Miracle-dz/UniFAR-FTUs huggingface dataset:
Reconstruction of lensless images using multi-scale attention block

Lensless imaging technology can recover real scenes from highly multiplexed measurements without the focusing lens, effectively reducing the size and cost of the camera. Current reconstruction algorithms based on iterative optimization are hard to generate high-quality results due to the superposition and inevitable loss of the valid information. In order to improve the quality of the generated results, we propose a two-stage network. The first stage generates preliminary scene mapping results. The second stage uses a modified U-net network to construct a backbone network that suppresses the use of error information measured in the Point Spread Function (PSF). Meanwhile, the multiple attention modules are used at different scales of the network to extract the effective feature information in the PSF and reformulate the image information. In addition, a dual-scale discriminator structure is proposed to further improve the image quality. The results of experiments in the mask-based lensless imaging system captured dataset show that the method produces realistic results.
An optical fiber pH sensor based on sodium alginate

A fiber optical Mach-Zehnder interferometer sensor for pH sensing has been proposed in this work. Single-Mode Fiber (SMF) and No-Core Fiber (NCF) are symmetrically spliced to form a SMF-NCF-SMF-NCF-SMF Mach-Zehnder interference structure. Sodium Alginate (SA) and Poly (diallyldimethylammonium chloride) (PDDA) are alternatively deposited on the sensing region to form the sensing coating via a Layer-By-Layer (LbL) self-assembly technique. The thickness and morphology of the SA/PDDA sensing coating are characterized by using Scanning Electron Microscopy (SEM). The images showed that uniform, granular, and continuous film with a thickness of 431.7 nm was obtained. The sensor exhibits a liner and fast response in the range of pH 3-9. The sensitivity of sensor is 0.99nm/pH, the response time and the recovery time are 12s and 16s respectively. This indicated that the highly sensitive and good potential for environmental analysis and process control.
Signal, noises, and detectivities in multi-stage infrared photodetectors

Multi-stage infrared (IR) photodetectors with discrete absorbers can circumvent the diffusion length limitation compared to the conventional detector with a continuous absorber. In this multi-stage detector architecture, the absorber in each stage is designed with a thickness that is thinner than the carrier diffusion length so that photo-generated carriers can be collected efficiently and quickly. Among them, interband cascade IR photodetectors (ICIPs) based on type-II heterostructures with unipolar (electron and hole) barriers are the most promising for high temperature and high-speed operation. In this paper, a comprehensive theory on signal, noises and detectivities in ICIPs will be reviewed and discussed to gain improved understanding of multi-stage IR detectors. Also, a formula is derived to correctly evaluate the detectivity for conventional photodetectors under reverse bias.
A method for calculating visual axis stability of multi-angle cameras for carbon monitoring

Multi angle camera is the main load of carbon monitoring satellite, and its main application is to improve the accuracy of parameter inversion through multi-directional observation of Bidirectional Reflectance coefficient (BRDF) of forest vegetation. The retrieval accuracy of forest parameters has a strong correlation with the sensitive angle BRDF, which requires that the multi angle camera has a high LOS pointing stability. In this paper, a method to calculate LOS stability based on the rigid body motion and sensitivity matrix of optical elements is proposed. In order to solve the problem that the coordinate system between the sensitivity matrix of optical elements and its rigid body motion is inconsistent, the rotation matrix of each optical element is introduced in the calculation process, and the formula for calculating LOS pointing deviation is given. Finally, according to the formula, the LOS change of the system under this working condition is obtained through simulation analysis of an actual working condition, and compared with the optical software simulation method, the calculation results are consistent, and the calculation method is correct. It can provide a new way to calculate the LOS stability of other remote sensors.
Optical fiber copper (II) ion sensor based on Mach–Zehnder interferometer

Copper being a heavy metal also plays a vital role in many organisms and industry. Traditional methods can not realize real-time and continuous monitoring of Copper (II) ion in water quality, therefore a new method based on Mach–Zehnder interferometer optical fiber sensor has been proposed. In the sensing region, the optical fiber has been coated with chitosan and Poly (sodium 4-styrene sulfonate) sensitive material, whose refractive index changes with the variation of Copper (II) ion concentration because chitosan can chelate Cu2+ ions. when the light signal interacts with the sensitive material, its wavelength, intensity, phase, or polarization state also changes along with the variation of Cu2+ ion concentration. Finally, the measured Copper (II) ion concentration can be monitored by detecting the change of optical fiber output signal. It is observed that the (CS/PSS)10 multilayer film surface has highly roughed morphology by SEM photograph, and film thickness is about 118.3 nm. The average thickness of CS/PSS was estimated about 11.83nm. The sensing results showed that the Cu2+ detection sensitivity of proposed sensor in the range of 0 to 10 µM and 10 to 1000 µM is 37.81 nm/mM and 7.1 nm/mM, respectively. The response times were approximately 56 s for 200 µM Cu2+ concentrations. Furthermore, the sensor was of high specificity towards Cu2+ ion compared to K+, Ca2+, Na+ and Mg2+. This method achieved specific and long-distance Cu2+ monitoring, which demonstrates promising applications in environmental monitoring fields.
Analyzing Design Typicality by Image Classification with Deep Learning

