Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantum Circuits For Two-Dimensional Isometric Tensor Networks

The variational quantum eigensolver (VQE) combines classical and quantum resources in order simulate classically intractable quantum states. Amongst other variables, successful VQE depends on the choice of variational ansatz for a problem Hamiltonian. We give a detailed description of a quantum circuit version of the 2D isometric tensor network (isoTNS) ansatz which we call qisoTNS. We benchmark the performance of qisoTNS on two different 2D spin 1/2 Hamiltonians. We find that the ansatz has several advantages. It is qubit efficient with the number of qubits allowing for access to some exponentially large bond-dimension tensors at polynomial quantum cost. In addition, the ansatz is robust to the barren plateau problem due emergent layerwise training. We further explore the effect of noise on the efficacy of the ansatz. Overall, we find that qisoTNS is a suitable variational ansatz for 2D Hamiltonians with local interactions.
Distributional Modeling and Forecasting of Natural Gas Prices

We examine the problem of modeling and forecasting European Day-Ahead and Month-Ahead natural gas prices. For this, we propose two distinct probabilistic models that can be utilized in risk- and portfolio management. We use daily pricing data ranging from 2011 to 2020. Extensive descriptive data analysis shows that both time series feature heavy tails, conditional heteroscedasticity, and show asymmetric behavior in their differences. We propose state-space time series models under skewed, heavy-tailed distributions to capture all stylized facts of the data. They include the impact of autocorrelation, seasonality, risk premia, temperature, storage levels, the price of European Emission Allowances, and related fuel prices of oil, coal, and electricity. We provide rigorous model diagnostics and interpret all model components in detail. Additionally, we conduct a probabilistic forecasting study with significance tests and compare the predictive performance against literature benchmarks. The proposed Day-Ahead (Month-Ahead) model leads to a 13% (9%) reduction in out-of-sample continuous ranked probability score (CRPS) compared to the best performing benchmark model, mainly due to adequate modeling of the volatility and heavy tails.
Considerations for evaluating thermodynamic properties with hybrid quantum-classical computing work flows

Quantum chemistry applications on quantum computers currently rely heavily on the variational quantum eigensolver (VQE) algorithm. This hybrid quantum-classical algorithm aims at finding ground state solutions of molecular systems based on the variational principle. VQE calculations can be systematically implemented for perturbations to each molecular degree of freedom, generating a Born-Oppenheimer potential energy surface (PES) for the molecule. The PES can then be used to derive thermodynamic properties, which are often desirable for applications in chemical engineering and materials design. It is clear from this process that quantum chemistry applications contain a substantial classical computing component in addition to steps that can be performed using a quantum computer. In order to design efficient work-flows that take full advantage of each hardware-type, it is critical to consider the entire process so that the high-accuracy electronic energies possible from quantum computing are not squandered in the process of calculating thermodynamic properties. We present a summary of the hybrid quantum-classical work-flow to compute thermodynamic properties. This work-flow contains many options that can significantly affect the efficiency and the accuracy of the results, including classical optimizer attributes, number of ansatz repetitions, and how the vibrational Schrödinger equation is solved to determine vibrational modes. We also analyze the effects of these options by employing robust statistics along with simulations and experiments on actual quantum hardware. We show that through careful selection of work-flow options, nearly order-of-magnitude increases in accuracy are possible at equivalent computing time.
Extracting Masonry Building Facades through Polygon Image Segmentation

—3D modeling of buildings is an important task for various ﬁelds such as urban planning, architecture and engineering analysis. This has led to development of many techniques for automatic generation of building models. Among them, we are focusing on a computer vision segmentation approach to group pixels into facades of buildings. We compare two types of approaches to this problem. The ﬁrst one relies on facade segmentation to mask the relevant portion of the image, while the second detects the corners of the facade which are then connected to a polygon. We show that for the case of building facade segmentation, our approach using a convolutional neural network to generate polygons has the potential to give better results than standard segmentation.
Investigation on the Printed CNT-Film-Based Electrochemical Sensor for Detection of Liquid Chemicals

We studied electrochemical sensors using printed carbon nanotubes (CNT) film on a polyethylene telephtalate (PET) substrate. The mechanical stability of the printed CNT film (PCF) was confirmed by using bending and Scotch tape tests. In order to determine the optimum sensor structure, a resistance-type PCF sensor (R-type PCF sensor) and a comb-type PCF sensor (C-type PCF sensor) were fabricated and compared using a diluted NH3 droplet with various concentrations. The magnitude of response, response time, sensitivity, linearity, and limit of detection (LOD) were compared, and it was concluded that C-type PCF sensor has superior performance. In addition, the feasibility of PCF electrochemical sensor was investigated using 12 kinds of hazardous and noxious substances (HNS). The detection mechanism and selectivity of the PCF sensor are discussed.
Process Variability in Top-Down Fabrication of Silicon Nanowire-Based Biosensor Arrays

