Miracle-dz/UniFAR-FTUs huggingface dataset:
Analytical modeling and Dynamics of Multi-Domains in Negative-Capacitance MFIS-FETs.

Analytical modeling and dynamics of multidomain in metal-ferroelectric-insulator-semiconductor (MFIS)-FETs are presented in this paper. The formation of multi-domain (MD) leads to oscillations in the conduction band in the channel and periodicity in the local electric field in the ferroelectric region. The impact of 2-D local electric field on the MD switching is captured in the model using the domain wall velocity concept. The optimum values of oxide thickness, ferroelectric thickness and channel length are calculated which corresponds to mono-domain device operation. Deviation from the optimum device parameters causes the transition of mono-domain state to multi-domain state in the ferroelectric. This work can be used as a guideline for designing MFIS-NCFETs, which provides the device parameters that leads to monodomain state in the MFIS-NCFET.
AlphaMWE: Construction of Multilingual Parallel Corpora with MWE Annotations

In this work, we present the construction of multilingual parallel corpora with annotation of multiword expressions (MWEs). MWEs include verbal MWEs (vMWEs) defined in the PARSEME shared task that have a verb as the head of the studied terms. The annotated vMWEs are also bilingually and multilingually aligned manually. The languages covered include English, Chinese, Polish, and German. Our original English corpus is taken from the PARSEME shared task in 2018. We performed machine translation of this source corpus followed by human post editing and annotation of target MWEs. Strict quality control was applied for error limitation, i.e., each MT output sentence received first manual post editing and annotation plus second manual quality rechecking. One of our findings during corpora preparation is that accurate translation of MWEs presents challenges to MT systems. To facilitate further MT research, we present a categorisation of the error types encountered by MT systems in performing MWE related translation. To acquire a broader view of MT issues, we selected four popular state-of-the-art MT models for comparisons namely: Microsoft Bing Translator, GoogleMT, Baidu Fanyi and DeepL MT. Because of the noise removal, translation post editing and MWE annotation by human professionals, we believe our AlphaMWE dataset will be an asset for cross-lingual and multilingual research, such as MT and information extraction. Our multilingual corpora are available as open access at github.com/poethan/AlphaMWE.
The effect of systematic redshift biases in BAO cosmology

With the remarkable increase in scale and precision provided by upcoming galaxy redshift surveys, systematic errors that were previously negligible may become significant. In this paper, we explore the potential impact of low-magnitude systematic redshift offsets on measurements of the Baryon Acoustic Oscillation (BAO) feature, and the cosmological constraints recovered from such measurements. Using 500 mock galaxy redshift surveys as our baseline sample, we inject a series of systematic redshift biases (ranging from +/-0.2% to +/-2%), and measure the resulting shift in the recovered isotropic BAO scale. When BAO measurements are combined with CMB constraints (in both {\Lambda}CDM and wCDM cosmologies), plausible systematics introduce a negligible offset on combined fits of H0 and {\Omega}m, and systematics must be an order of magnitude greater than this plausible baseline to introduce a 1-{\sigma} shift on such combined fits. We conclude that systematic redshift biases are very unlikely to bias constraints on parameters such as H0 provided by BAO cosmology, either now or in the near future. We also detail a theoretical model that predicts the impact of uniform redshift systematics on {\alpha}, and show this model is in close alignment with the results of our mock survey analysis.
Metrics also Disagree in the Low Scoring Range: Revisiting Summarization Evaluation Metrics

In text summarization, evaluating the efficacy of automatic metrics without human judgments has become recently popular. One exemplar work (Peyrard, 2019) concludes that automatic metrics strongly disagree when ranking high-scoring summaries. In this paper, we revisit their experiments and find that their observations stem from the fact that metrics disagree in ranking summaries from any narrow scoring range. We hypothesize that this may be because summaries are similar to each other in a narrow scoring range and are thus, difficult to rank. Apart from the width of the scoring range of summaries, we analyze three other properties that impact inter-metric agreement - Ease of Summarization, Abstractiveness, and Coverage.
Towards a quantitative assessment of neurodegeneration in Alzheimer's disease