While deep learning has been successfully applied to many domains and industries, it is still in the first step to investigate the potential application to the field of industrial design. This paper discusses how image classification with deep learning can be used to analyze design typicality, which is the primary factor in designing product appearance and brand image. For promoting it to no-code designers such as industrial designers to adopt the process based on the machine learning tool, Waikato Environment for Knowledge Analysis (Weka) is introduced. A pilot study shows a promising approach for the designers to build datasets, pre-process, train models, test models, and measure prediction performance. This study suggests bridging image classification techniques to product design processes to advance design research and practice.
A Method to Classify Data Quality for Decision Making Under Uncertainty

Every decision-making process is subject to a certain degree of uncertainty. In sectors where the outcomes of the operations planned are uncertain and difficult to control such as in forestry, data describing the available resources can have a large impact on productivity. When planning activities, it is often assumed that such data are accurate, which causes a need for more replanning efforts. Data verification is kept to a minimum even though using erroneous information increases the level of uncertainty. In this context, it is relevant to develop a process to evaluate whether the data used for planning decisions are appropriate, so as to ensure the decision validity and provide information for better understanding and actions. However, the level of data quality alone can sometimes be difficult to interpret and needs to be put into perspective. This article proposes an extension to most data quality assessment techniques by comparing data to past quality levels. A classification method is proposed to evaluate the level of data quality in order to support decision making. Such classification provides insights into the level of uncertainty associated with the data. The method developed is then exploited using a theoretical case based on the literature and a practical case based on the forest sector. An example of how classified data quality can improve decisions in a transportation problem is finally shown.
Endangered Tujia language Speech Recognition Research based on Audio-Visual Fusion

As an endangered language, Tujia language is a non-renewable intangible cultural resource. Automatic speech recognition (ASR) uses artificial intelligence technology to facilitate manually label Tujia language, which is an effective means to protect this language. However, due to the fact that Tujia language has few native speakers, few labeled corpus, and much noise in the corpus. The acoustic models thus suffer from over fitting and lowe noise immunity, which seriously harms the accuracy of recognition. To tackle the deficiencies, an approach of audio-visual speech recognition (AVSR) based on Transformer-CTC is proposed, which reduces the dependence of acoustic models on noise and the quantity of data by introducing visual modality in-formation including lip movements. Specifically, the new approach enhances the expression of speakers’ feature space through the fusion of audio and visual information, thus solving the problem of less available information for single modality. Experiment results show that the optimal CER of AVSR is 8.2% lower than that of traditional models, and 11.8% lower than that for lip reading. The proposed AVSR tackles the issue of low accuracy in recognizing endangered languages. Therefore, AVSR is of great significance in studying the protection and preservation of endangered languages through AI.
Affect recognition using simplistic 2D skeletal features from the upper body movement

Over the past two decades, affective computing has garnered considerable attention. However, affective computing using body modality is still in its initial stages. Body affect detection using 3D skeletal data or motion capture data has seen some progress and produced promising results, but such advancement using RGB videos is yet to be achieved. In this paper, using OpenPose, 2D skeletal data is extracted from RGB videos. Joint location and joint angle features from MPIIEmo and GEMEP datasets are used to efficiently recognize affective states of angry, happy, sad, and surprise.
Multi-Modal Depression Detection Based on High-Order Emotional Features

The diagnosis of depression has always been a difficulty in its treatment. At present, the research on automatic depression detection mostly directly uses low-order features such as video, audio and text as input. The lack of guidance of high-order features may be a potential problem. This paper proposed a multi-modal depression detection method based on high-order emotional features. A two-stage network is designed to realize emotion recognition and depression detection at the same time, and input the emotional results as high-order semantic features into the improved TBJE-E multi-modal network. This process guided the learning of other modalities with the help of co-attention module, and finally gave the prediction results. The results of experiments on DAIC-WOZ dataset show that the addition of emotional features effectively complements the high-order semantics. Compared with the original TBJE model, the F1 performance of TBJE-E model with emotional features is relatively improved by 6.3%. The method in this paper has reached the SOTA level in the depression detection task. The experimental data also show that at present, the risk of individual internal psychological privacy being stolen by this technology without their knowledge is very low, and this technology has some application value in criminal investigation, psychological diagnosis and treatment and other professional fields.
Norwegian Teachers’ Experiences with Distance Teaching and Online Schooling During the COVID-19 Pandemic

