Miracle-dz/UniFAR-FTUs huggingface dataset:
Pure surface plasmon-polariton optical sensor using an H-cross-section fiber and Bragg gratings

In this paper, we develop a novel structure and approach of a pure surface Plasmon Polariton (SPP) sensor with fiber Bragg gratings (FBG) in gold coated specially designed H-cross-section fiber. We evaluate its potential for biological sensing applications of the surrounding refractive index (SRI). Pure SPP has almost all its field concentration at the metal-dielectric interfaces and hence, it is highly sensitive to changes in the sensed medium refractive index. This scheme represents an “in-line” optical fiber SPP sensor scheme. Also, it provides larger and more flexible sensing area along its flat side. Simulation results show that the gold layer thickness essentially influences the sensitivity, and the shape of the reflection spectrum. Increasing the gold layer thickness decreases its sensitivity and broadens the spectrum, increasing the FWHM bandwidth. Results show a maximum sensitivity of 230 nm/RIU and maximum figure of merit; defined as the ratio of sensitivity to FWHM bandwidth of the reflection spectrum; of 10952 RIU-1 (Sensitivity = 230 nm/RIU and FWHM = 2.1 x 10-5 μm) with gold thickness of 10 nm. In conclusion, the proposed sensor is highly sensitive and free from any moving parts and can be used as bio-chemical sensor.
Leveraging GCN and User Friendship for Next POI Recommendation

The next POI (point of interest) recommendation aims to explore the behaviour patterns from users’ historical check-in records and recommend the next location. However, solving the data sparsity problem and improving the model recommendation performance are still considerable challenges in POI recommendations. This paper proposes a GFUC model consisting of the GCN (Graph Convolutional Networks) and FUC (Friendship of User Context) modules. The GCN module integrates weighted graph convolutional networks to obtain the best representation of users and POIs. The FUC module divides user check-in records with multiple fine-grain manners. Then it incorporates the user friendship and context information, significantly improving the model’s performance while solving the data sparsity problem. More specifically, experimental results show that our proposed GFUC model improves the performance of POI recommendations by more than 10% on both Yelp and Gowalla datasets with the evaluation metrics Precision@10.
Programming Languages in Education: 50 Years of Evolution as Evidenced by Literature

Various programming languages have been used in education during the last 50 years. In this study, we investigate their popularity measured with the number of dedicated scientific publications. While some of the findings could be expected, some are far from predictable. Also interesting are the indicated differences between the presented results and the general popularity of languages.
A large planar 39-GHz antenna array of waveguide-fed horns

A planar antenna in which box horns are used as radiating elements is described. The feed network is built by connecting rectangular waveguides with T-junctions. The matching of the T-junctions is improved by using rounded splitters and matching pins in the junctions. The radiating element has been designed to cancel out the grating lobe. The grating lobe is due to an element spacing larger than one wavelength. The highest sidelobes are at least 31 dB below the main beam in the H-plane and 16 dB in the E-plane. A gain of 37 dBi has been achieved at 39.2 GHz. These results demonstrate the feasibility of this antenna for applications requiring high gain at millimeter wavelengths.
rvcodec.js: An Educational Converter for RISC-V Instructions

rvcodec.js is an open-source, online RISC-V instruction encoder-decoder, specifically geared towards students learning about instruction formats. For improved usability, rvcodec.js uses bright colors to visually map the relationships between the instruction's assembly tokens and binary fields. Students can, for example, quickly identify which bits in the binary representation correspond to a certain immediate value in the assembly instruction. rvcodec.js is available at https://luplab.gitlab. io/rvcodecjs/.
Grasp stability assessment through the fusion of visual and tactile signals using deep bilinear network