Alzheimer's disease (AD) is an irreversible neurodegenerative disorder that progressively destroys memory and other cognitive domains of the brain. While effective therapeutic management of AD is still in development, it seems reasonable to expect their prospective outcomes to depend on the severity of baseline pathology. For this reason, substantial research efforts have been invested in the development of effective means of non-invasive diagnosis of AD at its earliest possible stages. In pursuit of the same objective, the present paper addresses the problem of the quantitative diagnosis of AD by means of Diffusion Magnetic Resonance Imaging (dMRI). In particular, the paper introduces the notion of a pathology specific imaging contrast (PSIC), which, in addition to supplying a valuable diagnostic score, can serve as a means of visual representation of the spatial extent of neurodegeneration. The values of PSIC are computed by a dedicated deep neural network (DNN), which has been specially adapted to the processing of dMRI signals. Once available, such values can be used for several important purposes, including stratification of study subjects. In particular, experiments confirm the DNN-based classification can outperform a wide range of alternative approaches in application to the basic problem of stratification of cognitively normal (CN) and AD subjects. Notwithstanding its preliminary nature, this result suggests a strong rationale for further extension and improvement of the explorative methodology described in this paper.
Efficient Permutation Discovery in Causal DAGs

The problem of learning a directed acyclic graph (DAG) up to Markov equivalence is equivalent to the problem of finding a permutation of the variables that induces the sparsest graph. Without additional assumptions, this task is known to be NP-hard. Building on the minimum degree algorithm for sparse Cholesky decomposition, but utilizing DAG-specific problem structure, we introduce an efficient algorithm for finding such sparse permutations. We show that on jointly Gaussian distributions, our method with depth $w$ runs in $O(p^{w+3})$ time. We compare our method with $w = 1$ to algorithms for finding sparse elimination orderings of undirected graphs, and show that taking advantage of DAG-specific problem structure leads to a significant improvement in the discovered permutation. We also compare our algorithm to provably consistent causal structure learning algorithms, such as the PC algorithm, GES, and GSP, and show that our method achieves comparable performance with a shorter runtime. Thus, our method can be used on its own for causal structure discovery. Finally, we show that there exist dense graphs on which our method achieves almost perfect performance, so that unlike most existing causal structure learning algorithms, the situations in which our algorithm achieves both good performance and good runtime are not limited to sparse graphs.
Comparing the market microstructure between two South African exchanges

We consider shared listings on two South African equity exchanges: the Johannesburg Stock Exchange (JSE) and the A2X Exchange. A2X is an alternative exchange that provides for both shared listings and new listings within the financial market ecosystem of South Africa. From a science perspective it provides the opportunity to compare markets trading similar shares, in a similar regulatory and economic environment, but with vastly different liquidity, costs and business models. A2X currently has competitive settlement and transaction pricing when compared to the JSE, but the JSE has deeper liquidity. In pursuit of an empirical understanding of how these differences relate to their respective price response dynamics, we compare the distributions and auto-correlations of returns on different time scales; we compare price impact and master curves; and we compare the cost of trading on each exchange. This allows us to empirically compare the two markets. We find that various stylised facts become similar as the measurement or sampling time scale increase. However, the same securities can have vastly different price responses irrespective of time scales. This is not surprising given the different liquidity and order-book resilience. Here we demonstrate that direct costs dominate the cost of trading, and the importance of competitively positioning cost ceilings. Universality is crucial for being able to meaningfully compare cross-exchange price responses, but in the case of A2X, it has yet to emerge in a meaningful way due to the infancy of the exchange -- making meaningful comparisons difficult.
Strongly correlated system-reservoir dynamics with quantized pulses: Controlling the atom-photon bound state

