Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploiting Reflection Direction Variation for Phase Control in Multiple Simultaneous IRS Links

An intelligent reflecting surface (IRS) is a device that can reflect radio waves in any direction by setting the phase shift of the reflective elements. The IRS is expected to both solve the problems of high-frequency band communication, such as vulnerability to obstacles and large distance attenuation, and realize ultra-multiple connections in the high-frequency band. However, the reflective elements of the IRS can only be controlled by time division, which means that the IRS can support only one user per time slot, whereas frequency resource division communication can be used by base stations to support multiple users. Therefore, in this study, we investigated a method that enables an IRS to support the communication of multiple users simultaneously. The proposed method determines the frequency allocated to each user and expands the reflected beamwidth of the IRS to maximize the effect of the frequency shift. This approach takes advantage of the misalignment in the reflection direction for each frequency that occurs when multiple frequencies with the same phase shift are incident on the IRS. Simulation experiments show the effect of allocation considering the misalignment in the reflection directions and the effect of expanding the beamwidth in a certain environment at 28 GHz. The results show the potential of this approach in IRS-based multi-user communication systems.
An Autoregressive Conversational Dynamics Model for Dialogue Systems

Conversational partners adapt their speech to one another in a phenomenon called entrainment . While entrainment behaviors are associated with a variety of positive conversational outcomes, they are rarely implemented in dialogue systems due to their poorly understood mechanics. Conversational dynamics models could discover entrainment behavior in a dialogue corpus, but to date they have not been designed for or evaluated in dialogue systems. In this paper, we propose an autoregressive model specifically for use in a dialogue system. We evaluate its ability to predict features for upcoming conversational turns, and show it outperforms several baseline models. Additionally, we analyze its attention mechanism to explain which turns it finds useful for predicting upcoming speech features. Finally, we discuss its potential for future deployment in a live dialogue system.
Design and Transformation Control of Triangulated Origami Tessellation: A Network Perspective

Origami is known as a traditional art of paper folding. It has attracted extensive attention due to its self-folding mechanism, shape-morphing capability, and deployable structures. This article develops network-based methods for designing and controlling a three-dimensional (3D) triangulated origami tessellation to approximate multiple surfaces. The desired surfaces are represented by sets of discrete nodes and the origami tessellation to be designed is composed of triangles. Then, the tessellation design problem is formulated as an optimization problem of minimizing the distance between the origami triangle vertices and the discrete nodes subject to developability and rigid-foldability constraints. Solving the resulting optimization problem leads to an origami tessellation with folding states associated with each target surface. To achieve transformation between different shapes, we first leverage graph rigidity theory to define every 3D origami shape uniquely up to translations and rotations. Next, in order to minimize the control efforts, the shape transformation control problem is formulated as an optimal control problem subject to the derived rigidity conditions, whose feasibility is guaranteed by transformability of the origami and controllability of dynamic vertices. Finally, simulation examples for surface approximation are provided to verify the effectiveness of the network-based design and control methods.
Appearance Label Balanced Triplet Loss for Multi-modal Aerial View Object Classification

Automatic target recognition (ATR) using image data is an important computer vision task with widespread applications in remote sensing for surveillance, object tracking, urban planning, agriculture, and more. Although there have been continuous advancements in this task, there is still significant room for further advancements, particularly with aerial images. This work extracts rich information from multimodal synthetic aperture radar (SAR) and electro-optical (EO) aerial images to perform object classification.Compared to EO images, the advantages of SAR images are that they can be captured at night and in any weather condition. Compared to EO images, the disadvantage of SAR images is that they are noisy. Overcoming the noise inherent to SAR images is a challenging, but worthwhile, task because of the additional information SAR images provide the model.This work proposes a training strategy that involves the creation of appearance labels to generate triplet pairs for training the network with both triplet loss and cross-entropy loss. During the development phase of the 2023 Perception Beyond Visual Spectrum (PBVS) Multi-modal Aerial Image Object Classification (MAVOC) challenge, our ResNet-34 model achieved a top-1 accuracy of 64.29% for Track 1 and our ensemble learning model achieved a top-1 accuracy 75.84% for Track 2. These values are 542% and 247% higher than the baseline values. Overall, this work ranked 3rd in both Track 1 and Track 2.
A Comprehensive Benchmark of Transcriptomic Biomarkers for Immune Checkpoint Blockades