Evaluating the grasp stability of the robot is critical to robotic manipulation. It is very effective to combine visual and tactile information to evaluate the stability of a grip. However, most of the methods directly concatenate features of heterogeneous data that may ignore the comprehensive interaction between different modalities. Furthermore, existing methods ignore the influence of intra-modality that may degrade the performance of grasp stability assessment. To address this issue, we proposed a framework named visual and tactile deep bilinear network (VTDBN) to evaluate the grasp stability of robots by integrating visual data and tactile data. Moreover, we conduct comprehensive experiments to build a dataset that can be used for training and testing. The experiment results show that VTDBN model significantly improves the performance of robotic grasp stability assessment and outperforms traditional methods.
I don’t feel so good! Detecting Depressive Tendencies using Transformer-based Multimodal Frameworks

One of the most common mental illnesses that affects 5% of adults globally is depression. The advancement of social media has meant that more and more people have gained a platform to voice their thoughts and beliefs. People’s social media interactions and posted content can be used to infer critical characteristics such as depressive tendencies which will allow for timely intervention and help. This paper describes a novel supervised approach to detect depressive tendencies in Twitter users using multimodal frameworks which account for user interaction and online behaviour in addition to the tweet content processed using transformers like BERT. The performance of three multimodal frameworks is described with different methods for combining modalities. The best result is obtained a cross-modality based model which improves the baseline by 12% points.
Low-frequency ambient sound in the North Pacific: Long time series observations

Long-term statistics of ambient sound in an ocean basin have been derived from 2 years of data collected on 13 widely distributed receivers in the North Pacific. The data consist of single hydrophone spectra (1–500 Hz in 1-Hz bands) averaged over 170 s and recorded at 5-min intervals. Cumulative probability distributions of the ambient sound level show that for the open-ocean arrays at 75 Hz, sound levels are 3 dB higher than the median level 10% of the time and 6 dB higher 1% of the time. For the coastal arrays, sound levels are 7 dB higher than the median level 10% of the time and 15 dB higher 1% of the time. The clearest feature in many of the spectrograms is a strong annual cycle in the 15–22 Hz band with peak signal levels up to 25 dB above the sound floor; this cycle is attributed to the presence and migration of blue and fin whales. On average, whales are detected 43% of the time. Ships are heard 31%–85% of the time on the coastal receivers and 19%–87% of the time on the open-ocean receivers, depen.
Smarter Radiologists: A User-Friendly Algorithm Aiming to Improve the Accuracy and Readability of Intracranial Hemorrhage CT Detection

Intracranial hemorrhage (ICH) is a serious medical emergency in which blood accumulates within the skull and can be induced by a series of fatal diseases such as trauma, stroke, aneurysm, vascular malformations, hypertension, and coagulation disorders. 50% of patients with ICH die immediately, while others may survive if they receive timely treatments. Therefore, rapid and accurate identification and classification of ICH is essential for applying the correct treatment plan. However, the diagnosis and classification of ICH is a time-consuming task that relies heavily on the experience of radiologists. To relieve the burdens of radiologists and avoid potential human errors, in this study, we designed and proposed a deep learning model that can identify and classify ICH from CT images. In addition, our model is able to demonstrate the precise location in the brain where ICH occurs on CT images. Specifically, our method utilized two image preprocessing methods with an optimized ensemble model that achieved superior performance compared with six baseline models and other ensemble models. Furthermore, we designed a user-friendly app that allowed radiologists to easily access our model and analyze their clinical CT image data in real-time.
Feature‐selection ability of the decision‐tree algorithm and the impact of feature‐selection/extraction on decision‐tree results based on hyperspectral data

The decision‐tree (DT) algorithm is a very popular and efficient data‐mining technique. It is non‐parametric and computationally fast. Besides forming interpretable classification rules, it can select features on its own. In this article, the feature‐selection ability of DT and the impacts of feature‐selection/extraction on DT with different training sample sizes were studied by using AVIRIS hyperspcetral data. DT was compared with three other feature‐selection methods; the results indicated that DT was an unstable feature selector, and the number of features selected by DT was strongly related to the sample size. Trees derived with and without feature‐selection/extraction were compared. It was demonstrated that the impacts of feature selection on DT were shown mainly as a significant increase in the number of tree nodes (14.13–23.81%) and moderate increase in tree accuracy (3.5–4.8%). Feature extraction, like Non‐parametric Weighted Feature Extraction (NWFE) and Decision Boundary Feature Extraction (DBFE), could enhance tree accuracy more obviously (4.78–6.15%) and meanwhile a decrease in the number of tree nodes (6.89–16.81%). When the training sample size was small, feature‐selection/extraction could increase the accuracy more dramatically (6.90–15.66%) without increasing tree nodes.
Pixel Classification of Hyperspectral Images based on Spectral and Gabor Texture Features