Within the matrix product state framework, we study the non-Markovian feedback dynamics of a two-level system interacting with the electromagnetic field inside a semi-infinite waveguide where the formation of an atom-photon bound state is possible. Taking the steady-state excitation of the emitter as a figure of merit, we compare the trapped excitation for an initially excited quantum emitter and an emitter prepared via quantized pulses. In the latter case, we find that for certain feedback delay times, multi-photon pulses yield a significantly higher steady-state excitation than possible with an initially excited emitter. In the system we consider, we find an increase of around 90 percent. Thus, the quantum optical initialization is found to be more efficient due to the self-consistent inclusion of emerging quantum noise. Furthermore, we propose a recursive Heisenberg ansatz which shows good qualitative agreement with the numerically expensive matrix product state method and requires only numerical resources growing quadratically with the number of excitations.
Intelligent Reflecting Surface Aided Secure UAV Communications

In this letter, we study the secure communication problem in the unmanned aerial vehicle (UAV) enabled networks aided by an intelligent reflecting surface (IRS) from the physical-layer security perspective. Specifically, the IRS is deployed to assist the wireless transmission from the UAV to the ground user in the presence of an eavesdropper. The objective of this work is to maximize the secrecy rate by jointly optimizing the phase shifts at the IRS as well as the transmit power and location of the UAV. However, the formulated problem is difficult to solve directly due to the non-linear and non-convex objective function and constraints. By invoking fractional programming and successive convex approximation techniques, the original problem is decomposed into three subproblems, which are then transformed into convex ones. Next, a low-complexity alternating algorithm is proposed to solve the challenging non-convex problem effectively, where the closed-form expressions for transmit power and phase shifts are obtained at each iteration. Simulations results demonstrate that the designed algorithm for IRS-aided UAV communications can achieve higher secrecy rate than benchmarks.
In situ Denitrification of Drinking Water

Pilot-plant and full scale studies of the in-situ biological denitrification of drinking water form the subject of this paper. Ethyl alcohol as a source of organic carbon was used in both cases. Good results were obtained during the pilot-plant study. The full-scale experiment was carried out as a system of four injection wells and one central collection well. Promising results were obtained when diluting water was recirculated into the injection wells to improve mixing of substrate in ground water. The efficiency of nitrate removal was about 50%.
Human-Like Active Learning: Machines Simulating the Human Learning Process

Although the use of active learning to increase learners' engagement has recently been introduced in a variety of methods, empirical experiments are lacking. In this study, we attempted to align two experiments in order to (1) make a hypothesis for machine and (2) empirically confirm the effect of active learning on learning. In Experiment 1, we compared the effect of a passive form of learning to active form of learning. The results showed that active learning had a greater learning outcomes than passive learning. In the machine experiment based on the human result, we imitated the human active learning as a form of knowledge distillation. The active learning framework performed better than the passive learning framework. In the end, we showed not only that we can make build better machine training framework through the human experiment result, but also empirically confirm the result of human experiment through imitated machine experiments; human-like active learning have crucial effect on learning performance.
Toward Unique and Unbiased Causal Effect Estimation From Data With Hidden Variables

Causal effect estimation from observational data is a crucial but challenging task. Currently, only a limited number of data-driven causal effect estimation methods are available. These methods either provide only a bound estimation of causal effects of treatment on the outcome or generate a unique estimation of the causal effect but making strong assumptions on data and having low efficiency. In this article, we identify a problem setting with the Cause Or Spouse of the treatment Only (COSO) variable assumption and propose an approach to achieving a unique and unbiased estimation of causal effects from data with hidden variables. For the approach, we have developed the theorems to support the discovery of the proper covariate sets for confounding adjustment (adjustment sets). Based on the theorems, two algorithms are proposed for finding the proper adjustment sets from data with hidden variables to obtain unbiased and unique causal effect estimation. Experiments with synthetic datasets generated using five benchmark Bayesian networks and four real-world datasets have demonstrated the efficiency and effectiveness of the proposed algorithms, indicating the practicability of the identified problem setting and the potential of the proposed approach in real-world applications.
Sampling connected subgraphs: nearly-optimal mixing time bounds, nearly-optimal $\epsilon$-uniform sampling, and perfect uniform sampling

