Miracle-dz/UniFAR-FTUs huggingface dataset:
Rayleigh anomaly-enabled mode hybridization in gold nanohole arrays by scalable colloidal lithography for highly-sensitive biosensing

Abstract Plasmonic sensors exhibit tremendous potential to accomplish real-time, label-free, and high-sensitivity biosensing. Gold nanohole array (GNA) is one of the classic plasmonic nanostructures that can be readily fabricated and integrated into microfluidic platforms for a variety of applications. Even though GNA has been widely studied, new phenomena and applications are still emerging continuously expanding its capabilities. In this article, we demonstrated narrow-band high-order resonances enabled by Rayleigh anomaly in the nanohole arrays that are fabricated by scalable colloidal lithography. We fabricated large-area GNAs with different hole diameters, and investigated their transmission characteristics both numerically and experimentally. We showed that mode hybridization between the plasmon mode of the nanoholes and Rayleigh anomaly of the array could give rise to high-quality decapole resonance with a unique nearfield profile. We experimentally achieved a refractive index sensitivity, i.e., RIS up to 407 nm/RIU. More importantly, we introduced a spectrometer-free refractive index sensing based on lens-free smartphone imaging of GNAs with (intensity) sensitivity up to 137%/RIU. Using this platform, we realized the label-free detection of BSA molecules with concentration as low as 10−8 M. We believe our work could pave the way for highly sensitive and compact point-of-care devices with cost-effective and high-throughput plasmonic chips.
Utilization of CRISPR-Mediated Tools for Studying Functional Genomics in Hematological Malignancies: An Overview on the Current Perspectives, Challenges, and Clinical Implications

Hematological malignancies (HM) are a group of neoplastic diseases that are usually heterogenous in nature due to the complex underlying genetic aberrations in which collaborating mutations enable cells to evade checkpoints that normally safeguard it against DNA damage and other disruptions of healthy cell growth. Research regarding chromosomal structural rearrangements and alterations, gene mutations, and functionality are currently being carried out to understand the genomics of these abnormalities. It is also becoming more evident that cross talk between the functional changes in transcription and proteins gives the characteristics of the disease although specific mutations may induce unique phenotypes. Functional genomics is vital in this aspect as it measures the complete genetic change in cancerous cells and seeks to integrate the dynamic changes in these networks to elucidate various cancer phenotypes. The advent of CRISPR technology has indeed provided a superfluity of benefits to mankind, as this versatile technology enables DNA editing in the genome. The CRISPR-Cas9 system is a precise genome editing tool, and it has revolutionized methodologies in the field of hematology. Currently, there are various CRISPR systems that are used to perform robust site-specific gene editing to study HM. Furthermore, experimental approaches that are based on CRISPR technology have created promising tools for developing effective hematological therapeutics. Therefore, this review will focus on diverse applications of CRISPR-based gene-editing tools in HM and its potential future trajectory. Collectively, this review will demonstrate the key roles of different CRISPR systems that are being used in HM, and the literature will be a representation of a critical step toward further understanding the biology of HM and the development of potential therapeutic approaches.
Sedimentary Anthropogenic Carbon Signals From the Western Pacific Margin for the Last Century

The declining trend of the δ 13C of tropical corals over the last century was about −0.01‰ year−1, according to global coral records. The decrease was attributable to the significant input of anthropogenic CO2 (13C Suess effect) to the atmosphere. Previous studies of δ 13C in corals suggested that the signal of the anthropogenic carbon in the Pacific and Indian Oceans were weaker than that in the Atlantic Ocean. However, biases relating to environments in which corals grew caused concerns. To investigate the anthropogenic carbon signal in the Western Pacific, foraminiferal records in a suite of 13 box cores with good age control were obtained from the continental slope off southwestern Taiwan between 2004 and 2006. δ 18O values of planktonic foraminifera (Globigerinoides sacculifer or so-called Trilobatus sacculifer) in collected cores were relatively stable at −2.5‰ to −2‰ in the last century, but foraminiferal δ 13C had a gradual secular decline after the 1900s. The decline trend of δ 13C began to intensify after the 1960s, and its rate was similar to that observed in the Atlantic. Similar decline trends of δ 13C were also found in coral records at regions where the human activity is high (Liuqiu) and low (Dongsha). Our findings indicate that the anthropogenic carbon signal in the Western Pacific was not weaker than that recorded in the Atlantic, and the nearshore sediment can supplement the lack of δ 13C records in corals, which are deficient when the environment is not suitable to grow.
Manual Therapy Research Methods in Animal Models, Focusing on Soft Tissues

