Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Candidate Generation for Low-resource Cross-lingual Entity Linking

Cross-lingual entity linking (XEL) is the task of finding referents in a target-language knowledge base (KB) for mentions extracted from source-language texts. The first step of (X)EL is candidate generation, which retrieves a list of plausible candidate entities from the target-language KB for each mention. Approaches based on resources from Wikipedia have proven successful in the realm of relatively high-resource languages, but these do not extend well to low-resource languages with few, if any, Wikipedia pages. Recently, transfer learning methods have been shown to reduce the demand for resources in the low-resource languages by utilizing resources in closely related languages, but the performance still lags far behind their high-resource counterparts. In this paper, we first assess the problems faced by current entity candidate generation methods for low-resource XEL, then propose three improvements that (1) reduce the disconnect between entity mentions and KB entries, and (2) improve the robustness of the model to low-resource scenarios. The methods are simple, but effective: We experiment with our approach on seven XEL datasets and find that they yield an average gain of 16.9% in Top-30 gold candidate recall, compared with state-of-the-art baselines. Our improved model also yields an average gain of 7.9% in in-KB accuracy of end-to-end XEL.1
Analysis and Synthesis of Low-Gain Integral Controllers for Nonlinear Systems with Application to Feedback-Based Optimization

Relaxed conditions are given for stability of a feedback system consisting of an exponentially stable multi-input multi-output nonlinear plant and an integral controller. Roughly speaking, it is shown that if the composition of the plant equilibrium input-output map and the integral feedback gain is infinitesimally contracting, then the closed-loop system is exponentially stable if the integral gain is sufficiently low. The main result is applied to analyze stability of an optimal frequency regulation scheme for AC power systems. We demonstrate how the key condition can be checked computationally via semidefinite programming, and how gain matrices can be synthesized for nonlinear systems to achieve guaranteed performance bounds. Finally, we apply the results to show closed-loop stability of recently developed feedback-based schemes which optimize the steady-state behaviour of a dynamic system.
Efficient variational contraction of two-dimensional tensor networks with a non-trivial unit cell

Tensor network states provide an efficient class of states that faithfully capture strongly correlated quantum models and systems in classical statistical mechanics. While tensor networks can now be seen as becoming standard tools in the description of such complex many-body systems, close to optimal variational principles based on such states are less obvious to come by. In this work, we generalize a recently proposed variational uniform matrix product state algorithm for capturing one-dimensional quantum lattices in the thermodynamic limit, to the study of regular two-dimensional tensor networks with a non-trivial unit cell. A key property of the algorithm is a computational effort that scales linearly rather than exponentially in the size of the unit cell. We demonstrate the performance of our approach on the computation of the classical partition functions of the antiferromagnetic Ising model and interacting dimers on the square lattice, as well as of a quantum doped resonating valence bond state.
Stochastic Analysis on Downlink Performance of Coexistence between WiGig and NR-U in 60 GHz Band

The 60 GHz band (57-71 GHz) has been attracting considerable research interest thanks to the bandwidth abundance and ruling as an unlicensed band. The 60 GHz Wi-Fi (also known as WiGig) and 5G New Radio Unlicensed (NR-U) are among the key access technologies that will operate in the band. This paper inspects the downlink performance of the two technologies under inter-technology interference from each other in 60 GHz band. Based on a dense small-cell setting, this paper finds stochastic models for signal-to-interference-plus-noise ratio (SINR) and data rate. Via simulations, this paper discovers that the downlink SINR and data rate follow Gaussian mixture distributions with three modes representing (i) severely interfered, (ii) mildly interfered, and (iii) not interfered UEs.
Natural Language Processing Advancements By Deep Learning: A Survey