We study the connected subgraph sampling problem: given an integer $k \ge 3$ and a simple graph $G=(V,E)$, sample a connected induced $k$-node subgraph of $G$ (also called $k$-graphlet) uniformly at random. This is a fundamental graph mining primitive, with applications in social network analysis and bioinformatics. In this work we give several results that improve substantially upon the current state of the art: 1) We settle the mixing time of the graphlet random walk. We show it is $\tilde{\Theta}(t(G) \cdot \rho(G)^{k-1})$, where $t(G)$ is the mixing time of $G$ and $\rho(G)$ is the ratio between its maximum and minimum degree. As a consequence, we obtain state-of-the-art running time bounds for random-walk graphlet sampling. 2) We give the first efficient algorithm for perfectly uniform graphlet sampling. The algorithm has a preprocessing phase that uses time ${O}(n + m \log \Delta)$ and space ${O}(n)$, and a sampling phase that uses $k^{{O}(k)} {O}(\log \Delta)$ time per sample. 3) We give a nearly-optimal algorithm for $\epsilon$-uniform graphlet sampling. The preprocessing runs in time $k^{{O}(k)}{O}\big(\frac{1}{\epsilon}\, n \log n \big)$, which is sublinear when $G$ is not too sparse, and space ${O}(n)$. The sampling takes $k^{{O}(k)}{O}\big((\frac{1}{\epsilon})^{10}\log \frac{1}{\epsilon} \big)$ expected time per sample. This is nearly optimal, as any $\epsilon$-uniform algorithm has worst-case expected running time $\Omega(n)$.
Learning Asymmetric and Local Features in Multi-Dimensional Data Through Wavelets With Recursive Partitioning

Effective learning of asymmetric and local features in images and other data observed on multi-dimensional grids is a challenging objective critical for a wide range of image processing applications involving biomedical and natural images. It requires methods that are sensitive to local details while fast enough to handle massive numbers of images of ever increasing sizes. We introduce a probabilistic model-based framework that achieves these objectives by incorporating adaptivity into discrete wavelet transforms (DWT) through Bayesian hierarchical modeling, thereby allowing wavelet bases to adapt to the geometric structure of the data while maintaining the high computational scalability of wavelet methods—linear in the sample size (e.g., the resolution of an image). We derive a recursive representation of the Bayesian posterior model which leads to an exact message passing algorithm to complete learning and inference. While our framework is applicable to a range of problems including multi-dimensional signal processing, compression, and structural learning, we illustrate its work and evaluate its performance in the context of image reconstruction using real images from the ImageNet database, two widely used benchmark datasets, and a dataset from retinal optical coherence tomography and compare its performance to state-of-the-art methods based on basis transforms and deep learning.
A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game Encoding

We introduce a general framework for designing and training neural network layers whose forward passes can be interpreted as solving non-smooth convex optimization problems, and whose architectures are derived from an optimization algorithm. We focus on convex games, solved by local agents represented by the nodes of a graph and interacting through regularization functions. This approach is appealing for solving imaging problems, as it allows the use of classical image priors within deep models that are trainable end to end. The priors used in this presentation include variants of total variation, Laplacian regularization, bilateral filtering, sparse coding on learned dictionaries, and non-local self similarities. Our models are fully interpretable as well as parameter and data efficient. Our experiments demonstrate their effectiveness on a large diversity of tasks ranging from image denoising and compressed sensing for fMRI to dense stereo matching.
Towards a unified generic framework to define and observe contacts between livestock and wildlife: a systematic review

