Miracle-dz/UniFAR-FTUs huggingface dataset:
NRC-CNRC Systems for Upper Sorbian-German and Lower Sorbian-German Machine Translation 2021

We describe our neural machine translation systems for the 2021 shared task on Unsupervised and Very Low Resource Supervised MT, translating between Upper Sorbian and German (low-resource) and between Lower Sorbian and German (unsupervised). The systems incorporated data filtering, backtranslation, BPE-dropout, ensembling, and transfer learning from high(er)-resource languages. As measured by automatic metrics, our systems showed strong performance, consistently placing first or tied for first across most metrics and translation directions.
The Role of Leadership Practices in Supporting Organisational Citizenship Behaviours (An Empirical Study)

Mohamed A. Amin, Mohamed M. Elmetwaly, Wael Hassan ElGaraihy, Wael Mohammed Saleh, Mahmoud M. H. Alayis, Assistant Professor, Business Administration Department, College of Applied Studies and Community Service, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia. Assistant Professor, Marketing Department, College of Applied Studies and Community Service, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia. Associate professor of management, Business Administration Department, College of Applied Studies and Community Service, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia. Email: moaaahmed@iau.edu. sa, mhmetwally@iau.edu. sa, whgaraihy@iau.edu. sa, wmSaleh@iau.edu. sa, mmhali@iau.edu. sa
Performance evaluation of CMOS ring-oscillators with source/drain regions fabricated by asymmetric/symmetric ion-implantation

0.5 /spl mu/m CMOS ring-oscillators with LDD-type surface-channel n-MOSFETs and EPS-type buried-channel p-MOSFETs with asymmetric/symmetric source/drain fabricated by four kinds of ion-implantation methods were measured for evaluating the circuit performance. The ion-implantation methods were correlated to supply-current/oscillation-frequency/delay-power product and substrate current of the ring-oscillator. The most preferable implantation method was the symmetric 7/spl deg//spl times/4-implantation in terms of circuit performance, asymmetry/mismatch and punchthrough immunity of CMOSFET.
A transient induction motor model including saturation and deep bar effect

Transient cage induction motor models for use in inverter-fed drives and controllers are reviewed. A simple transient model is presented that includes rotor deep bar effect and magnetic saturation of the magnetising and rotor leakage flux paths. The improved model requires motor details in the form of simple impedance versus frequency characteristics which can be obtained from a variety of external sources. These can range typically from detailed steady-state finite-element solutions to simple experimental measurements. The model is verified experimentally using a 75 kW, 4 pole vector controlled AC motor drive.
Application of species-specific primers to estimate the in situ diet of Bythotrephes [Cladocera, Onychopoda] in its native European range via molecular gut content analysis

Abstract The study of invasive species often focuses on regions of recent introduction rather than native habitats. Understanding an invasive species in its natural environment, however, can provide important insights regarding the long-term outcome of invasions. In this study we investigated the diet of the invasive spiny water flea, Bythotrephes longimanus, in two Austrian perialpine lakes, where it is native. The gut contents of wild-caught Bythotrephes individuals were estimated by quantitative polymerase chain reaction, targeting species-specific fragments of the barcoding region of the cytochrome c oxidase I gene of potential prey. The observed prey spectrum of Bythotrephes in the study lakes consisted primarily of Eudiaptomus gracilis and Diaphanosoma brachyurum. The Daphnia longispina complex, Leptodora kindtii and Mesocyclops leuckarti also contributed to the diet. Results indicate that Bythotrephes is a generalist feeder with a preference for epilimnetic prey.
CSF-Derived CD4+ T-Cell Diversity Is Reduced in Patients With Alzheimer Clinical Syndrome

Background and Objectives Patients with Alzheimer dementia display evidence of amyloid-related neurodegeneration. Our focus was to determine whether such patients also display evidence of a disease-targeting adaptive immune response mediated by CD4+ T cells. To test this hypothesis, we evaluated the CSF immune profiles of patients with Alzheimer clinical syndrome (ACS), who display clinically defined dementia. Methods Innate and adaptive immune profiles of patients with ACS were measured using multicolor flow cytometry. CSF-derived CD4+ and CD8+ T-cell receptor repertoire genetics were measured using next-generation sequencing. Brain-specific autoantibody signatures of CSF-derived antibody pools were measured using array technology or ELISA. CSF from similar-age healthy controls (HCs) was used as a comparator cohort. Results Innate cells were expanded in the CSF of patients with ACS in comparison to HCs, and innate cell expansion increased with age in the patients with ACS, but not HCs. Despite innate cell expansion in the CSF, the frequency of total CD4+ T cells reduced with age in the patients with ACS. T-cell receptor repertoire genetics indicated that T-cell clonal expansion is enhanced, and diversity is reduced in the patients with ACS compared with similar-age HCs. Discussion Examination of CSF indicates that CD4+ T cell–mediated adaptive immune responses are altered in patients with ACS. Understanding the underlying mechanisms affecting adaptive immunity will help move us toward the goal of slowing cognitive decline.
Newton methods based convolution neural networks using parallel processing

