Miracle-dz/UniFAR-FTUs huggingface dataset:
Recognition of MicroArray Protein Crystals Images using Multi-scale Representations

Abstract: Micro-array protein crystal images are now routinely acquired automatically by CCD cameras. High-throughput automatic classification of protein crystals requires to alleviation of the time-consuming task of manual visual inspection. We propose a classification framework combined with a multi-scale image processing method for recognizing protein crystals and precipitates versus clear drops. The main two points of the processing method are the multi-scale Laplacian pyramid filters and histogram analysis techniques to find an effective feature vector. The processing steps include: 1. Tray well cropping using Radon Transform; 2. Droplet cropping using an ellipsoid Hough Transform; 3. Multi-scale image separation with Laplacian pyramidal filters; 4. Feature vector extraction from the histogram of the multi-scale boundary images. The feature vector combines geometric and texture features of each image and provides input to a feed forward binomial neural network classifier. Using human (expert crystallographers) classified images as ground truth, the current experimental results gave 86% true positive and 94% true negative rates (average true percentage is 90%) using an image database which contained over 2,000 images. To enable NESG collaborators to carry our crystal classification, a web-based Matlab© server was also developed. Users at other locations on the internet can input micro-array crystal image folders and parameters for training and testing processes through a friendly web interface. Recognition results are shown on the client side website and may be downloaded by a remote user as an Excel© spreadsheet file.
A Review on Opinion Mining Techniques and Challenges

With the evolution of web technology, there is a huge amount of data present in the web for the internet users who uses almost every available data for one to another usage in their day to day life. These users not only use data but also give their opinion in the form of feedback, thus generating additional useful information. By these user‟s opinions, views, feedback and suggestions available, it become really useful to explore, analyse and organize their views for better decision making. Nowdays,Merchants selling product on the web often ask their customers to review the products that they have purchased. As E-Commerce is becoming more and more popular, the number of customer reviews that a product receives grows rapidly. This makes it difficult for a customer to read them to make a decision on whether to purchase the product. In this research, we aim to mine and to summarize all the customer reviews of a product. And this summarization task involves the mining of the features of the product on which the customers have expressed their opinion on whether the opinions are positive or negative. This workflow of reviews is facilitated by a technique called Opinion Mining. Opinion itself means “what other people think”and mining illustrates “opinions explained in the form of positive, negative or neutral comments and quotes underlying the text”. This survey gives an overview of the efficient techniques which can be implemented, discuss about the applications, recent as well as future challenges yielding in the field of opinion mining and about tools which are used to track the opinion or polarity from user generated contents.
Journal Pre-proofs

This article concentrates on ground vision guided autonomous landing of a fixed-wing Unmanned Aerial Vehicle (UAV) within Global Navigation Satellite System (GNSS) denied environments. Cascaded deep learning models are developed and employed into image detection and its accuracy promoting for UAV autolanding, respectively. Firstly, we design a target bounding box detection network BboxLocate-Net to extract its image coordinate of the flying object. Secondly, the detected coordinate is fused into spatial localization with an extended Kalman filter estimator. Thirdly, a point regression network PointRefine-Net is developed for promoting detection accuracy once the flying vehicle’s motion continuity is checked unacceptable. The proposed approach definitely accomplishes the closed-loop mutual inspection of spatial positioning and image detection, and automatically improves the inaccurate coordinates within a certain range. Experimental results demonstrate and verify that our method outperforms the previous works in terms of accuracy, robustness and real-time criterions. Specifically, the newly developed BboxLocate-Net attaches over 500 fps, almost five times the published state-of-the-art in this field, with comparable localization accuracy.
JOB STRESS , COPING PROCESS AND INTENTIONS TO LEAVE

HE purpose of this study was to find out the causes of occupational stress amongst the software professionals, the ways adopted by them to cope up with it and their intention to leave employment in a sample of twenty six software professionals working in three different software companies. The sample included professionals working for full time, with varying demographic details. Qualitative methods were used to collect the data which included four focused group discussions and twenty six in-depth interviews. Hence, this study tries to bring readers attention to work related issues of the software professionals and the impact of it on them.
SIGMOD Executive Committee :

Motivated by applications in declarative data analysis, we study DatalogZ—an extension of Datalog with stratified negation and arithmetics over integers. Reasoning in this language is undecidable, so we present a fragment, called limit DatalogZ, that is powerful enough to naturally capture many important data analysis tasks. In limit DatalogZ, all intensional predicates with a numeric argument are limit predicates that keep only the maximal or minimal bounds on numeric values. Reasoning in limit DatalogZ is decidable if multiplication is used in a way that satisfies our linearity condition. Moreover, fact entailment in limit-linear DatalogZ is ∆ 2 -complete in combined and ∆ P 2 -complete in data complexity, and it drops to coNEXP and coNP, respectively, if only (semi-)positive programs are considered. We also propose an additional stability requirement, for which the complexity drops to EXP and P, matching the bounds for usual Datalog. Limit DatalogZ thus provides us with a unified logical framework for declarative data analysis and can be used as a basis for understanding the expressive power of the key data analysis constructs.
Determinants of job satisfaction and turnover among physicians.

