Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine learning for condensed matter physics

Condensed matter physics (CMP) seeks to understand the microscopic interactions of matter at the quantum and atomistic levels, and describes how these interactions result in both mesoscopic and macroscopic properties. CMP overlaps with many other important branches of science, such as chemistry, materials science, statistical physics, and high-performance computing. With the advancements in modern machine learning (ML) technology, a keen interest in applying these algorithms to further CMP research has created a compelling new area of research at the intersection of both fields. In this review, we aim to explore the main areas within CMP, which have successfully applied ML techniques to further research, such as the description and use of ML schemes for potential energy surfaces, the characterization of topological phases of matter in lattice systems, the prediction of phase transitions in off-lattice and atomistic simulations, the interpretation of ML theories with physics-inspired frameworks and the enhancement of simulation methods with ML algorithms. We also discuss in detail the main challenges and drawbacks of using ML methods on CMP problems, as well as some perspectives for future developments.
Pricing Temperature Derivatives under a Time-Changed Levy Model

The objective of the paper is to price weather contracts using temperature as the underlying process when the later follows a mean-reverting dynamics driven by a time-changed Brownian motion coupled to a Gamma Levy subordinator and time-dependent deterministic volatility. This type of model captures the complexity of the temperature dynamic providing a more accurate valuation of their associate weather contracts. An approximated price is obtained by a Fourier expansion of its characteristic function combined with a selection of the equivalent martingale measure following the Esscher transform proposed in Gerber and Shiu (1994).
Human Recognition Using Face in Computed Tomography

With the mushrooming use of computed tomography (CT) images in clinical decision making, management of CT data becomes increasingly difficult. From the patient identification perspective, using the standard DICOM tag to track patient information is challenged by issues such as misspelling, lost file, site variation, etc. In this paper, we explore the feasibility of leveraging the faces in 3D CT images as biometric features. Specifically, we propose an automatic processing pipeline that first detects facial landmarks in 3D for ROI extraction and then generates aligned 2D depth images, which are used for automatic recognition. To boost the recognition performance, we employ transfer learning to reduce the data sparsity issue and to introduce a group sampling strategy to increase inter-class discrimination when training the recognition network. Our proposed method is capable of capturing underlying identity characteristics in medical images while reducing memory consumption. To test its effectiveness, we curate 600 3D CT images of 280 patients from multiple sources for performance evaluation. Experimental results demonstrate that our method achieves a 1:56 identification accuracy of 92.53% and a 1:1 verification accuracy of 96.12%, outperforming other competing approaches.
Simplifying Deep-Learning-Based Model for Code Search

To accelerate software development, developers frequently search and reuse existing code snippets from a large-scale codebase, e.g., GitHub. Over the years, researchers proposed many information retrieval (IR) based models for code search, which match keywords in query with code text. But they fail to connect the semantic gap between query and code. To conquer this challenge, Gu et al. proposed a deep-learning-based model named DeepCS. It jointly embeds method code and natural language description into a shared vector space, where methods related to a natural language query are retrieved according to their vector similarities. However, DeepCS' working process is complicated and time-consuming. To overcome this issue, we proposed a simplified model CodeMatcher that leverages the IR technique but maintains many features in DeepCS. Generally, CodeMatcher combines query keywords with the original order, performs a fuzzy search on name and body strings of methods, and returned the best-matched methods with the longer sequence of used keywords. We verified its effectiveness on a large-scale codebase with about 41k repositories. Experimental results showed the simplified model CodeMatcher outperforms DeepCS by 97% in terms of MRR (a widely used accuracy measure for code search), and it is over 66 times faster than DeepCS. Besides, comparing with the state-of-the-art IR-based model CodeHow, CodeMatcher also improves the MRR by 73%. We also observed that: fusing the advantages of IR-based and deep-learning-based models is promising because they compensate with each other by nature; improving the quality of method naming helps code search, since method name plays an important role in connecting query and code.
On Incorporating Structural Information to improve Dialogue Response Generation

We consider the task of generating dialogue responses from background knowledge comprising of domain specific resources. Specifically, given a conversation around a movie, the task is to generate the next response based on background knowledge about the movie such as the plot, review, Reddit comments etc. This requires capturing structural, sequential and semantic information from the conversation context and the background resources. We propose a new architecture that uses the ability of BERT to capture deep contextualized representations in conjunction with explicit structure and sequence information. More specifically, we use (i) Graph Convolutional Networks (GCNs) to capture structural information, (ii) LSTMs to capture sequential information and (iii) BERT for the deep contextualized representations that capture semantic information. We analyze the proposed architecture extensively. To this end, we propose a plug-and-play Semantics-Sequences-Structures (SSS) framework which allows us to effectively combine such linguistic information. Through a series of experiments we make some interesting observations. First, we observe that the popular adaptation of the GCN model for NLP tasks where structural information (GCNs) was added on top of sequential information (LSTMs) performs poorly on our task. This leads us to explore interesting ways of combining semantic and structural information to improve the performance. Second, we observe that while BERT already outperforms other deep contextualized representations such as ELMo, it still benefits from the additional structural information explicitly added using GCNs. This is a bit surprising given the recent claims that BERT already captures structural information. Lastly, the proposed SSS framework gives an improvement of 7.95% on BLUE score over the baseline.
Efficient quantum circuits for quantum computational chemistry