Silicon nanowire field-effect transistors (SiNW-FET) have been studied as ultra-high sensitive sensors for the detection of biomolecules, metal ions, gas molecules and as an interface for biological systems due to their remarkable electronic properties. “Bottom-up” or “top-down” approaches that are used for the fabrication of SiNW-FET sensors have their respective limitations in terms of technology development. The “bottom-up” approach allows the synthesis of silicon nanowires (SiNW) in the range from a few nm to hundreds of nm in diameter. However, it is technologically challenging to realize reproducible bottom-up devices on a large scale for clinical biosensing applications. The top-down approach involves state-of-the-art lithography and nanofabrication techniques to cast SiNW down to a few 10s of nanometers in diameter out of high-quality Silicon-on-Insulator (SOI) wafers in a controlled environment, enabling the large-scale fabrication of sensors for a myriad of applications. The possibility of their wafer-scale integration in standard semiconductor processes makes SiNW-FETs one of the most promising candidates for the next generation of biosensor platforms for applications in healthcare and medicine. Although advanced fabrication techniques are employed for fabricating SiNW, the sensor-to-sensor variation in the fabrication processes is one of the limiting factors for a large-scale production towards commercial applications. To provide a detailed overview of the technical aspects responsible for this sensor-to-sensor variation, we critically review and discuss the fundamental aspects that could lead to such a sensor-to-sensor variation, focusing on fabrication parameters and processes described in the state-of-the-art literature. Furthermore, we discuss the impact of functionalization aspects, surface modification, and system integration of the SiNW-FET biosensors on post-fabrication-induced sensor-to-sensor variations for biosensing experiments.
Clay-Based Polymer Nanocomposites: Essential Work of Fracture

This work details the general structure of the clays used as a reinforcement phase in polymer nanocomposites. Clays are formed by the molecular arrangement of atomic planes described through diagrams to improve their visualization. The molecular knowledge of clays can facilitate the selection of the polymer matrix and achieve a suitable process to obtain clay-based polymer nanocomposite systems. This work highlights the development of polymer nanocomposites using the melt intercalation method. The essential work of fracture (EWF) technique has been used to characterize the fracture behavior of materials that show ductility and where complete yielding of the ligament region occurs before the crack propagation. In this sense, the EWF technique characterizes the post-yielding fracture mechanics, determining two parameters: the specific essential work of fracture (we), related to the surface where the actual fracture process occurs, and the specific non-essential work of fracture (wp), related to the plastic work carried out in the outer zone of the fracture zone. The EWF technique has been used successfully in nano-reinforced polymers to study the influence of different variables on fracture behavior. In this work, the fundamentals of the EWF technique are described, and some examples of its application are compiled, presenting a summary of the most relevant contributions in recent years.
A Comprehensive Review on Edge Caching from the Perspective of Total Process: Placement, Policy and Delivery

With the explosive growth of smart devices and mobile applications, mobile core networks face the challenge of exponential growth in traffic and computing demand. Edge caching is one of the most promising solutions to the problem. The main purpose of edge caching is to place popular content that users need at the edge of the network, borrow free space to reduce user waiting time, and lighten the network load by reducing the amount of duplicate data. Due to the promising advantages of edge caching, there have been many efforts motivated by this topic. In this paper, we have done an extensive survey on the existing work from our own perspectives. Distinguished from the existing review articles, our work not only investigates the latest articles in this area, but more importantly, covers all the researches of the total process of edge caching from caching placement optimization, policy design, to the content delivery process. In particular, we discuss the benefits of caching placement optimization from the perspective of different stakeholders, detail the delivery process, and conduct in-depth discussions from the five phases, i.e., requested content analysis, user model analysis, content retrieval, delivery, and update. Finally, we put forward several challenges and potential future directions, and hope to bring some ideas for the follow-up researches in this area.
Dipodal Tetraamide Derivatives of 1,10-Diaza-18-Crown-6 and Alkylmalonic Acids—Synthesis and Use as Ionophores in Ion Selective Membrane Electrodes

Novel dipodal derivatives of an 18-membered diaza-crown ether with two diamide chains featuring methylmalonic or butylmalonic acid residues were obtained and tested as ionophores in ion-selective plasticized membrane electrodes. The objective of the study was to identify measurement conditions which ensure the most favorable performance for magnesium ion-selective electrodes. The relationship between the molar lipophilic anion salt-to-ionophore ratio and selectivity of electrodes was examined. The best result was obtained for the conventional electrode containing Mg2 ionophore. Calculated selectivity coefficients were as follows: logKMg/Ca = −2.77, logKMg/Na = −3.46 and logKMg.K = −2.24 (SSM, 1M).
Application of Intelligent Modeling Method to Optimize the Multiple Quality Characteristics of the Injection Molding Process of Automobile Lock Parts