ABSTRACT In Mach 2020, the Norwegian schools were closed in response to Covid19. The current study concerns teachers’ experiences of distance teaching in April 2020 during the online schooling period in one Norwegian municipality. A survey was sent via e-mail to all teachers in primary and secondary state schools in the explored municipality. Thematic analysis of the data provided the following three themes: (1) Adjustment of teaching context (2) Involving and supportive management. (3) A culture of collaboration and sharing. Distance teaching is time-consuming for teachers, increases inequality in learning and increases the needs for adaptive education. Teachers’ experience of interaction and collaboration with other professions is generally challenged and reduced due to online schooling.
An $\ell_1$-Method for Clustering High-Dimensional Data.

In general, the clustering problem is NP-hard, and global optimality cannot be established for non-trivial instances. For high-dimensional data, distance-based methods for clustering or classification face an additional difficulty, the unreliability of distances in very high-dimensional spaces. We propose a distance-based iterative method for clustering data in very high-dimensional space, using the $\ell_1$-metric that is less sensitive to high dimensionality than the Euclidean distance. For $K$ clusters in $\mathbb{R}^n$, the problem decomposes to $K$ problems coupled by probabilities, and an iteration reduces to finding $Kn$ weighted medians of points on a line. The complexity of the algorithm is linear in the dimension of the data space, and its performance was observed to improve significantly as the dimension increases.
Home security cameras as a tool for behavior observations and science equity

Reliably capturing transient animal behavior in the field and laboratory remains a logistical and financial challenge, especially for small ectotherms. Here, we present a camera system that is affordable, accessible, and suitable to monitor small, cold-blooded animals historically overlooked by commercial camera traps, such as small amphibians. The system is weather-resistant, can operate offline or online, and allows collection of time-sensitive behavioral data in laboratory and field conditions with continuous data storage for up to four weeks. The lightweight camera can also utilize phone notifications over Wi-Fi so that observers can be alerted when animals enter a space of interest, enabling sample collection at proper time periods. We present our findings, both technological and scientific, in an effort to elevate tools that enable researchers to maximize use of their research budgets. We discuss the relative affordability of our system for researchers in South America, which is home to the largest population of ectotherm diversity.
Dynamic Entrainment: A deep learning and data-driven process approach for synchronization in the Hodgkin-Huxley model

Resonance and synchronized rhythm are important phenomena and can be either constructive or destructive in dynamical systems in the nature, specifically in biology. There are many examples showing that the human’s body organs must maintain their rhythm in order to function properly. For instance, in the brain, synchronized or desynchronized electrical activities can lead to neurodegenerative disorders such as Huntington’s disease. In this paper, we adopt a well known conductance based neuronal model known as Hodgkin-Huxley model describing the propagation of action potentials in neurons. Armed with the “data-driven” process alongside the outputs of the Hodgkin-Huxley model, we introduce a novel Dynamic Entrainment technique, which is able to maintain the system to be in its entrainment regime dynamically by applying deep learning approaches.
Nucleosome reorganisation in breast cancer tissues

Nucleosome repositioning in cancer is believed to cause many changes in genome organisation and gene expression. Understanding these changes is important to elucidate fundamental aspects of cancer. It is also important for medical diagnostics based on cell-free DNA (cfDNA), which originates from genomic DNA regions protected from digestion by nucleosomes. Here we have generated high resolution nucleosome maps in paired tumour and normal tissues from the same breast cancer patients using MNase-assisted histone H3 ChIP-seq and compared them with the corresponding cfDNA from blood plasma. This analysis has detected single-nucleosome repositioning at key regulatory regions in a patient-specific manner and common cancer-specific patterns across patients. The nucleosomes gained in tumour versus normal tissue were particularly informative of cancer pathways, with ∼20-fold enrichment at CpG islands, a large fraction of which marked promoters of genes encoding DNA-binding proteins. In addition, tumour tissues were characterised by a 5-10 bp decrease in the average distance between nucleosomes (nucleosome repeat length, NRL), which is qualitatively similar to the differences between pluripotent and differentiated cells. These effects were correlated with gene activity, DNA sequence repeats abundance, differential DNA methylation and binding of linker histone variants H1.4 and H1X. Our findings provide a new mechanistic understanding of nucleosome repositioning in tumour tissues that can be valuable for patient stratification and monitoring using liquid biopsies.
A 1.5-Mb continuous endogenous viral region in the arbuscular mycorrhizal fungus Rhizophagus irregularis