Manual therapies have been practiced for centuries, yet little research has been performed to understand their efficacy and almost no animal research has been performed to inform mechanisms of action. The methods of manual therapy practice are quite varied and present a challenge for scientists to model the treatments and perform research using rodents. In this perspective we present a descriptive analysis of the complexity of the treatments, highlighting the role of tissue mechanics and physics. With these complexities in mind, we compare using manual therapy as clinically practiced, to attempts to develop machinery to model or mimic manual therapy. We propose that because of the complexities of manual therapy as practiced, having therapists perform the treatments on research animals just as they would on humans is the most scientific approach. Our results using this approach have supported its practicality.
Fuzzing: A Survey for Roadmap

Fuzz testing (fuzzing) has witnessed its prosperity in detecting security flaws recently. It generates a large number of test cases and monitors the executions for defects. Fuzzing has detected thousands of bugs and vulnerabilities in various applications. Although effective, there lacks systematic analysis of gaps faced by fuzzing. As a technique of defect detection, fuzzing is required to narrow down the gaps between the entire input space and the defect space. Without limitation on the generated inputs, the input space is infinite. However, defects are sparse in an application, which indicates that the defect space is much smaller than the entire input space. Besides, because fuzzing generates numerous test cases to repeatedly examine targets, it requires fuzzing to perform in an automatic manner. Due to the complexity of applications and defects, it is challenging to automatize the execution of diverse applications. In this article, we systematically review and analyze the gaps as well as their solutions, considering both breadth and depth. This survey can be a roadmap for both beginners and advanced developers to better understand fuzzing.
Insulator Flashover Voltage Forecasting Based on Neural Network and Ant Colony Algorithm

To improve prediction precision and efficiency of icing insulator flashover voltage,combination of BP neural network with ant colony algorithm is proposed to design prediction model.According to the experimental data of flashover voltage and its impact factors,the neural network prediction model is established.Taking neural network weights and threshold values as decision variables,the global minimum error can be obtained by using iterative computation with ant colony.Then secondary training of neural network can be accomplished.Example results show that the method has high prediction accuracy and it is suitable for prediction of insulator flashover voltage.
Mechanical bistability of the mesoderm epithelium facilitates mesoderm invagination during Drosophila gastrulation

Apical constriction driven by non-muscle myosin II (“myosin”) provides a well-conserved mechanism to mediate epithelial folding. It remains unclear how contractile forces near the apical surface of a cell sheet drive out-of-the-plane bending of the sheet and whether myosin contractility is required throughout folding. By optogenetic-mediated acute inhibition of myosin, we find that during Drosophila mesoderm invagination, myosin contractility is critical to prevent tissue relaxation during the early, “priming” stage of folding but is dispensable for the actual folding step after the tissue passes through a stereotyped transitional configuration. The binary response suggests that the mesoderm is mechanically bistable during gastrulation. Combined modeling analysis and experimental measurements suggest that the observed mechanical bistability may arise from apicobasal shrinkage of the surrounding ectoderm, which promotes mesoderm invagination by facilitating a buckling transition. Our results suggest that Drosophila mesoderm invagination requires a joint action of local myosin contractility and mechanical bistability of the epithelium to trigger epithelial buckling.
Searching for Refactoring Opportunities to apply the Strategy Pattern

It is difficult to maintain and to adapt poorly written code presenting shortcomings in its structure. Refactoring techniques are used to improve the code and the structure of applications, making them better and easier to modify. Design patterns are reusable solutions used in similar problems in object-oriented systems, so there is no need to recreate the solutions. Applying design patterns in the context of refactoring in a corrective way becomes a desired activity in the life cycle of a specific software system. However, in large-scale projects, the manual examination of artefacts to find problems and opportunities to apply a design pattern is a hard task. In this context, we present a metric-based heuristic function to detect where the Strategy design pattern can be applied in a given project. To evaluate the heuristic function and its results we have also built a tool to show the results. This tool can examine source code using ASTs (Abstract Syntax Trees), searching for opportunities to apply the Strategy pattern, indicating the exact location in the source code where the pattern is suggested, also showing some evidences used in the detection.
A Cyber-Security Prototype Module for Smart Bikes

The Internet of Things (IoT) applications have been explored extensively in the past years due to multiple factors, notably advancements in hardware and software. Nevertheless, IoT devices are still vulnerable to attacks because of their nature, e.g.: lower computation, storage, and network capacities. IoT is considered the cornerstone of the smart city paradigm. ITS-Intelligent Transportation Systems is one major component of a smart city and, in turn, Smart Bikes have become an unavoidable environmentally friendly means within ITS. The way the different entities of an ITS communicate and how their communications are secured is a main issue that is being addressed by researchers in multiple different approaches. In this paper, a cyber-security system that utilizes pre-existing encryption methods to implement a secured real-time double hybrid encryption algorithm is pro-posed. As an illustrative example, the system proposed is applied to a Smart autonomous bike prototype for encryption during the data transfer between the bike and the different surrounding elements in its environment.
Urban Rail Traffic Security Management System Based on Big Data Platform