DETERMINANTS OF JOB SATISFACION AND TURNOVER AMONG PHYSICIANS by Dustin P. Kaplan This study examined the determinants of job satisfaction and turnover among physicians at the University of California, San Diego. The relationship between eight predictor variables (administrative requirements, autonomy, ability to provide quality patient care, workload, work/private life conflict, pay, satisfaction with community, locus of control) and two dependent variables (job satisfaction, turnover intentions) was studied. Results indicated physicians' satisfaction with their current position was related to their perceptions of their autonomy, workload, work/private life conflict, pay, and community satisfaction. In addition, all predictors except workload had an impact on physicians' satisfaction with the current career. The only predictor that influenced the likelihood of leaving was community satisfaction. The implications of these findings are discussed.
Full dimension mimo (FD-MIMO): the next evolution of MIMO in LTE systems

Full dimension MIMO has attracted significant attention in the wireless industry and academia in the past few years as a candidate technology for the next generation evolution toward beyond fourth generation and fifth generation cellular systems. FD-MIMO utilizes a large number of antennas placed in a 2D antenna array panel for realizing spatially separated transmission links to a large number of mobile stations. The arrangement of these antennas on a 2D panel allows the extension of spatial separation to the elevation domain as well as the traditional azimuth domain. This article discusses features and performance benefits of FD-MIMO along with the ongoing standardization efforts in 3GPP to incorporate FD-MIMO features in the next evolution of LTE. Furthermore, a design of a 2D antenna array, which plays a key role in the implementation of FD-MIMO, is also discussed. Finally, in order to demonstrate the performance benefits of FD-MIMO, system-level evaluation results are provided.
Text generation using Monte-Carlo Sampling

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Robust formant detection using group delay function and stabilized weighted linear prediction

In this paper, we propose a robust spectral representation for detecting formants in heavily degraded conditions. The method combines the temporal robustness of the stabilized weighted linear prediction (SWLP) with the robustness of group delay (GD) function in the frequency domain. Weighting of the cost function in linear prediction analysis with the short-time energy of the speech signal improves the robustness of the resultant spectrum. It also improves the accuracy of the estimated resonances as the weighting function gives more weightage to the closed phase of the glottal cycle, which is also the high SNR region of the signal. The group delay spectrum computed as the sum of individual resonances denoted by the roots of the SWLP coefﬁcients, improves the robustness of weaker higher order resonances. The proposed SWLP-GD spectrum performs better than the conventional LP spectrum and the STRAIGHT spectrum in terms of spectral distortion measure and formant detection accuracies.
Semantic Modeling for Deductive Question-Answering

A description of techniques used for semantic modeling in a deductive question-answering system is given. The system maintains a dialog and is able to understand situations which can be expressed as a series of sequential time-frames. Specific relevant questions are asked by the system when it is unable to succeed in a given task. It can also provide reasons for its previous actions.
Automated Classification and Scoring of Smooth Pursuit Eye Movements in Presence of Fixations and Saccades

Ternary eye movement classification, which separates fixations, saccades, and smooth pursuit from the raw eye positional data, is extremely challenging. This paper develops new and modifies existing eye tracking algorithms to perform meaningful ternary classification. To aid the purpose of meaningful automated ternary classification a set of qualitative and quantitative scores is introduced to facilitate the assessment of classification performance. Experimental evaluation of the proposed methods is conducted using eye movement records recorded from 11 subjects at 1000Hz in response to a step and ramp stimuli eliciting fixations, saccades, and smooth pursuit. Performance results for the automated and manual classification are reported. CR Categories: I.6. 4 [Simulation and Modeling]: Model Validation and Analysis; J.7 [Computers in Other Systems]: Process control, Real time.
FINITE ELEMENT CENTER