Training of convolutional neural networks is a high dimensional and a non-convex optimization problem. At present, it is inefficient in situations where parametric learning rates can not be confidently set. Some past works have introduced Newton methods for training deep neural networks. Newton methods for convolutional neural networks involve complicated operations. Finding the Hessian matrix in second-order methods becomes very complex as we mainly use the finite differences method with the image data. Newton methods for convolutional neural networks deals with this by using the sub-sampled Hessian Newton methods. In this paper, we have used the complete data instead of the sub-sampled methods that only handle partial data at a time. Further, we have used parallel processing instead of serial processing in mini-batch computations. The results obtained using parallel processing in this study, outperform the time taken by the previous approach.
Vibrational Modes and Terahertz Physical Phenomena Underpinning ZIF-71 Metal-Organic Framework

The zeolitic imidazole framework ZIF-71 has the potential to outperform other well-studied metal-organic frameworks due to its intrinsic hydrophobicity and large pore size. However, a detailed description of its complex physical phenomena and structural dynamics has been lacking thus far. Herein, we elucidated all vibrational modes of ZIF-71 using high-resolution inelastic neutron scattering and synchrotron radiation infrared spectroscopy in conjunction with density functional theory calculations. We discover low-energy collective modes, such as gateopening and shearing mechanisms that may affect the functions and framework stability of ZIF-71. Its single-crystal mechanical properties are further unraveled by nanoscale analytics.
Performance analysis of a command destruct subsystem of the spacelift range system

The paper presents an analysis of a typical command destruct subsystem (CDS) of the U.S. Air Force Spacelift Range System. The command destruct system sends a signal comprising a sequence of pairs of tone signal pulses to the space vehicle, in case the space vehicle significantly deviates from its intended trajectory and results in safety concerns. By necessity, due to the safety-critical nature of such a system, the command link for the CDS is designed for relatively high signal-to-noise ratio (SNR) and it is generally considered that high detection performance can be achieved due to the high SNR. However, a closer analysis shows the presence of some non-ideal factors which can potentially limit the achievable performance of the CDS system independent of the SNR. More specifically the paper analyzes the impact of narrowband tone filters present in a typical CDS receiver on the detection performance of the system. Another important source of possible performance degradation is the spill over effect of the FFT processor.
Co-domain Symmetry for Complex-Valued Deep Learning

We study complex-valued scaling as a type of symmetry natural and unique to complex-valued measurements and representations. Deep Complex Networks (DCN) extend real-valued algebra to the complex domain without addressing complex-valued scaling. SurReal extends manifold learning to the complex plane, achieving scaling invariance with manifold distances that discard phase information. Treating complex-valued scaling as a co-domain transformation, we design novel equivariant/invariant layer functions and architectures that exploit co-domain symmetry. We also propose novel complex-valued representations of RGB images, where complex-valued scaling indicates hue shift or correlated changes across color channels. Benchmarked on MSTAR, CIFAR10, CIFAR100, and SVHN, our co-domain symmetric (CDS) classifiers deliver higher accuracy, better generalization, more robustness to co-domain transformations, and lower model bias and variance than DCN and SurReal with far fewer parameters.
A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulation

Automated Driving Systems (ADSs) have seen rapid progress in recent years. To ensure the safety and reliability of these systems, extensive testings are being conducted before their future mass deployment. Testing the system on the road is the closest to real-world and desirable approach, but it is incredibly costly. Also, it is infeasible to cover rare corner cases using such real-world testing. Thus, a popular alternative is to evaluate an ADS's performance in some well-designed challenging scenarios, a.k. a. scenario-based testing. High-fidelity simulators have been widely used in this setting to maximize flexibility and convenience in testing what-if scenarios. Although many works have been proposed offering diverse frameworks/methods for testing specific systems, the comparisons and connections among these works are still missing. To bridge this gap, in this work, we provide a generic formulation of scenario-based testing in high-fidelity simulation and conduct a literature review on the existing works. We further compare them and present the open challenges as well as potential future research directions.
The packet number space debate in multipath QUIC

With a standardization process that attracted much interest, QUIC can been seen as the next general-purpose transport protocol. Still, it does not provide true multipath support yet, missing some use cases that Multipath TCP addresses. To fill that gap, the IETF recently adopted a Multipath proposal merging several proposed designs. While it focuses on its core components, there still remains one major design issue: the amount of packet number spaces that should be used. This paper provides experimental results with two different Multipath QUIC implementations based on NS3 simulations to understand the impact of using one packet number space per path or a single packet number space for the whole connection. Our results show that using one packet number space per path makes Multipath QUIC more resilient to the receiver's heuristics to acknowledge packets and detect duplicates.
Simple Alternatives to the Common Correlated Effects Model