To absorb new technological achievements and realize ecologicalization, digitization, intelligentization, high speed and dynamic energy conversion, the state has been accelerating construction of emerging infrastructure in the latest years, including urban rail traffic, industrial internet, 5G base station, artificial intelligence and big data center. In future development of digital cities, digitization must be available to manage complex system of megacity in high efficiency. However, information security threat is the most serious challenge to digitization. Especially for the significant key infrastructure like urban rail traffic, effective and complete information security management is especially important. This paper focuses on security protection and researches the framework of urban rail traffic security management center based on big data analysis from perspective of technology, management and operation to solve the difficulty of security management center framework design in urban rail traffic digitization process.
Music Suggestions from Determining the Atmosphere of Images

Human emotion is affected by visual stimuli from all around the environment. This change in emotion and mood carries over to our tastes in various things, one of them being music. Computer vision and data mining are sub-fields of computer science that deal with the interpretation of visual media and analysis of temporal data to derive an outcome respectively. There has yet to be a method of interpreting visual media and associating it with any sort of music genre. Computer vision can be utilized to recognize the effect of visual elements of the environment on human emotion while data mining can be used to suggest appropriate music genres based on that emotion. This is the approach our paper used to solve the aforementioned problem.In this paper, we have used different models to interpret different attributes from a given image. In our implementation, five attributes were identified and five models were used to detect them. A few surveys were conducted to get a pattern in people’s taste in music according to visual stimuli. The results from the surveys were then used to recommend a music genre from the processed combination of attributes mentioned before. This shall provide a starting point and motivate further research of its kind.
Accelerating Power Grid Monitoring with Flying Robots and Artificial Intelligence

The digital revolution is expected to surpass all previous economic transformations in scale, scope, and complexity. Digital technologies are making electrical grids more connected, more reliable, and sustainable. Several efforts have been made to revamp the electric grid and modernize century-old systems. In the near future, we expect to see a revolution in power grind monitoring with incredible results through artificial intelligence and big data, drones, among others. The business opportunity for using drones in the energy sector is impressive, although very few companies have joined in its implementation. The drone allows the safe remote overflight of high voltage power lines. They can be deployed to detect, inspect, and diagnose the defects of the power line infrastructure. In this article, we provide the state of the art of using drones in smart grid monitoring. We demonstrate and propose an architecture based on Faster R-CNN for detecting ice accretion on power lines. Finally, we shed light on opportunities and future trends of these emerging technologies that can guide future research directions.
Unsupervised Deep Learning to Detect Agitation from Videos in People with Dementia

Behavioural symptoms of dementia present a significant risk within Long Term Care (LTC) homes, which face difficulties supporting residents and monitoring their safety with limited staffing resources. Many LTC facilities have installed video surveillance systems in common areas that can help staff to observe residents; however, typically these video streams are not monitored. In this paper, we present the development of a computer vision algorithm to use these video streams to detect episodes of clinically important agitation in people with dementia. Given that episodes of agitation are rare in comparison to normal behaviours, we formulated this as an anomaly detection problem. This involves using the video camera to monitor the scene rather than tracking individuals. We developed a customized spatio-temporal convolution autoencoder that is trained on the normal behaviours and then identified agitation during testing as anomalous behaviour. We present a proof-of-concept using video data collected from a specialized dementia unit and annotated for agitation events. We trained the unsupervised neural network on approximately 24 hours of normal activities and tested on 11 hours of videos containing both normal activities and agitation events, and obtained an area under the curve of the receiver operating characteristic curve of 0.754. This research paves the way for leveraging existing surveillance infrastructure in LTC and other mental health settings to detect agitation or aggression, with the potential for improved health and safety.
Integrative approaches to environmental life cycle assessment of consumer electronics and connected media