Simple Summary Immune checkpoint blockades (ICBs) therapy has produced durable clinical responses in many cancer types, but only a fraction of patients can benefit from ICB treatment. Previous studies have reported multiple transcriptomic biomarkers to predict ICB responses and improve treatment precision in various cancer types. However, a timely and unbiased assessment of these biomarkers has yet to be conducted due to the lack of large-scale uniformly curated ICB-treated datasets. To address the needs, we developed ICB-Portal, a comprehensive resource about ICB including RNA-seq data of 29 datasets from public sources and standardized metadata of each study through a uniform pre-processing, 48 biomarker scores associated with ICB response, results of a systematic benchmark analysis evaluating the efficacy, and generalization ability for each biomarker in various scenarios such as different cancer types, anti-bodies, biopsy time, and combinatory treatments with other drugs by a standardized bioinformatics workflow and an online benchmark platform. Abstract Immune checkpoint blockades (ICBs) have revolutionized cancer therapy by inducing durable clinical responses, but only a small percentage of patients can benefit from ICB treatments. Many studies have established various biomarkers to predict ICB responses. However, different biomarkers were found with diverse performances in practice, and a timely and unbiased assessment has yet to be conducted due to the complexity of ICB-related studies and trials. In this study, we manually curated 29 published datasets with matched transcriptome and clinical data from more than 1400 patients, and uniformly preprocessed these datasets for further analyses. In addition, we collected 39 sets of transcriptomic biomarkers, and based on the nature of the corresponding computational methods, we categorized them into the gene-set-like group (with the self-contained design and the competitive design, respectively) and the deconvolution-like group. Next, we investigated the correlations and patterns of these biomarkers and utilized a standardized workflow to systematically evaluate their performance in predicting ICB responses and survival statuses across different datasets, cancer types, antibodies, biopsy times, and combinatory treatments. In our benchmark, most biomarkers showed poor performance in terms of stability and robustness across different datasets. Two scores (TIDE and CYT) had a competitive performance for ICB response prediction, and two others (PASS-ON and EIGS_ssGSEA) showed the best association with clinical outcome. Finally, we developed ICB-Portal to host the datasets, biomarkers, and benchmark results and to implement the computational methods for researchers to test their custom biomarkers. Our work provided valuable resources and a one-stop solution to facilitate ICB-related research.
Visible Light Communications Using Commercially Available Fluorescent Fibers as Optical Antennas

The use of fluorescent optical antennas in visible light communications (VLC) can enhance the transmission performance due to their multiple functions of optical filtering, light concentration and collecting light from a wide range of incident angles. One convenient way to create an optical antenna is by using commercially available fluorescent fibers. In this paper, we introduce a new antenna structure made of two bent fluorescent fibers and study its performance in a white light-emitting diode (LED)-based VLC system. We show that, since the fluorophore doped into the selected fiber only absorbs the blue light emitted from the gallium nitride (GaN) LED rather than the yellow light converted from the phosphor coating, it can result in a high modulation bandwidth. Also, by placing two bent fibers in an intersecting way, the receiver becomes omnidirectional. More importantly, our experimental results show that, when the illuminance at the receiver is a typical indoor lighting level, the receiver incorporated with this new fluorescent antenna can result in a much higher transmission data rate compared to the case when no antenna is used.
Comparisons of Loss Reduction Techniques Based on Pulsewidth Modulation and Model Predictive Control for Three-Phase Voltage Source Inverters

Due to the lack of comparative studies between discontinuous pulse-width modulation and model predictive control methods for reducing switching losses in two-level three-phase voltage source inverter, a comparative analysis of a generalized discontinuous pulse-width modulation and two model predictive control approaches for reducing switching losses is studied in this paper. Both generalized discontinuous pulse-width modulation and two model predictive control approaches are described and conducted in the simulation and experiment. The output performance is obtained by these methods after conducting in various conditions, including switching frequency, output power, and load conditions. It is validated that the generalized discontinuous pulse-width modulation control scheme achieves a better control performance at steady-state, while two model predictive control schemes have better transient-state performance with a superior dynamic. Additionally, the generalized discontinuous pulse-width modulation approach achieves better reducing switching losses performance and has slightly higher efficiency than that of two model predictive control approaches.
Open RAN for detection of a jamming attack in a 5G network

One of the essential security threats for 5G networks is jamming. It is relatively easy to be performed utilizing cheap and publically available devices. Open Radio Access Network (O-RAN) architecture is very suitable for detecting such events thanks to the openness of interfaces and the ability to analyze wireless traffic metrics and exchange control messages in a RAN Intelligent Controller (RIC) using some dedicated xApp or rApp. This paper presents a statistical method for downlink jamming detection utilizing the link quality reports provided by User Equipments (UEs). A vision of its implementation in O-RAN is presented altogether with performance quality metrics obtained via simulations.
The effect of curcumin on hypoxia in the tumour microenvironment as a regulatory factor in cancer