This study focuses on applying intelligent modeling methods to different injection molding process parameters, to analyze the influence of temperature distribution and warpage on the actual development of auto locks. It explores the auto locks using computer-aided engineering (CAE) simulation performance analysis and the optimization of process parameters by combining multiple quality characteristics (warpage and average temperature). In this experimental design, combinations were explored for each single objective optimization process parameter, using the Taguchi robust design process, with the L18 (21 × 37) orthogonal table. The control factors were injection time, material temperature, mold temperature, injection pressure, packing pressure, packing time, cooling liquid, and cooling temperature. The warpage and temperature distribution were analysed as performance indices. Then, signal-to-noise ratios (S/N ratios) were calculated. Gray correlation analysis, with normalization of the S/N ratio, was used to obtain the gray correlation coefficient, which was substituted into the fuzzy theory to obtain the multiple performance characteristic index. The maximum multiple performance characteristic index was used to find multiple quality characteristic-optimized process parameters. The optimal injection molding process parameters with single objective are a warpage of 0.783 mm and an average temperature of 235.23 °C. The optimal parameters with multi-objective are a warpage of 0.753 mm and an average temperature of 238.71 °C. The optimal parameters were then used to explore the different cooling designs (original cooling, square cooling, and conformal cooling), considering the effect of the plastics temperature distribution and warpage. The results showed that, based on the design of the different cooling systems, conformal cooling obtained an optimal warpage of 0.661 mm and a temperature of 237.62 °C. Furthermore, the conformal cooling system is smaller than the original cooling system; it reduces the warpage by 12.2%, and the average temperature by 0.46%.
High Correlation of Static First-Minute-Frame (FMF) PET Imaging after 18F-Labeled Amyloid Tracer Injection with [18F]FDG PET Imaging

Dynamic early-phase PET images acquired with radiotracers binding to fibrillar amyloid-beta (Aβ) have shown to correlate with [18F]fluorodeoxyglucose (FDG) PET images and provide perfusion-like information. Perfusion information of static PET scans acquired during the first minute after radiotracer injection (FMF, first-minute-frame) is compared to [18F]FDG PET images. FMFs of 60 patients acquired with [18F]florbetapir (FBP), [18F]flutemetamol (FMM), and [18F]florbetaben (FBB) are compared to [18F]FDG PET images. Regional standardized uptake value ratios (SUVR) are directly compared and intrapatient Pearson’s correlation coefficients are calculated to evaluate the correlation of FMFs to their corresponding [18F]FDG PET images. Additionally, regional interpatient correlations are calculated. The intensity profiles of mean SUVRs among the study cohort (r = 0.98, p < 0.001) and intrapatient analyses show strong correlations between FMFs and [18F]FDG PET images (r = 0.93 ± 0.05). Regional VOI-based analyses also result in high correlation coefficients. The FMF shows similar information to the cerebral metabolic patterns obtained by [18F]FDG PET imaging. Therefore, it could be an alternative to the dynamic imaging of early phase amyloid PET and be used as an additional neurodegeneration biomarker in amyloid PET studies in routine clinical practice while being acquired at the same time as amyloid PET images.
Electrocardiogram-Based Emotion Recognition Systems and Their Applications in Healthcare—A Review

Affective computing is a field of study that integrates human affects and emotions with artificial intelligence into systems or devices. A system or device with affective computing is beneficial for the mental health and wellbeing of individuals that are stressed, anguished, or depressed. Emotion recognition systems are an important technology that enables affective computing. Currently, there are a lot of ways to build an emotion recognition system using various techniques and algorithms. This review paper focuses on emotion recognition research that adopted electrocardiograms (ECGs) as a unimodal approach as well as part of a multimodal approach for emotion recognition systems. Critical observations of data collection, pre-processing, feature extraction, feature selection and dimensionality reduction, classification, and validation are conducted. This paper also highlights the architectures with accuracy of above 90%. The available ECG-inclusive affective databases are also reviewed, and a popularity analysis is presented. Additionally, the benefit of emotion recognition systems towards healthcare systems is also reviewed here. Based on the literature reviewed, a thorough discussion on the subject matter and future works is suggested and concluded. The findings presented here are beneficial for prospective researchers to look into the summary of previous works conducted in the field of ECG-based emotion recognition systems, and for identifying gaps in the area, as well as in developing and designing future applications of emotion recognition systems, especially in improving healthcare.
Rebirth of Distributed AI—A Review of eHealth Research