Natural Language Processing (NLP) helps empower intelligent machines by enhancing a better understanding of the human language for linguistic-based human-computer communication. Recent developments in computational power and the advent of large amounts of linguistic data have heightened the need and demand for automating semantic analysis using data-driven approaches. The utilization of data-driven strategies is pervasive now due to the significant improvements demonstrated through the usage of deep learning methods in areas such as Computer Vision, Automatic Speech Recognition, and in particular, NLP. This survey categorizes and addresses the different aspects and applications of NLP that have benefited from deep learning. It covers core NLP tasks and applications and describes how deep learning methods and models advance these areas. We further analyze and compare different approaches and state-of-the-art models.
Optical transport of sub-micron lipid vesicles along an optical nanofibre

Enhanced manipulation and analysis of bio-particles using light confined in nano-scale dielectric structures has proceeded apace in the last several years. Small mode volumes, along with the lack of a need for bulky optical elements give advantages in sensitivity and scalability relative to conventional optical manipulation. However, manipulation of lipid vesicles (liposomes) remains difficult, particularly in the sub-micron diameter regime. Here we demonstrate the optical trapping and transport of sub-micron diameter liposomes along an optical nanofiber using the nanofiber mode's evanescent field. We find that nanofiber diameters below a nominal diffraction limit give optimal results. Our results pave the way for integrated optical transport and analysis of liposome-like bio-particles, as well as their coupling to nano-optical resonators.
Coverage Analysis of UAVs in Millimeter Wave Networks: A Stochastic Geometry Approach

Recent developments in robotics and communication technologies are paving the way towards the use of Unmanned Aerial Vehicles (UAVs) to provide ubiquitous connectivity in public safety scenarios or in remote areas. The millimeter wave (mmWave) spectrum, in particular, has gained momentum since the huge amount of free spectrum available at such frequencies can yield very high data rates. In the UAV context, however, mmWave operations may incur severe signal attenuation and sensitivity to blockage, especially considering the very long transmission distances involved. In this paper, we present a tractable stochastic analysis to characterize the coverage probability of UAV stations operating at mmWaves. We exemplify some of the trade-offs to be considered when designing solutions for mmWave scenarios, such as the beamforming configuration, and the UAV altitude and deployment.
MRI Super-Resolution with GAN and 3D Multi-Level DenseNet: Smaller, Faster, and Better

High-resolution (HR) magnetic resonance imaging (MRI) provides detailed anatomical information that is critical for diagnosis in the clinical application. However, HR MRI typically comes at the cost of long scan time, small spatial coverage, and low signal-to-noise ratio (SNR). Recent studies showed that with a deep convolutional neural network (CNN), HR generic images could be recovered from low-resolution (LR) inputs via single image super-resolution (SISR) approaches. Additionally, previous works have shown that a deep 3D CNN can generate high-quality SR MRIs by using learned image priors. However, 3D CNN with deep structures, have a large number of parameters and are computationally expensive. In this paper, we propose a novel 3D CNN architecture, namely a multi-level densely connected super-resolution network (mDCSRN), which is light-weight, fast and accurate. We also show that with the generative adversarial network (GAN)-guided training, the mDCSRN-GAN provides appealing sharp SR images with rich texture details that are highly comparable with the referenced HR images. Our results from experiments on a large public dataset with 1,113 subjects showed that this new architecture outperformed other popular deep learning methods in recovering 4x resolution-downgraded images in both quality and speed.
Entropy Dynamics of Phonon Quantum States Generated by Optical Excitation of a Two-Level System

In quantum physics, two prototypical model systems stand out due to their wide range of applications. These are the two-level system (TLS) and the harmonic oscillator. The former is often an ideal model for confined charge or spin systems and the latter for lattice vibrations, i.e., phonons. Here, we couple these two systems, which leads to numerous fascinating physical phenomena. Practically, we consider different optical excitations and decay scenarios of a TLS, focusing on the generated dynamics of a single phonon mode that couples to the TLS. Special emphasis is placed on the entropy of the different parts of the system, predominantly the phonons. While, without any decay, the entire system is always in a pure state, resulting in a vanishing entropy, the complex interplay between the single parts results in non-vanishing respective entanglement entropies and non-trivial dynamics of them. Taking a decay of the TLS into account leads to a non-vanishing entropy of the full system and additional aspects in its dynamics. We demonstrate that all aspects of the entropy’s behavior can be traced back to the purity of the states and are illustrated by phonon Wigner functions in phase space.
Curriculum By Texture