We study estimation of factor models in a fixed-T panel data setting and significantly relax the common correlated effects (CCE) assumptions pioneered by Pesaran (2006) and used in dozens of papers since. In the simplest case, we model the unobserved factors as functions of the cross-sectional averages of the explanatory variables and show that this is implied by Pesaran's assumptions when the number of factors does not exceed the number of explanatory variables. Our approach allows discrete explanatory variables and flexible functional forms in the covariates. Plus, it extends to a framework that easily incorporates general functions of cross-sectional moments, in addition to heterogeneous intercepts and time trends. Our proposed estimators include Pesaran's pooled correlated common effects (CCEP) estimator as a special case. We also show that in the presence of heterogeneous slopes our estimator is consistent under assumptions much weaker than those previously used. We derive the fixed-T asymptotic normality of a general estimator and show how to adjust for estimation of the population moments in the factor loading equation.
Robust Online Selection with Uncertain Offer Acceptance

Online advertising has motivated interest in online selection problems. Displaying ads to the right users benefits both the platform (e.g., via pay-per-click) and the advertisers (by increasing their reach). In practice, not all users click on displayed ads, while the platform’s algorithm may miss the users most disposed to do so. This mismatch decreases the platform’s revenue and the advertiser’s chances to reach the right customers. With this motivation, we propose a secretary problem where a candidate may or may not accept an offer according to a known probability p. Because we do not know the top candidate willing to accept an offer, the goal is to maximize a robust objective defined as the minimum over integers k of the probability of choosing one of the top k candidates, given that one of these candidates will accept an offer. Using Markov decision process theory, we derive a linear program for this max-min objective whose solution encodes an optimal policy. The derivation may be of independent interest, as it is generalizable and can be used to obtain linear programs for many online selection models. We further relax this linear program into an infinite counterpart, which we use to provide bounds for the objective and closed-form policies. For p ≥ p∗ ≈ 0.6, an optimal policy is a simple threshold rule that observes the first p1/(1−p) fraction of candidates and subsequently makes offers to the best candidate observed so far. *Georgia Institute of Technology, sperez@gatech.edu †Georgia Institute of Technology, mohit.singh@isye.gatech.edu ‡Georgia Institute of Technology, atoriello@isye.gatech.edu 1 ar X iv :2 11 2. 00 84 2v 1 [ cs . G T ] 1 D ec 2 02 1
Power Interruption Costs to Commercial Customers of Electricity in Korea

This paper presents the result of the estimation of the power interruption costs of Korean commercial customers. Commercial customers are usually located in urban areas and considered very important because a power interruption on them can expand to additional damage to related users. Their power interruption costs were calculated through a survey with the process of data selection. Also, the power interruption cost for each customer's business type was calculated and compared with that of foreign cases.
Inducing Causal Structure for Interpretable Neural Networks

In many areas, we have well-founded insights about causal structure that would be useful to bring into our trained models while still allowing them to learn in a data-driven fashion. To achieve this, we present the new method of interchange intervention training (IIT). In IIT, we (1) align variables in a causal model (e.g., a deterministic program or Bayesian network) with representations in a neural model and (2) train the neural model to match the counterfactual behavior of the causal model on a base input when aligned representations in both models are set to be the value they would be for a source input. IIT is fully differentiable, flexibly combines with other objectives, and guarantees that the target causal model is a causal abstraction of the neural model when its loss is zero. We evaluate IIT on a structural vision task (MNIST-PVR), a navigational language task (ReaSCAN), and a natural language inference task (MQNLI). We compare IIT against multi-task training objectives and data augmentation. In all our experiments, IIT achieves the best results and produces neural models that are more interpretable in the sense that they more successfully realize the target causal model.
SIDNet: Learning Shading-Aware Illumination Descriptor for Image Harmonization

Image harmonization aims at adjusting the appearance of the foreground to make it more compatible with the background. Without exploring background illumination and its effects on the foreground elements, existing works are incapable of generating a realistic foreground shading. In this paper, we decompose the image harmonization task into two sub-problems: 1) illumination estimation of the background image and 2) re-rendering of foreground objects under background illumination. Before solving these two sub-problems, we first learn a shading-aware illumination descriptor via a well-designed neural rendering framework, of which the key is a shading bases module that generates multiple shading bases from the foreground image. Then we design a background illumination estimation module to extract the illumination descriptor from the background. Finally, the Shading-aware Illumination Descriptor is used in conjunction with the neural rendering framework (SIDNet) to produce the harmonized foreground image containing a novel harmonized shading. Moreover, we construct a photo-realistic synthetic image harmonization dataset that contains numerous shading variations with image-based lighting. Extensive experiments on both synthetic and real data demonstrate the superiority of the proposed method, especially in dealing with foreground shadings.
Interpretable Deep Learning-Based Forensic Iris Segmentation and Recognition