Most fungal viruses are RNA viruses and no double-stranded DNA virus that infects fungi is known to date. A recent study detected DNA polymerase genes that originated from large dsDNA viruses in the genomes of basal fungi, suggestive of the existence of dsDNA viruses capable of infecting fungi. In this study, we searched for viral infection signatures in chromosome-level genome assemblies of the arbuscular mycorrhizal fungus Rhizophagus irregularis. We identified a continuous 1.5 Mb putative viral region on a chromosome in R. irregularis strain 4401. Phylogenetic analyses revealed that the viral region is related to viruses in the family Asfarviridae of the phylum Nucleocytoviricota. Single-copy marker genes from Nucleocytoviricota were detected as single-copy genes in the viral region. Furthermore, this viral region was absent in the genomes of four other R. irregularis strains and had fewer signals of fungal transposable elements than the other genomic regions. These results suggest a recent and single insertion of a large dsDNA viral genome in the genome of this fungal strain, providing strong evidence of the recent infection of the fungus by a dsDNA virus.
nELISA: A high-throughput, high-plex platform enables quantitative profiling of the secretome

We present the nELISA, a high-throughput, high-fidelity, and high-plex protein profiling platform. DNA oligonucleotides are used to pre-assemble antibody pairs on spectrally encoded microparticles and perform displacement-mediated detection. Spatial separation between non-cognate antibodies prevents the rise of reagent-driven cross-reactivity, while read-out is performed cost-efficiently and at high-throughput using flow cytometry. We assembled an inflammatory panel of 191 targets that were multiplexed without cross-reactivity or impact on performance vs 1-plex signals, with sensitivities as low as 0.1pg/mL and measurements spanning 7 orders of magnitude. We then performed a large-scale secretome perturbation screen of peripheral blood mononuclear cells (PBMCs), with cytokines as both perturbagens and read-outs, measuring 7,392 samples and generating ∼1.5M protein datapoints in under a week, a significant advance in throughput compared to other highly multiplexed immunoassays. We uncovered 447 significant cytokine responses, including multiple putatively novel ones, that were conserved across donors and stimulation conditions. We also validated the nELISA’s use in phenotypic screening, and propose its application to drug discovery.
Persistent Memory in Database Systems

Persistent memory is a promising new hardware that aims to close the gap between traditional storage devices (such as SSD and HDD) and main memory (i.e., DRAM). It inherits the persistency and capacity of SSDs while retaining the low latency and high bandwidth of DRAM at affordable costs. However, there is no free lunch: persistent memory is unlikely to outright eclipse either one and therefore exists as a hybrid device between memory and storage. Nonetheless, its properties are unique and it is up to the systems community to define its role in future software. In this dissertation, we make two contributions towards this goal. First, we propose an optimized integration of persistent memory into database management systems that makes full use of its persistency, low latency and byte-addressability. Our OLTP-focused system is build on a buffer manager that employs pointer swizzling and different page sizes that can be lazily loaded. It is build to gracefully scale over a three tier storage hierarchy with DRAM, persistent memory and SSDs delivering competitive performance to layer-specialized systems. Second, we provide one of the first evaluations of actual persistent memory hardware. Our investigation of the bandwidth, latency, and special characteristics is an essential contribution to understanding persistent memory and assists developers in optimizing algorithms and data structures. Using these findings we define a set of guidelines for the efficient use of persistent memory. Adhering to these insights we design highly-tuned algorithms for logging, page propagation, and in-place updates. Further, we suggest a novel technique to interleave multiple persistent writes using user space threads that allows to largely avoid the memory stall associated with a persistent write operation.
3D Eigenfaces for Face Modeling

In this paper we propose the idea of approximating an arbitrary 3D face model by a linear combination of ”3D Eigenfaces”. First, a set of 3D face models are constructed by a face modeling system and the principal component analysis is performed over the data set. The eigen vectors associated with the largest eigen values are extracted to form the eigen space. We call these eigen vectors ”3D Eigenfaces”. Any face model can then be approximated by a linear combination of these eigenfaces, thus reducing the whole space of face shape to the smaller space of these coefficients. We show that the reconstructed faces using these 3D eigenfaces are sufficiently close to the original faces. This result can be applied in image-based reconstruction of 3D face models.