The VQE (variational quantum eigensolver) is a hybrid classical-quantum algorithm that can determine the energy eigenvalues of molecules by solving the electronic structure problem. Compared to other purely quantum algorithms, the VQE requires shallower quantum circuits and is more noise tolerant, which makes it a potential application for early NISQ (noisy intermediate-scale quantum) computers. The VQE works by minimizing the expectation value of a molecular Hamiltonian with respect to a parametrized ansatz state. The majority of ansatz states, so far considered by the scientific community, like the UCCSD (unitary coupled-cluster single and double), are derived from classical algorithms, and correspond to a combination of electron (fermionic) excitations applied to an initial guess ansatz state. Therefore, constructing efficient circuits that perform the action of fermionic excitations is critical for the practical realization of the VQE. Here, we introduce the concept of a "qubit excitation", which compared to a fermionic excitation, does not account for fermionic anti-commutation relations. We then construct circuits, optimized in terms of $CNOT$ gates, that perform such single and double qubit excitations. Finally, we modify the functionality of these circuits to account for fermionic anti-commutation relations. In this way we obtain circuits for single and double fermionic excitations that reduce the number of $CNOT$ gates used, by factors of $2$ and $8$, respectively, compared to circuits constructed with the standard stair-case $CNOT$ structures.
A priori and a posteriori error analysis for the Nitsche's method of a reduced Landau-de Gennes problem

The equilibrium configurations of a two dimensional planar bistable nematic liquid crystal device are modelled by a system of second order semi-linear elliptic partial differential equations with non-homogeneous boundary conditions. In this article, Nitsche's method is applied to approximate the solution of this non-linear model. A discrete inf-sup condition sufficient for the stability of a well-posed linear problem is established and this with a fixed point theorem allows the proof of local existence and uniqueness of a discrete solution to the semi-linear problems. {\it A priori} and {\it a posteriori} energy norm analysis is established for a sufficiently large penalization parameter and sufficiently fine triangulation. Optimal order {\it a priori} error estimates in $L^2$ norm is also established. Several numerical examples that confirm the theoretical results are presented.
Agnostic Learning of a Single Neuron with Gradient Descent

We consider the problem of learning the best-fitting single neuron as measured by the expected square loss $\mathbb{E}_{(x,y)\sim \mathcal{D}}[(\sigma(w^\top x)-y)^2]$ over some unknown joint distribution $\mathcal{D}$ by using gradient descent to minimize the empirical risk induced by a set of i.i. d. samples $S\sim \mathcal{D}^n$. The activation function $\sigma$ is an arbitrary Lipschitz and non-decreasing function, making the optimization problem nonconvex and nonsmooth in general, and covers typical neural network activation functions and inverse link functions in the generalized linear model setting. In the agnostic PAC learning setting, where no assumption on the relationship between the labels $y$ and the input $x$ is made, if the optimal population risk is $\mathsf{OPT}$, we show that gradient descent achieves population risk $O(\mathsf{OPT}^{1/2})+\epsilon$ in polynomial time and sample complexity. When labels take the form $y = \sigma(v^\top x) + \xi$ for zero-mean sub-Gaussian noise $\xi$, we show that gradient descent achieves population risk $\mathsf{OPT} + \epsilon$. Our sample complexity and runtime guarantees are (almost) dimension independent, and when $\sigma$ is strictly increasing and Lipschitz, require no distributional assumptions beyond boundedness. For ReLU, we show the same results under a nondegeneracy assumption for the marginal distribution of the input. To the best of our knowledge, this is the first result for agnostic learning of a single neuron using gradient descent.
Attention: to Better Stand on the Shoulders of Giants