Wild animals are the source of many pathogens of livestock and humans. Concerns about the potential transmission of economically important and zoonotic diseases from wildlife have led to increased surveillance at the livestock-wildlife interface. Knowledge of the types, frequency and duration of contacts between livestock and wildlife is necessary to identify risk factors for disease transmission and to design possible mitigation strategies. Observing the behaviour of many wildlife species is challenging due to their cryptic nature and avoidance of humans, meaning there are relatively few studies in this area. Further, a consensus on the definition of what constitutes a ‘contact’ between wildlife and livestock is lacking. A systematic review was conducted to investigate which livestock-wildlife contacts have been studied and why, as well as the methods used to observe each species. Over 30,000 publications were screened, of which 122 fulfilled specific criteria for inclusion in the analysis. The majority of studies examined cattle contacts with badgers or with deer; studies involving wild pig contacts with cattle or with domestic pigs were the next most frequent. There was a range of observational methods including motion-activated cameras and global positioning system collars. As a result of the wide variation and lack of consensus in the definitions of direct and indirect contacts, we developed a unified framework to define livestock-wildlife contacts that is sufficiently flexible to be applied to most wildlife and livestock species for non-vector-borne diseases. We hope this framework will help standardise the collection and reporting of contact data; a valuable step towards being able to compare the efficacy of wildlife-livestock observation methods. In doing so, it may aid the development of better disease transmission models and improve the design and effectiveness of interventions to reduce or prevent disease transmission.
BDNF Val66Met Polymorphism, the Allele-Specific Analysis by qRT-PCR - a Novel Protocol

Background: Alteration in brain-derived neurotrophic factor (BDNF) production is a marker of neuropathological conditions, which has led to the investigation of Val66Met polymorphism occurring in the human BDNF gene (BDNF). Presently, there are no reported methods available for the analysis of Val66Met impact on human BDNF functioning. Purpose: To develop a qRT-PCR protocol for the allele-specific expression evaluation of the Val66Met polymorphism in BDNF. Methods: Using RNA extracted from muscle samples of 9 healthy volunteers (32.9 ± 10.3 y) at rest and following a maximal effort aerobic capacity exercise test, a protocol was developed for the detection of Val66/Met66 allele-specific BDNF expression in Real-Time Quantitative Reverse Transcription PCR (qRT-PCR) - relative to housekeeping genes - and validated by absolute quantification in Droplet Digital Polymerase Chain Reaction (ddPCR). Results: Differences in the relative values of BDNF mRNA were confirmed by ddPCR analysis. HPRT1 and B2M were the most stable genes expressed in muscle tissue among different metabolic conditions, while GAPDH revealed to be metabolic responsive. Conclusion: Our qRT-PCR protocol successfully determines the allele-specific detection and changes in BDNF expression regarding the Val66Met polymorphism.
A checklist of chromosome numbers and a review of karyotype variation in Odonata of the world

Abstract The ancient insect order Odonata is divided into three suborders: Anisoptera and Zygoptera with approximately 3000 species worldwide each, and Anisozygoptera with only four extant species in the relict family Epiophlebiidae. An updated list of Odonata species studied regarding chromosome number, sex chromosome mechanism and the occurrence of m-chromosomes (= microchromosomes) is given. Karyotypes of 607 species (198 genera, 23 families), covering approximately 10% of described species, are reported: 423 species (125 genera, 8 families) of the Anisoptera, 184 species (72 genera, 14 families) of the Zygoptera, and one species of the Anisozygoptera. Among the Odonata, sex determination mechanisms in males can be of X(0), XY and X1X2Y types, and diploid chromosome numbers can vary from 6 to 41, with a clear mode at 2n = 25(60%) and two more local modes at 2n = 27(21%) and 2n = 23(13%). The karyotype 2n = 25(24A + X) is found in each of the three suborders and is the most typical (modal) in many families, including the best-covered Libellulidae, Corduliidae (Anisoptera), Lestidae, Calopterygidae, and Platycnemididae (Zygoptera). This chromosome set is considered ancestral for the Odonata in general. Chromosome rearrangements, among which fusions and fissions most likely predominated, led to independent origins of similar karyotypes within different phylogenetic lineages of the order. The karyotype 2n = 27(26A + X) prevails in Aeshnidae and Coenagrionidae, whereas the karyotype 2n = 23(22A + X) is modal in Gomphidae and Chlorocyphidae, in both pairs of families one being from the Anisoptera while the other from the Zygoptera.