Convolutional Neural Networks (CNNs) have shown impressive performance in computer vision tasks such as image classification and segmentation. One factor for the success of CNNs is that they have an inductive bias that assumes a certain type of spatial structure is present in the data. Recent work by Geirhos et al. (2018) shows how learning in CNNs causes the learned CNN models to be biased towards high-frequency textural information, compared to low-frequency shape information in images. Many tasks generally requires both shape and textural information. Hence, we propose a simple curriculum based scheme which improves the ability of CNNs to be less biased towards textural information, and at the same time, being able to represent both the shape and textural information. We propose to augment the training of CNNs by controlling the amount of textural information that is available to the CNNs during the training process, by convolving the output of a CNN layer with a low-pass filter, or simply a Gaussian kernel. By reducing the standard deviation of the Gaussian kernel, we are able to gradually increase the amount of textural information available as training progresses, and hence reduce the texture bias. Such an augmented training scheme significantly improves the performance of CNNs on various image classification tasks, while adding no additional trainable parameters or auxiliary regularization objectives. We also observe significant improvements when using the trained CNNs to perform transfer learning on a different dataset, and transferring to a different task which shows how the learned CNNs using the proposed method act as better feature extractors.
Selectivity considered harmful: evaluating the causal impact of class selectivity in DNNs

The properties of individual neurons are often analyzed in order to understand the biological and artificial neural networks in which they're embedded. Class selectivity-typically defined as how different a neuron's responses are across different classes of stimuli or data samples-is commonly used for this purpose. However, it remains an open question whether it is necessary and/or sufficient for deep neural networks (DNNs) to learn class selectivity in individual units. We investigated the causal impact of class selectivity on network function by directly regularizing for or against class selectivity. Using this regularizer to reduce class selectivity across units in convolutional neural networks increased test accuracy by over 2% in ResNet18 trained on Tiny ImageNet. In ResNet20 trained on CIFAR10 we could reduce class selectivity by a factor of 2.5 with no impact on test accuracy, and reduce it nearly to zero with only a small ($\sim$2%) drop in test accuracy. In contrast, regularizing to increase class selectivity had rapid and disastrous effects on test accuracy across all models and datasets. These results indicate that class selectivity in individual units is neither sufficient nor strictly necessary, and can even impair DNN performance. They also encourage caution when focusing on the properties of single units as representative of the mechanisms by which DNNs function.
Player Chemistry: Striving for a Perfectly Balanced Soccer Team

Soccer scouts typically ignore the team balance and team chemistry when evaluating potential signings for their teams. Instead, they focus on the individual qualities of the players in isolation. To overcome this limitation of their recruitment process, this paper takes a first step towards objectively providing insight into the question: How well does a team of soccer players gel? We address that question in both an observational and a predictive setting. In the former setting, we observe the chemistry between players who have actually played together, which is relevant when selecting the best possible line-up for a match. In the latter setting, we predict the chemistry between players who have never played together before, which is particularly relevant to assess the fit of a potential signing with the players who are already on the team. We introduce two chemistry metrics that measure the offensive and defensive chemistry for a pair of players, respectively. The offensive chemistry metric measures the pair's joint performance in terms of scoring goals, whereas the defensive chemistry metric measures their joint performance in preventing their opponents from scoring goals. We compute our metrics for 361 seasons in 106 different competitions and present a number of concrete use cases. For instance, we show that the partnership between Mohamed Salah and Roberto Firmino in Liverpool's 2017/2018 Champions League campaign exhibited the highest mutual chemistry between two players. Furthermore, we show that Mesut Ozil's chemistry has rapidly started declining following Alexis Sanchez' departure to Manchester United in 2018.
Effect of Probiotic Consortium on Level and Mechanism of Intestine Function.