Hyperspectral images consist of rich spectral information and spatial structure information that can be fully employed to implement the ground objects classification effectively. Due to the strong representation ability of Gabor filter to extract relevant features at different scales and directions, this paper proposes a Gabor filter group with 5 wavelengths and 4 directions that is exploited to the two-dimensional image of each band. To be specific, the 3×3 local neighborhood mean square error of the filtered image pixel is used as the texture feature of the pixel in a certain filtered image, and the texture features of all the filtered images in each band are combined together as the overall texture features of this pixel. XGBoost is leveraged as the classifier to carry out the classification experiments based on spectral feature, Gabor texture feature, and the combined features of the spectral and Gabor texture features, respectively. The experimental results show that our proposal can obtain the accuracy of , and based on only used spectral features, only used texture features, and the combined features, respectively. It is obvious that the classification accuracy can be significantly enhanced based on the combined features of the spectral and Gabor texture features.
Robust Influence Maximization Under Both Aleatory and Epistemic Uncertainty

Uncertainty is ubiquitous in almost every real-life optimization problem, which must be effectively managed to get a robust outcome. This is also true for the Influence Maximization (IM) problem, which entails locating a set of influential users within a social network. However, most of the existing IM approaches have overlooked the uncertain factors in finding the optimal solution, which often leads to subpar performance in reality. A few recent studies have considered only the epistemic uncertainty (i.e., arises from the imprecise data), while ignoring completely the aleatory uncertainty (i.e., arises from natural or physical variability). In this article, we propose a formulation and a novel algorithm for the Robust Influence Maximization (RIM) problem under both types of uncertainties. First, we develop a robust influence spread function under aleatory uncertainty that, in contrast to the existing IM theory, is no longer monotone and submodular. Thereafter, we expand our RIM formulation to incorporate epistemic uncertainty aiming to maximize the robust ratio between the selected worst-case solution and the best-case optimal solution, adopting a conservative approach. Furthermore, using a chance-constraint-based method, we investigated feasibility robustness by accounting for the uncertainties related to constraint functions. Finally, an Evolutionary Algorithm (named EA-RIM) is designed to solve the proposed formulation of the RIM problem. Experimental evaluation results on four empirical datasets show that our proposed formulation and algorithm are more effective in dealing with uncertainties and finding an optimal solution for the RIM problem.
Lessons Learned From Teaching Artificial Intelligence to Middle School Students

The AI4GA project is developing a nine-week elective course called Living and Working with Artificial Intelligence and piloting it in several Georgia middle schools. Since we aspire to educate all students about AI, the course addresses a wide range of student abilities, levels of academic preparedness, and prior computing experience, and leaves room for teachers to adapt the material to their own students' needs and interests. The course content is primarily focused on unplugged activities and online demonstration programs. We also provide small programming projects using AI tools as an option for teachers to incorporate. In this poster we describe lessons learned from initial pilot offerings by five teachers who taught 12 sections of the course totaling 299 students. We present evidence that middle school students can successfully engage with substantive technical content about Artificial Intelligence.
Deep Q-Learning based resource allocation and load balancing in a mobile edge system serving different types of user requests