Science of science (SciSci) is an emerging discipline wherein science is used to study the structure and evolution of science itself using large data sets. The increasing availability of digital data on scholarly outcomes offers unprecedented opportunities to explore SciSci. In the progress of science, the previously discovered knowledge principally inspires new scientific ideas, and citation is a reasonably good reflection of this cumulative nature of scientific research. The researches that choose potentially influential references will have a lead over the emerging publications. Although the peer review process is the mainly reliable way of predicting a paper's future impact, the ability to foresee the lasting impact based on citation records is increasingly essential in the scientific impact analysis in the era of big data. This paper develops an attention mechanism for the long-term scientific impact prediction and validates the method based on a real large-scale citation data set. The results break conventional thinking. Instead of accurately simulating the original power-law distribution, emphasizing the limited attention can better stand on the shoulders of giants.
Hyperspectral Image Super-resolution via Deep Spatio-spectral Convolutional Neural Networks

Hyperspectral images are of crucial importance in order to better understand features of different materials. To reach this goal, they leverage on a high number of spectral bands. However, this interesting characteristic is often paid by a reduced spatial resolution compared with traditional multispectral image systems. In order to alleviate this issue, in this work, we propose a simple and efficient architecture for deep convolutional neural networks to fuse a low-resolution hyperspectral image (LR-HSI) and a high-resolution multispectral image (HR-MSI), yielding a high-resolution hyperspectral image (HR-HSI). The network is designed to preserve both spatial and spectral information thanks to an architecture from two folds: one is to utilize the HR-HSI at a different scale to get an output with a satisfied spectral preservation; another one is to apply concepts of multi-resolution analysis to extract high-frequency information, aiming to output high quality spatial details. Finally, a plain mean squared error loss function is used to measure the performance during the training. Extensive experiments demonstrate that the proposed network architecture achieves best performance (both qualitatively and quantitatively) compared with recent state-of-the-art hyperspectral image super-resolution approaches. Moreover, other significant advantages can be pointed out by the use of the proposed approach, such as, a better network generalization ability, a limited computational burden, and a robustness with respect to the number of training samples.
Remote capacitive sensing in two-dimensional quantum-dot arrays.

We investigate gate-induced quantum dots in silicon on insulator nanowire field-effect transistors fabricated using a foundry-compatible fully-depleted silicon-on-insulator (FD-SOI) process. A series of split gates wrapped over the silicon nanowire naturally produces a 2 x n bilinear array of quantum dots along a single nanowire. We begin by studying the capacitive coupling of quantum dots within such a 2 x 2 array, and then show how such couplings can be extended across two parallel silicon nanowires coupled together by shared, electrically isolated, 'floating' electrodes. With one quantum dot operating as a single-electron-box sensor, the floating gate serves to enhance the charge sensitivity range, enabling it to detect charge state transitions in a separate silicon nanowire. By comparing measurements from multiple devices we illustrate the impact of the floating gate by quantifying both the charge sensitivity decay as a function of dot-sensor separation and configuration within the dual-nanowire structure.
Quantitation of soya protein by enzyme linked immunosorbent assay of its characteristic peptide

An enzyme linked immunosorbent assay (ELISA) is described for the quantitative measurement of soya proteins in processed food products. The peptide fragment bearing an antigenicity against anti-glycinin (soya bean 11S globulin) antibody was purified from the trypsin digests of autoclaved glycinin and used as an indicator antigen for soya proteins. ELISA samples were prepared also by a combination of autoclaving and tryptic digestion. Quantitation of soya proteins by ELISA was found to resist interference by other food materials, and the ELISA signal was independent of soya bean cultivar. The ELISA method was evaluated on pork sausages which were prepared in the laboratory to contain 0 to 20·8% soya protein isolate. With commercial soya protein isolate as reference standard, the results showed that the detected amounts were in quantitative agreement with the added amounts of soya protein isolate.
Strategy to Maintain the Cinema Industry in the Middle of Development of Internet Technology

Internet technology has been utilized in many sectors of business including in the film industry. People are using internet technology to replace the newspaper and television and also creating a new platform (computer, cellular phone and other types of gadget) to watch a film, it threats the cinema industry. This research aims to formulate the strategy of cinema industry to survive in the middle of internet technology. The motive of cinema-goers to watch the film becomes the main issue in this research. The influence of information, personnel film, word of mouth and cinema-quality to cinema-goers are evaluated. Hundred and twenty-eight cinema-goers involved as respondents. Structure Equation Modelling (SEM) is used to analyze the data. The research revealed that the cinema-goers had a specific expectation of the cinema industry that cannot be replaced by internet technology.
The relationships among lodging, stem anatomy, degree of lignification, and resistance to mycosphaerella blight in field pea (Pisum sativum)