Active control of the resistive wall mode (RWM) for DIII-D [Luxon and Davis, Fusion Technol. 8, 441(1985)] plasmas is studied using the MARS-F code [Y.Q. Liu, et al., Phys. Plasmas 7, 3681(2000)]. Control optimization shows that the mode can be stabilized up to the ideal wall beta limit, using the internal control coils (I-coils) and poloidal sensors located at the outboard midplane, in combination with an ideal amplifier. With the present DIII-D power supply model, the stabilization is achieved up to 70% of the range between no-wall and ideal-wall limits. Reasonably good quantitative agreement is achieved between MARS-F simulations and experiments on DIII-D and JET (Joint European Torus) [P.H. Rebut et al., Nucl. Fusion 25, 1011(1985)] on critical rotation for the mode stabilization. Dynamics of rotationally stabilized plasmas is well described by a single mode approximation; whilst a strongly unstable plasma requires a multiple mode description. For ITER [R. Aymar, P. Barabaschi, and Y. Shimomura, Plasma Phys. Controlled Fusion 44, 519(2002)], the MARS-F simulations show the plasma rotation may not provide a robust mechanism for the RWM stabilization in the advanced scenario. With the assumption of ideal amplifiers, and using optimally tuned controllers and sensor signals, the present feedback coil design in ITER allows stabilization of the n = 1 RWM for plasma pressures up to 80% of the range between the no-wall and ideal-wall limits.
Sequential Logic Synthesis Using Symbolic Bi-Decomposition

This paper uses under-approximation of unreachable states of a design to derive incomplete specification of combina-tional logic. The resulting incompletely-specified functions are decomposed to enhance the quality of technology-dependent synthesis. The decomposition choices are computed implicitly using novel formulation of symbolic bi-decomposition that is applied recursively to decompose logic in terms of simple primitives. The ability of BDDs to represent compactly certain exponentially large combinato-rial sets helps us to implicitly enumerate and explore variety of decomposition choices improving quality of synthesized circuits. Benefits of the symbolic technique are demonstrated in sequential synthesis of publicly available benchmarks as well as on the realistic industrial designs.
Lateral Variations in SKS Splitting Across the MAGIC Array, Central Appalachians

The eastern margin of North America has been shaped by several cycles of supercontinent assembly. These past episodes of orogenesis and continental rifting have likely deformed the lithosphere, but the extent, style, and geometry of this deformation remain poorly known. Measurements of seismic anisotropy in the upper mantle can shed light on past lithospheric deformation, but may also reveal contributions from present‐day mantle flow in the asthenosphere. Here we examine SKS waveforms and measure splitting of SKS phases recorded by the MAGIC experiment, a dense transect of seismic stations across the central Appalachians. Our measurements constrain small‐scale lateral variations in azimuthal anisotropy and reveal distinct regions of upper mantle anisotropy. Stations within the present‐day Appalachian Mountains exhibit fast splitting directions roughly parallel to the strike of the mountains and delay times of about 1.0 s. To the west, transverse component waveforms for individual events reveal lateral variability in anisotropic structure. Stations immediately to the east of the mountains exhibit complicated splitting patterns, more null SKS arrivals, and a distinct clockwise rotation of fast directions. The observed variability in splitting behavior argues for contributions from both the lithosphere and the asthenospheric mantle. We infer that the sharp lateral transition in splitting behavior at the eastern edge of the Appalachians is controlled by a change in anisotropy in the lithospheric mantle. We hypothesize that beneath the Appalachians, SKS splitting reflects lithospheric deformation associated with Appalachian orogenesis, while just to the east this anisotropic signature was modified by Mesozoic rifting.
NURS-Neural Machine Translation for Under-Resourced Scenarios Deliverables D2 and D3 on Linguistic and Named Entity Input Features and Terminology Handling within NMT

Rule-based machine translation is a machine translation paradigm where linguistic knowledge is encoded by an expert in the form of rules that translate from source to target language. While this approach grants total control over the output of the system, the cost of formalising the needed linguistic knowledge is much higher than training a corpus-based system, where a machine learning approach is used to automatically learn to translate from examples. In this paper, we describe different approaches to leverage the information contained in rulebased machine translation systems to improve a corpus-based one, namely, a neural machine translation model, with a focus on a low-resource scenario. Our results suggest that adding morphological information to the source language is as effective as using subword units in this particular setting.
Remote Sensing of the Reconnection Electric Field From In Situ Multipoint Observations of the Separatrix Boundary

A remote sensing technique to infer the local reconnection electric field based on in situ multipoint spacecraft observation at the reconnection separatrix is proposed. In this technique, the increment of the reconnected magnetic flux is estimated by integrating the in‐plane magnetic field during the sequential observation of the separatrix boundary by multipoint measurements. We tested this technique by applying it to virtual observations in a two‐dimensional fully kinetic particle‐in‐cell simulation of magnetic reconnection without a guide field and confirmed that the estimated reconnection electric field indeed agrees well with the exact value computed at the X‐line. We then applied this technique to an event observed by the Magnetospheric Multiscale mission when crossing an energetic plasma sheet boundary layer during an intense substorm. The estimated reconnection electric field for this event is nearly 1 order of magnitude higher than a typical value of magnetotail reconnection.
Development of artificial biofilms for use in mass transport studies