The envisioned smart city domains are expected to rely heavily on artificial intelligence and machine learning (ML) approaches for their operations, where the basic ingredient is data. Privacy of the data and training time have been major roadblocks to achieving the specific goals of each application domain. Policy makers, the research community, and the industrial sector have been putting their efforts into addressing these issues. Federated learning, with its distributed and local training approach, stands out as a potential solution to these challenges. In this article, we discuss the potential interplay of different technologies and AI for achieving the required features of future smart city services. Having discussed a few use-cases for future eHealth, we list design goals and technical requirements of the enabling technologies. The paper confines its focus on federated learning. After providing the tutorial on federated learning, we analyze the Federated Learning research literature. We also highlight the challenges. A solution sketch and high-level research directions may be instrumental in addressing the challenges.
Texture Recognition Based on Perception Data from a Bionic Tactile Sensor

Texture recognition is important for robots to discern the characteristics of the object surface and adjust grasping and manipulation strategies accordingly. It is still challenging to develop texture classification approaches that are accurate and do not require high computational costs. In this work, we adopt a bionic tactile sensor to collect vibration data while sliding against materials of interest. Under a fixed contact pressure and speed, a total of 1000 sets of vibration data from ten different materials were collected. With the tactile perception data, four types of texture recognition algorithms are proposed. Three machine learning algorithms, including support vector machine, random forest, and K-nearest neighbor, are established for texture recognition. The test accuracy of those three methods are 95%, 94%, 94%, respectively. In the detection process of machine learning algorithms, the asamoto and polyester are easy to be confused with each other. A convolutional neural network is established to further increase the test accuracy to 98.5%. The three machine learning models and convolutional neural network demonstrate high accuracy and excellent robustness.
The Long-Term Maintenance of Upper Limb Motor Improvements Following Transcranial Direct Current Stimulation Combined with Rehabilitation in People with Stroke: A Systematic Review of Randomized Sham-Controlled Trials

Background: The effectiveness of transcranial direct current stimulation (tDCS) in the upper limb (UL) motor rehabilitation of stroke has been widely studied. However, the long-term maintenance of its improvements has not yet been proven. Methods: A systematic search was conducted in MEDLINE/Pubmed, Web of Science, PEDRo, and Scopus databases from inception to April 2021. Randomized controlled trials were included if they performed a tDCS intervention combined with UL rehabilitation in stroke patients, performed several sessions (five or more), and assessed long-term results (at least three-month follow-up). Risk of bias and methodological quality were evaluated with the Cochrane RoB-2 and the Oxford quality scoring system. Results: Nine studies were included, showing a high methodological quality. Findings regarding UL were categorized into (1) functionality, (2) strength, (3) spasticity. All the studies that showed significant improvements retained them in the long term. Baseline functionality may be a limiting factor in achieving motor improvements, but not in sustaining them over the long term. Conclusion: It seems that the improvements achieved during the application of tDCS combined with UL motor rehabilitation in stroke were preserved until the follow-up time (from 3 months to 1 year). Further studies are needed to clarify the long-term effects of tDCS.
Security Design, Informed Intermediation, and the Resolution of Borrowers’ Financial Distress*

Banks as informed intermediaries have information about their borrowers to make efficient liquidation versus restructuring decisions for distressed loans, but their information also creates an adverse selection problem when they seek financing from uninformed investors. We demonstrate that a bank with high-quality loans faces incentives to distort its resolution policy in order to improve allocative efficiency and to signal information about loan quality, with the direction of the distortion depending on whether the security issued to uninformed investors is concave or convex. We find that the bank’s equilibrium resolution policy is biased towards liquidation when it optimally designs and sells a debt (concave) security to raise financing. Regulations aimed at promoting ex post efficient liquidation may increase banks’ financing costs and discourage their screening effort, thereby reducing welfare. (JEL: D8, G21, G23, G24)
Fabry–Perot Resonance in 2D Dielectric Grating for Figure of Merit Enhancement in Refractive Index Sensing

We have recently reported in our previous work that one-dimensional dielectric grating can provide an open structure for Fabry–Perot mode excitation. The grating gaps allow the sample refractive index to fill up the grating spaces enabling the sample to perturb the Fabry–Perot mode resonant condition. Thus, the grating structure can be utilized as a refractive index sensor and provides convenient sample access from the open end of the grating with an enhanced figure of merit compared to the other thin-film technologies. Here, we demonstrate that 2D grating structures, such as rectangular pillars and circular pillars, can further enhance refractive index sensing performance. The refractive index theory for rectangular pillars and circular pillars are proposed and validated with rigorous coupled wave theory. An effective refractive index theory is proposed to simplify the 2D grating computation and accurately predict the Fabry–Perot mode positions. The 2D gratings have more grating space leading to a higher resonant condition perturbation and sensitivity. They also provide narrower Fabry–Perot mode reflectance dips leading to a 4.5 times figure of merit enhancement than the Fabry–Perot modes excited in the 1D grating. The performance comparison for thin-film technologies for refractive index sensing is also presented and discussed.