The relationships among lodging, stem anatomy, degree of stem lignification, and resistance to mycosphaerella blight (Mycosphaerella pinodes (Berk. & Blox.) Vestergr. ) in field pea (Pisum sativum L.) were investigated in field experiments in Saskatchewan from 2000 to 2002. Disease severity, lodging resistance, plant height, internode length and diameter, and the cellulose, lignin, and fibre contents of stems were measured from 9 (2000) and 20 (2001, 2002) cultivars of field pea. Significant differences were observed in resistance to mycosphaerella blight and lodging among cultivars. In all years, ‘AC Tamor’ was among those cultivars with the highest disease ratings and lodging scores, whereas ‘CDC Striker’, ‘Carneval’, and ‘Integra’ were among those with the lowest ratings. Disease severity was positively correlated with lodging scores of plants. Lodging was negatively correlated with the proportions of supportive tissue (formerly parenchyma cells in the pith periphery, which have differentiated to sclere.
Government (Big) Data Ecosystem: Definition, Classification of Actors, and Their Roles

 Abstract — Organizations, including governments, generate (big) data that are high in volume, velocity, veracity, and come from a variety of sources. Public Administrations are using (big) data, implementing base registries, and enforcing data sharing within the entire government to deliver (big) data related integrated services, provision of insights to users, and for good governance. Government (Big) data ecosystem actors represent distinct entities that provide data, consume data, manipulate data to offer paid services, and extend data services like data storage, hosting services to other actors. In this research work, we perform a systematic literature review. The key objectives of this paper are to propose a robust definition of government (big) data ecosystem and a classification of government (big) data ecosystem actors and their roles. We showcase a graphical view of actors, roles, and their relationship in the government (big) data ecosystem. We also discuss our research findings. We did not find too much published research articles about the government (big) data ecosystem, including its definition and classification of actors and their roles. Therefore, we lent ideas for the government (big) data ecosystem from numerous areas that include scientific research data, humanitarian data, open government data, industry data, in the literature.
Technological Advancement in Fashion Online Retailing: A Comparative Study of Pakistan and UK Fashion E-Commerce

— The study aims to establish the virtual size and fit technology features to enhance fashion online retailing platforms, utilising digital human measurements to provide customised style and function to consumers. A few firms in the UK have launched advanced interactive fashion shopping domains for personalised shopping globally, aided by the latest internet technology. Virtual size and fit interfaces have a great potential to provide a personalised better-fitted garment to promote mass customisation globally. Made-to-measure clothing, consuming unstitched fabric is a common practice offered by fashion brands in Pakistan. This product is regarded as economical and sustainable to be utilised by consumers in Pakistan. Although the manual sizing system is practiced to sell garments online, virtual size and fit visualisation and recommendation technologies are uncommon in Pakistani fashion interfaces. A comparative assessment of Pakistani fashion brand websites and UK technology-driven fashion interfaces was conducted to highlight the vast potential of the virtual size and fit technology. The results indicated that web 2.0 technology adopted by Pakistani apparel brands has limited features, whereas companies practicing web 3.0 technology provide interactive online real-store shopping experience leading to enhanced customer satisfaction and globalisation of brands.
Development of patient-specific 3D-printed breast phantom using silicone and peanut oils for magnetic resonance imaging.

Background Despite increasing reports of 3D printing in medical applications, the use of 3D printing in breast imaging is limited, thus, personalized 3D-printed breast model could be a novel approach to overcome current limitations in utilizing breast magnetic resonance imaging (MRI) for quantitative assessment of breast density. The aim of this study is to develop a patient-specific 3D-printed breast phantom and to identify the most appropriate materials for simulating the MR imaging characteristics of fibroglandular and adipose tissues. Methods A patient-specific 3D-printed breast model was generated using 3D-printing techniques for the construction of the hollow skin and fibroglandular region shells. Then, the T1 relaxation times of the five selected materials (agarose gel, silicone rubber with/without fish oil, silicone oil, and peanut oil) were measured on a 3T MRI system to determine the appropriate ones to represent the MR imaging characteristics of fibroglandular and adipose tissues. Results were then compared to the reference values of T1 relaxation times of the corresponding tissues: 1,324.42±167.63 and 449.27±26.09 ms, respectively. Finally, the materials that matched the T1 relaxation times of the respective tissues were used to fill the 3D-printed hollow breast shells. Results The silicone and peanut oils were found to closely resemble the T1 relaxation times and imaging characteristics of these two tissues, which are 1,515.8±105.5 and 405.4±15.1 ms, respectively. The agarose gel with different concentrations, ranging from 0.5 to 2.5 wt%, was found to have the longest T1 relaxation times. Conclusions A patient-specific 3D-printed breast phantom was successfully designed and constructed using silicone and peanut oils to simulate the MR imaging characteristics of fibroglandular and adipose tissues. The phantom can be used to investigate different MR breast imaging protocols for the quantitative assessment of breast density.