Abstract With the expansion of the communicative and perceptual capabilities of mobile devices in recent years, the number of complex and high computational applications has also increased rendering traditional methods of traffic management and resource allocation quite insufficient. Recently, mobile edge computing (MEC) has emerged as a new viable solution to these problems. It can provide additional computing features at the edge of the network and allow alleviation of the resource limit of mobile devices while increasing the performance for critical applications especially in terms of latency. In this work, we addressed the issue of reducing the service delay by choosing the optimal path in the MEC network, which consists of multiple MEC servers that has different capabilities, applying network load balancing where multiple requests need to be handled simultaneously and routing selection based on a deep- Q network (DQN) algorithm. A novel traffic control and resource allocation method is proposed based on deep Q-learning (DQL) which allows reducing the end-to-end delay in cellular networks and in the mobile edge network. Real life traffic scenarios with various types of user requests are considered and a novel DQL resource allocation scheme which adaptively assigns computing and network resources is proposed. The algorithm optimizes traffic distribution between servers reducing the total service time and balancing the use of available resources under varying environmental conditions.
Study of NAD-interacting proteins highlights the extent of NAD regulatory roles in the cell and its potential as a therapeutic target

Abstract Nicotinamide adenine dinucleotide (NAD) levels are essential for the normal physiology of the cell and are strictly regulated to prevent pathological conditions. NAD functions as a coenzyme in redox reactions, as a substrate of regulatory proteins, and as a mediator of protein-protein interactions. The main objectives of this study were to identify the NAD-binding and NAD-interacting proteins, and to uncover novel proteins and functions that could be regulated by this metabolite. It was considered if cancer-associated proteins were potential therapeutic targets. Using multiple experimental databases, we defined datasets of proteins that directly interact with NAD – the NAD-binding proteins (NADBPs) dataset – and of proteins that interact with NADBPs – the NAD-protein–protein interactions (NAD-PPIs) dataset. Pathway enrichment analysis revealed that NADBPs participate in several metabolic pathways, while NAD-PPIs are mostly involved in signalling pathways. These include disease-related pathways, namely, three major neurodegenerative disorders: Alzheimer’s disease, Huntington’s disease, and Parkinson’s disease. Then, the complete human proteome was further analysed to select potential NADBPs. TRPC3 and isoforms of diacylglycerol (DAG) kinases, which are involved in calcium signalling, were identified as new NADBPs. Potential therapeutic targets that interact with NAD were identified, that have regulatory and signalling functions in cancer and neurodegenerative diseases.
Satellite Finds Highest Land Skin Temperatures on Earth

AMERICAN METEOROlOGICAl SOCIETy | 855 AFFILIATIONS: Mildrexler, Zhao, and running—Numerical Terradynamic Simulation Group, Dept. of Ecosystem and Conservation Sciences, University of Montana, Missoula, Montana CORRESPONDING AUTHOR: David Mildrexler, Numerical Terradynamic Simulation Group, Dept. of Ecosystem and Conservation Sciences, University of Montana, Missoula, MT 59812 E-mail: drexler@ntsg.umt. edu
Chromatin accessibility and pioneer factor FOXA1 restrict glucocorticoid receptor action in prostate cancer

Treatment of prostate cancer relies predominantly on the inhibition of androgen receptor (AR) signaling. Despite the initial effectiveness of the antiandrogen therapies, the cancer often develops resistance to the AR blockade. One mechanism of the resistance is glucocorticoid receptor (GR)-mediated replacement of AR function. Nevertheless, the mechanistic ways and means how the GR-mediated antiandrogen resistance occurs have remained elusive. Here, we have discovered several crucial features of GR action in prostate cancer cells through genome-wide techniques. We detected that the replacement of AR by GR in enzalutamide-exposed prostate cancer cells occurs almost exclusively at pre-accessible chromatin sites displaying FOXA1 occupancy. Counterintuitively to the classical pioneer factor model, silencing of FOXA1 potentiated the chromatin binding and transcriptional activity of GR. This was attributed to FOXA1-mediated repression of the NR3C1 (gene encoding GR) expression via the corepressor TLE3. Moreover, the small-molecule inhibition of coactivator p300’s enzymatic activity efficiently restricted GR-mediated gene regulation and cell proliferation. Overall, we identified chromatin pre-accessibility and FOXA1-mediated repression as important regulators of GR action in prostate cancer, pointing out new avenues to oppose steroid receptor-mediated antiandrogen resistance.
Measurement of the 0.3-8.5 MeV Galactic Gamma-Ray Spectrum from the Galactic Center Direction