CHICHLOWSKI, MACIEJ. Effect of probiotic consortium on level and mechanism of intestine function. (Under the direction of Warren James Croom, Jr.). A probiotic consortium or direct-fed microbial (DFM) is a live-microbial food supplement that improves health and performance, in broilers and other animals. They may have potential as an alternative to supplemental feed antibiotics use within the poultry industry to prevent enteric disease. Mechanisms by which DFM operate, collectively called “competitive exclusion”, includes spatial exclusion, microenvironmental alterations, production of antimicrobial substances and epithelial barrier integrity. The majority of recent research suggests the likelihood of a small but additive series of beneficial changes from the animal’s exposure to DFM. The interactions between intestinal microbiota, the gut epithelium, and the immune system are important in the competitive exclusion process. Data presented in this study suggest that DFM increase metabolic efficiency via changes in intestinal physiology and metabolism, especially when compared to the traditional feed supplement prophylactic, salinomycin. Also, the effects of DFM on ileal glucose and proline absorption and their relationship to gastrointestinal energy expenditures were examined. Increases in the efficiency of nutrient absorption and decreases in intestinal fermentation with DFM may contribute to previously observed decreases in energy expenditures; however, these contributions are relatively minor, indicating that other physiological mechanisms are involved. The effects of DFM on intestinal histomorphometrics and micro-architecture were examined. Since DFM colonization can prevent attachment of the pathogens to the gastrointestinal epithelium, spatial relationships between the gastrointestinal bacteria and gastrointestinal epithelium, described in this study, confirm previous assumptions about the ability of DFM to physically exclude the colonization of bacterial pathogens, preventing enteric disease. In summary, it is likely that the beneficial effects of DFM are the result of the summation of a complex, multi-variate series of alterations in gut microbial and whole body metabolism. EFFECT OF PROBIOTIC CONSORTIUM ON LEVEL AND MECHANISM OF INTESTINE FUNCTION by MACIEJ CHICHLOWSKI A dissertation submitted to the Graduate Faculty of North Carolina State University In partial fulfillment of the Requirements for the Degree of Doctor of Philosophy
The superior hydrothermal stability of Pd/SSZ-39 in low temperature passive NOx adsorption (PNA) and methane combustion

We successfully synthesized uniform SSZ-39 with an average crystal size of about a micron. Pd (0.7 - 3 wt%) was supported on SSZ-39 with Si/Al ratio ~12. The as-synthesized materials were characterized by FTIR, XRD, Helium Ion Microscopy, HAADF-STEM imaging, 27Al, 29Si and H solid state NMR spectroscopic techniques. FTIR studies with CO and NO probe molecules reveal that the 0.7 wt% Pd/SSZ-39 material with Si/Al ~12 has the majority of Pd dispersed atomically as isolated Pd(II) and Pd(II)-OH centers, and thus can be used as a low-temperature passive NOx adsorber. Pd(II)-NO, Pd(II)(OH)(NO) and Pd(II)(CO)(NO) complexes form during PNA in this material. We compare this PNA material directly with the Pd/SSZ-13 system (with Si/Al ratio ~12) and show its superior hydrothermal stability. Remarkably, Pd/SSZ-39 with Si/Al ratio ~12 survives hydrothermal aging up to 815 ºC in 10% H2O/Air vapor for 16 hours without significant loss in activity. The SSZ-39 crystal structure remains intact during hydrothermal aging up to 1,000 ºC as we elucidate it with XRD and HAADF-STEM imaging/EDS mapping. However, changes to the framework during such harsh hydrothermal treatment significantly change the NOx release profiles during PNA as evidenced by high-field 27Al NMR on fresh and aged Pd/SSZ-39 samples as well as PNA performance measurements. Besides PNA application, these hydrothermally very stable materials (3 wt% Pd on SSZ-39 with Si/Al ratio ~12) can be used as a robust methane combustion catalyst under industrially relevant conditions (GHSV~600,000hr-1). This catalyst shows minimal deactivation after both harsh hydrothermal aging at 750 and 800 ºC, and prolonged time on stream (105 hrs) at 425 ⁰C. In contrast, both 3wt% Pd/alumina and 3wt% SSZ-13 supported samples lose a significant portion of their activity.
Compliance to dietary counseling provided to patients with type 2 diabetes at a tertiary care hospital