Artificial biofilms are useful laboratory tools allowing fundamental biofilm research to be conducted under controlled and reproducible conditions. Unfortunately, these synthetic biofilms, which generally consist of uniformly distributed bacteria throughout a polymer matrix, lack the structural heterogeneity (voids, microcolonies) often seen in natural biofilms. Thus data obtained from this type of biofilm may not accurately describe the behavior of a natural biofilm system. This thesis presents a procedure which can be used to construct an artificial biofilm that incorporates the structural heterogeneity observed in natural biofilm. Alginate was harvested from a mucoid strain of Pseudomonas aeruginosa, filter sterilized, and fixed to a glass slide where it served as the biofilm matrix. High densities of R aeruginosa were injected into specific locations within the alginate matrix to represent microcolonies similar to those found in natural biofilms. The heterogeneous artificial biofilm was then used to study the effects of a single microcolony on the mass transport of dissolved oxygen. The artificial biofilm was probed using a dissolved oxygen microelectrode to obtain concentration profiles through the biofilm. Visual analysis of these profiles indicated that the surrounding dissolved oxygen field was affected by a single microcolony (150 pm diameter), up to a distance of 1400 pm away from the microcolony, and that the mass transfer resistance due to the biofilm matrix itself was negligible. An artificial biofilm, containing uniformly distributed bacteria throughout an alginate matrix was used to evaluate a procedure used, to extract the microbial respiration kinetics from substrate concentration profiles. Only those profiles which were partially-penetrated with respect to oxygen could be analyzed using this procedure. This indicates that the applicability of this procedure was highly dependent upon the shape of the substrate concentration profile. Those profiles which could be analyzed showed good reproducibility in determining a value for Ks. Observation of these profiles showed that the diffusion boundary layer was significantly influenced by the flow velocity. The diffusion boundary layer decreased from 0.048 cm to 0.004 cm when the flow velocity was changed from low to high.
The MCAT Math Retrieval System for NTCIR-11 Math Track

This paper describes the participation of our MCAT search system in the NTCIR-11 Math-2 Task. The purpose of this task is to search mathematical expressions using hybrid queries containing both formulae and keywords. We introduce an encoding technique to capture the structure and content of the mathematical expressions. Each expression is accompanied by two types of automatically extracted textual information, namely words in context window and descriptions. In addition, we examine the improvement in ranking obtained by utilizing dependency graph of mathematical expressions and post-retrieval reranking method. The results show that the use of description and dependency graph together delivers better ranking performances than the use of context window. Furthermore, using both the description and context window together delivers even better results. The evaluation results also indicate that our reranking method is eective for improving the ranking performances.
Uniﬁed Enclave Abstraction and Secure Enclave Migration on Heterogeneous TEE Architectures

Nowadays, application migration becomes more and more attractive. For example, it can make computation closer to data sources or make service closer to end-users, which may signiﬁcantly decrease latency in edge computing. Yet, migrating applications among servers that are controlled by diﬀerent platform owners raises security issues. We leverage hardware-secured Trusted Execution Environment (TEE, aka., enclave) technologies, such as Intel SGX, AMD SEV, and ARM TrustZone, for protecting critical computations on untrusted servers. However, these hardware TEEs propose non-uniform programming abstractions and are based on heterogeneous architectures, which not only forces programmers to develop secure applications targeting some speciﬁc abstraction but also hinders the migration of protected applications. Therefore, we propose UniTEE which gives a uniﬁed enclave programming abstraction across the above three hardware TEEs by using a microkernel-based design and enables the secure enclave migration by integrating heterogeneous migration techniques. We have implemented the prototype on real machines. The evaluation results show the migration support incurs nearly-zero runtime overhead and the migration procedure is also eﬃcient.
Comparative analysis of image enhancement techniques

This paper propose a new image resolution enhancing technique based on interpolation of high frequency sub-bands generated using lifting wavelet transform (LWT) and low resolution input image. By using stationary wavelet transform (SWT) the edges are enhanced by introducing an intermediate stage. LWT is applied in order to decompose an input image in to different high frequency (HF) sub-bands. Then the high frequency subbands and also the input image are interpolated in parallel. Then estimated high frequency subbands are being modified by using high frequency subbands which are obtained from SWT. Then all these HF subbands and estimated LR image are reconstructed by inverse lifting wavelet transform (ILWT) to generate a new high resolution image. The quantitative and visual images of the described technique show the superiority of the proposed method over conventional and state-of-the-art methods.
Computer Aided Systems Theory EXTENDED ABSTRACTS

In this work, we tackle the problem of vehicle license plate localization in traffic images. Instead of modeling the appearance of the license plate, and inspired by recent advances in human pose estimation, we propose to use a CNN-based model trained to infer the numerical location of the four corners that define the limits of the license plate. Coordinate regression by means of di↵erentiable spatial to numerical transform (DSNT) layer [1] is used as a trade-o↵ between speed and accuracy. Preliminary results support the proposed methodology.