Iris recognition of living individuals is a mature biometric modality that has been adopted globally from governmental ID programs, border crossing, voter registration and de-duplication, to unlocking mobile phones. On the other hand, the possibility of recognizing deceased subjects with their iris patterns has emerged recently. In this paper, we present an end-to-end deep learning-based method for postmortem iris segmentation and recognition with a special visualization technique intended to support forensic human examiners in their efforts. The proposed postmortem iris segmentation approach outperforms the state of the art and - in addition to iris annulus, as in case of classical iris segmentation methods - detects abnormal regions caused by eye decomposition processes, such as furrows or irregular specular highlights present on the drying and wrinkling cornea. The method was trained and validated with data acquired from 171 cadavers, kept in mortuary conditions, and tested on subject-disjoint data acquired from 259 deceased subjects. To our knowledge, this is the largest corpus of data used in postmortem iris recognition research to date. The source code of the proposed method are offered with the paper. The test data will be available through the National Archive of Criminal Justice Data (NACJD) archives.
Bio-potential amplifier for potential gradient measurements

This work proposes a bio-potential amplifier suitable for measurements from an electric potential gradient sensor, in electro-encephalography (EEG). The sensor is an array made by three electrodes placed on the vertices of an equilateral triangle of reduced size. Measuring the gradient requires small separation between electrodes hence, very low amplitude signals, of a few μV, are obtained. Therefore, it is important to minimize amplifier noise and electromagnetic interference effects. In the proposed scheme, the first stage is a passive and balanced ac-coupling network adapted to the gradient configuration and the second stage is an 80 dB gain amplifier. The implementation requires a reduced number of components. Therefore, the circuit can be mounted just above the electrodes (active electrodes). The proposed amplifier was built and tested. It achieves a CMRR of 125dB at 50 Hz and an equivalent input noise voltage of 0,3μV RMS in the band 0.5 – 500 Hz. Finally, some preliminary results in the detection of occipital alpha rhythm are presented.
Architecting and Visualizing Deep Reinforcement Learning Models

To meet the growing interest in Deep Reinforcement Learning (DRL), we sought to construct a DRL-driven Atari Pong agent and accompanying visualization tool. Existing approaches do not support the flexibility required to create an interactive exhibit with easily-configurable physics and a human-controlled player. Therefore, we constructed a new Pong game environment, discovered and addressed a number of unique data deficiencies that arise when applying DRL to a new environment, architected and tuned a policy gradient based DRL model, developed a real-time network visualization, and combined these elements into an interactive display to help build intuition and awareness of the mechanics of DRL inference.
Formal Verification of a Controller Implementation in Fixed-Point Arithmetic

For the implementations of controllers on digital processors, certain limitations, e.g. in the instruction set and register length, need to be taken into account, especially for safety-critical applications. This work aims to provide a computer-certified inductive definition for the control functions that are implemented on such processors accompanied with the fixed-point data type in a proof assistant. Using these inductive definitions we formally ensure correct realization of the controllers on a digital processor. Our results guarantee overflow-free computations of the implemented control algorithm. The method presented in this paper currently supports functions that are defined as polynomials within an arbitrary fixed-point structure. We demonstrate the verification process in the case study on an example with different scenarios of fixed-point type implementations.
GraphCode2Vec: Generic Code Embedding via Lexical and Program Dependence Analyses

Code embedding is a keystone in the application of machine learning on several Software Engineering (SE) tasks. To effectively support a plethora of SE tasks, the embedding needs to capture program syntax and semantics in a way that is generic. To this end, we propose the first self-supervised pre-training approach (called Graphcode2vec) which produces task-agnostic embedding of lexical and program dependence features. Graphcode2vec achieves this via a synergistic combination of code analysis and Graph Neural Networks. Graphcode2vec is generic, it allows pre-training, and it is applicable to several SE downstream tasks. We evaluate the effectiveness of Graphcode2vec on four (4) tasks (method name prediction, solution classification, mutation testing and overfitted patch classification), and compare it with four (4) similarly generic code embedding baselines (Code2Seq, Code2Vec, CodeBERT, Graph-CodeBERT) and seven (7) task-specific, learning-based methods. In particular, Graphcode2vec is more effective than both generic and task-specific learning-based baselines. It is also complementary and comparable to GraphCodeBERT (a larger and more complex model). We also demonstrate through a probing and ablation study that Graphcode2vec learns lexical and program dependence features and that self-supervised pre-training improves effectiveness.