Objectives: To observe the compliance to dietary counseling b y trained dietitians provided to subjects with type 2 diabetes at a tertiary care hospital. Method: Compliance to dietary counseling of 72 type 2 diabetics attending BIDE was assessed through comparison of food habits at first and second visit (made after three months). Compliance was indicated by decrease in number of dietary modifications needed in diet at second visit as compared to that at first visit. Results: Fifty percent of the subjects were males. Mean age of the subjects was 52.3 ±10.7 years. On the first visit 66.7% had very inadequate and 29.2 % had poor diets while at 2nd visit only 19.4% had very inadequate and 1.4% had poor diet. Overall compliance to dietary counseling was very good by 19.4% good by 37.5%, fair by 33.3% and poor by 9.7 % of the subjects. Diet quality improved for 94.5% of subjects. Conclusion: Compliance rates were sufficiently high to bring positive changes in diets of people with diabetes. Universal availability of dietary counseling to diabetics has potential to improve patients' diet and thus can contribute to controlling diabetes complications rate
Optimization of double layer capacitor arrays

The double layer capacitor was developed in the late 1960s by the SOHIO Corporation, with further development continued by NEC and Matsushita in Japan. In 1978, a commercial product was introduced by Matsushita, the Panasonic Gold capacitor. This recent technological advancement has mainly been applied in computer memory back-up systems, but with recent increases in double layer capacitor energy storage levels higher power applications have become a real possibility. This paper examines the design of a capacitor bank interfaced to a constant power load though a DC-DC power converter. A numerical procedure has been implemented in a program which determines the dependence of run time, efficiency and energy utilization on the capacitor array dimensions, given the empirically determined capacitor parameters as well as a specified converter output power. Capacitor bank design calculations made using this numerical procedure have been verified using a commercial DC-DC power converter.
Epidemiology of Spontaneous Pneumothoraces in Jamaica

There has been no report on the prevalence of spontaneous pneumothoraces in developing Caribbean nations. We performed a retrospective survey of all tertiary level hospitals serving Kingston, the capital of Jamaica over five years from January 1997 to December 2001. All patients treated for spontaneous pneumothoraces were identified and their records were retrospectively examined. There were 81 patients treated for spontaneous pneumothoraces over the study period. Spontaneous pneumothorax affects 1.96/100,000 persons yearly in Jamaica, with a 3:1 male preponderance. Secondary spontaneous pneumothorax (SSP) occurs in 0.56/100,000 persons yearly in this setting (0.36 in males and 0.19 in females). The commoner underlying disorders causing SSP were COPD (47.8%), Tuberculosis (26.1%), Asthma (13%), Pneumocysitis Carinii Pneumonia (4.4%), granulomatous lung disease (4.4%) and endometriosis (4.4%). The incidence of primary spontaneous pneumothorax (PSP) in Jamaica was 1.40/100,000 persons yearly (1.16 in males and 0.24 in females). The majority of patients (58/81, 72%) were current smokers of cigarettes and/or cannabis, and there was a significantly higher incidence of current smoking among males with PSP (83% vs 33%; X2 = 16.67; p <0.001). There was a significant predilection for the right hemi-thorax to be affected (52/81, 64%) compared to the left hemithorax (28/81, 34%) and bilateral disease (1/81, 4.4%). There were 13 patients (16%) with recurrent pneumothoraces. Nine were ipsilateral, all on the right and 4 were contra lateral recurrences. Spontaneous pneumothoraces are potentially dangerous conditions. The incidence in Jamaica is relatively low. Emergency room physicians must remain aware of this problem and maintain a high level of suspicion in order to optimize diagnosis and therapy.