The environmental impacts of information and communication technologies and consumer electronics are challenging to evaluate. Organizations and individuals wishing to reduce the impacts attributable to their usage of these products and systems rely on a limited technical knowledge base that struggles to stay current. Using a life cycle assessment approach which expresses environmental impacts quantitatively in terms of greenhouse gas emissions and primary energy demand, this dissertation significantly expands our understanding of the impacts of desktop computers, electronics products in general, and connected media services accessed in the home, in order to support environmentally-conscious decision-making and policy regarding these products and systems. The first of three studies, a meta-analysis of prior life cycle assessments of desktop PCs, resolves an important ambiguity in this literature and demonstrates that greenhouse gas emissions due to operational energy consumption usually exceed those due to device manufacturing. The second study calculates embodied greenhouse gas emissions of eleven electronics products through a teardown analysis, and finds a linear relationship between mass and embodied emissions, thus demonstrating that lightweight, compact products offer environmental benefits relative to larger products. A comparison to studies of older products also reveals that newer products are more materially efficient, largely due to reduction in integrated circuit content per product. Finally, the third study calculates aggregate US consumer greenhouse gas emissions due to broadcast television, video on demand, online video, other online uses, and offline uses when consumed using televisions, personal computers, tablets, and smartphones, including emissions due to devices in the home, networks, and datacenters. The study concludes that emissions due to video end-uses account for 75% of total consumer ICT emissions. About 71% of consumer ICT emissions arise due to devices in the home, especially TVs and desktop PCs, with the remainder due to networks and datacenters. Mobile platforms using Wi-Fi connections are the least
Cognitive Radar Signal Processing Based on Convolutional Neural Network

Single-criterion is difficult to meet the radar’s multi-working mode and multi-tasks, a signal processing method using convolutional neural network (CNN) for cognitive radar is proposed, which uses the learning ability of CNN to learn the nonlinear relationship between one-dimensional data to joint the mutual information (MI) criterion and signal to noise ratio (SNR) criterion. Firstly, a train set composed of signals generated by MI and SNR criteria and the corresponding environmental information was constructed; Secondly, designing a CNN model based on the form of data set; Using the local connection and global sharing of CNN to reduce the number of network parameters and improve training efficiency. Using CNN to efficiently extract features of one-dimensional data from MI and SNR criteria to generate signal features, which are sent to the fully connected layer for regression to achieve the purpose of combining MI and SNR criteria. Finally, the CNN model is trained on the train set and test by test set. Simulation results prove that the proposed method can joint the MI criterion and SNR criterion, and improve the comprehensive performance of radar.
Assessment of the Efficiency of 5G Network Exposure for the Industrial Internet of Things

In this paper, we compare the efficiency of the traditional customer service request based configuration of a 5G network with that of a programmable network API, through the example of a use case that involves a self-reconfiguring production cell composed of 5G-connected modular robot elements. We demonstrate the features provided by the 3GPP Service Enablement Architecture Layer and how they can be applied in this factory of the future scenario. The outcome of this analysis will help industry players interested in adopting a 5G cellular wireless network to decide which is the most appropriate network configuration option for their use cases. A demonstration video is available at [1].
Critique of “MemXCT: Memory-Centric X-Ray CT Reconstruction With Massive Parallelization” by SCC Team From Georgia Tech

For the 2020 Student Cluster Competition, we reproduced results from “MemXCT: Memory-Centric X-ray CT Reconstruction with Massive Parallelization” (Hidayetoğlu et al.). Reproducibility is of critical importance to the scientific community, not just to verify correctness of results but also to see how easily others can understand and work with the given methods. MemXCT is an approach for image reconstruction in X-ray ptychography, which has a broad range of applications in materials science. MemXCT is not the only X-ray tomography algorithm, though; as opposed to compute-centric algorithms, it is designed to scale better by optimizing for memory bandwidth and memory latency. MemXCT also applies several key optimizations in order to ease memory pressure. In this article, we test the performance and strong scaling of MemXCT on 1 to 256 AMD CPU cores (1-4 nodes) and 1-16 Nvidia V100 GPUs (1-4 nodes). We confirm the impact of MemXCT’s optimizations. Still, we find that the performance of some important loops in the MemXCT kernel is much lower on the AMD processors (with AVX2) of our CPU nodes compared to the Intel CPUs (with AVX-512) used in the original article. We also confirm MemXCT performance on Tesla V100 GPUs, as reported in the article.
Speech Enhancement Model for High Sampling Rate Speech Datasets Based on Multi-branch Time Convolutional Network

In this paper, a high sampling rate speech enhancement method based on multi-branch time convolutional networks (TCN) is proposed. The most important parameter in traditional speech enhancement algorithms is the prior signal-to-noise ratio (SNR). In this paper, Deep Xi framework is used to estimate the prior SNR, and multi-branch TCN is proposed to realize the mapping of the amplitude spectrum of noisy speech to the prior SNR. The multi-branch time convolutional network proposed in this paper can better capture context information and smaller model size. In addition, in the waveform reconstruction stage, this paper proposes to use the weighted Euclidean distortion measure to correct the gain function. Experimental results on a speech dataset with a 48kHz sampling rate show that our strategy has more advanced performance and superior performance.