end to end delay of delay sensitive traffic in reaching the sink. The protocol is evaluated through simulation using NS2.