The low-energy gamma-ray spectrum from the direction of the Galactic center is determined using data obtained with the SMM Gamma-Ray Spectrometer. It is found that the diffuse gamma-ray spectrum from the Galactic center region can be interpreted in a straightforward way as the sum of five components of a presented equation. The components include a hard power law dominating the continuum at high energies caused principally by cosmic ray electron bremsstrahlung radiation, two narrow lines due to Al-26 decay and positron annihilation, an excess continuum component below 0.511 MeV consistent with the annihilation of positrons by formation of Ps, and a soft power law at low energies which is consistent with an extrapolation upward in energy of known hard X-ray sources in the Galactic center region. 59 refs.
The pyruvate dehydrogenase complex regulates matrix protein phosphorylation and mitophagic selectivity

The mitophagic degradation of mitochondrial matrix proteins in S. cerevisiae was previously shown to be selective, reflecting a pre-engulfment sorting step within the mitochondrial network. This selectivity is regulated through phosphorylation of mitochondrial matrix proteins by the matrix kinases Pkp1 and Pkp2, which in turn appear to be regulated by the phosphatase Aup1/Ptc6. However, these same proteins also regulate the phosphorylation status and catalytic activity of the yeast pyruvate dehydrogenase complex, which is critical for mitochondrial metabolism. To understand the relationship between these two functions, we evaluated the role of the pyruvate dehydrogenase complex in mitophagic selectivity. Surprisingly, we identified a novel function of the complex in regulating mitophagic selectivity, which is independent of its enzymatic activity. Our data support a model in which the pyruvate dehydrogenase complex directly regulates the activity of its associated kinases and phosphatases. This regulatory interaction then determines the phosphorylation state of mitochondrial matrix proteins and their mitophagic fates.
Design and characterization of porous poly(glycerol-dodecanedioate) scaffolds for cartilage repair

Synthetic polymeric scaffolds play an important role in establishing the microenvironment for chondrocytes in engineered cartilage. A three-dimensional pore network allows cell accommodation and supports extracellular matrix (ECM) production by chondrocytes. Ligand coating and biomechanical properties of scaffolds guide regeneration of functional cartilage by mediating cell attachment and establishing the local strain environment. Poly(glycerol-dodecanedioate) (PGD) is a novel biodegradable elastomer with nonlinear-elastic properties similar to native cartilage. However, its harsh curing environments limit the feasibility of common strategies for pore creation in polymeric scaffolds. Herein, we developed porous PGD (pPGD) scaffolds with tailorable pore structures using an inverse molding method and evaluated the range of scaffold structural parameters achievable and their subsequent mechanical properties. The influence of coating PGD with various ECM ligands on the cell shape, metabolic activity, and ECM production of human articular chondrocytes (hACs) was evaluated. pPGD scaffolds were created with pore sizes ranging from 250 – 1000 μm, resulting in 20 – 50% porosity. The morphology and metabolic activity of hACs on PGD were regulated by the type of ligand coating used. When compared to tissue culture plastic, PGD enhanced ECM production in monolayer cultures. Finite element analysis showed that the tensile strains that developed on the pores’ surfaces were at levels shown to be anabolic for hACs. The predicted strain profile varied with pore size and porosity under load, demonstrating that the pore structural parameters could be tuned to optimize cellular-level strains. These results suggest that pPGD scaffolds have the potential to guide cartilage regeneration. Statement of Significance Previous studies have established the importance of designing pore geometry and surface properties in engineered cartilage tissue constructs. This work reports the development and assessment of pPGD scaffolds with tunable pore and surface parameters for cartilage regeneration. The cellular-level strain that cells may experience inside the pores was influenced by the scaffolds’ pore geometry. Ligand coating on PGD balanced out the less ideal properties of the material itself and regulated the shape, attachment, metabolic activity, and ECM production of hACs during in vitro culture. These findings highlight how intelligent design of scaffold parameters can optimize chondrocyte function during 3D culture by tuning ligand presentation and cellular-level strain profiles.