Hypoxia can lead to different responses from cancer cells, including cell death or survival, partially depending on how long it is exposed. Patients with cancer and under hypoxic tumour conditions have a poorer prognosis and are at greater risk of metastasis. Physiological response to low oxygen levels is controlled by hypoxia-inducible factor (HIF)-1. Curcumin, the major component of the rhizomes of Curcuma longa L. , reduces HIF-1 levels and function, inhibiting the production of vascular endothelial growth factor (VEGF). In addition, curcumin efficiently inhibits the angiogenesis of vascular endothelial cells triggered by hypoxia. One of the most compelling features that drive continued interest in curcumin is the molecules’ modulation of initiation, promotion, and progression stages of cancer while concomitantly acting as a radiosensitizer and chemosensitizer for tumours. In this review, we discuss the role of curcumin in modulating hypoxia and investigate the mechanisms and regulatory factors of hypoxia in tumour tissues.
Lightning‐Ignited Wildfires in the Western United States: Ignition Precipitation and Associated Environmental Conditions

Cloud‐to‐ground lightning with minimal rainfall (“dry” lightning) is a major wildfire ignition source in the western United States (WUS). Although dry lightning is commonly defined as occurring with <2.5 mm of daily‐accumulated precipitation, a rigorous quantification of precipitation amounts concurrent with lightning‐ignited wildfires (LIWs) is lacking. We combine wildfire, lightning and precipitation data sets to quantify these ignition precipitation amounts across ecoprovinces of the WUS. The median precipitation for all LIWs is 2.8 mm but varies with vegetation and fire characteristics. “Holdover” fires not detected until 2–5 days following ignition occur with significantly higher precipitation (5.1 mm) compared to fires detected promptly after ignition (2.5 mm), and with cooler and wetter environmental conditions. Further, there is substantial variation in precipitation associated with promptly‐detected (1.7–4.6 mm) and holdover (3.0–7.7 mm) fires across ecoprovinces. Consequently, the widely‐used 2.5 mm threshold does not fully capture lightning ignition risk and incorporating ecoprovince‐specific precipitation amounts would better inform WUS wildfire prediction and management.
Predicting Electric Vehicle Charging Stations Occupancy: A Federated Deep Learning Framework

Electric vehicles (EVs) have long been recognized as a solution to the shortage of fossil fuels and the environmental problems associated with increasing CO2 emissions. However, charging an electric vehicle can take significant time at certain charging stations. Additionally, the limited deployment of charging stations is a significant barrier to the widespread adoption of electric mobility (e-mobility). In fact, many drivers struggle to locate a convenient charging station before their vehicle’s battery runs out. This study introduces a novel approach to addressing the issue of congestion at public charging stations and reducing the amount of time drivers spend waiting in line by predicting their occupancy. Previous research has relied on traditional Deep Learning (DL) techniques for prediction, which require centralized data collection. Nevertheless, each Charging Station Operator (CSO) holds sensitive data about its charging stations and users that cannot be shared with external parties. To address these privacy concerns, we propose a Federated Deep Learning approach where each CSO trains a DL model locally and then sends the model updates (or parameters) to a server for aggregation. Experiments on a real-world dataset demonstrate that predicting occupancy using the Federated Deep Learning approach achieves promising results (86,21% of accuracy and 91,49% of f1-score ), guarantees privacy, minimizes data transfer costs over the network, and allows individual CSOs to benefit from the rich datasets of others without sharing their sensitive data.
Algae Blooms on the Greenland Ice Sheet Detected Through Solar-Induced Fluorescence

Glacial algae blooms on the Greenland ice sheet darken the surface albedo, enhancing surface melt. Sentinel-3 data was the first to highlight the extent of these blooms, which are expected to become larger as climate changes. Here, we propose a novel use of solar-induced chlorophyll fluorescence (SIF) data from TROPOspheric monitoring instrument (TROPOMI) to confirm the Sentinel-based maps and extend the data to daily levels to understand year-to-year variations in algae blooms. We combine the SIF data with a random walker algorithm to identify algae-covered areas. The SIF-based maps are found to be highly correlated with the Sentinel-based ones. The extent and the timing of the algae bloom are consistent between 2018 and 2020. This work opens the door to a new, complementary method for the monitoring of algae blooms on ice sheets and improving our understanding of their impact on glacier melt rate.
Low and Ultra-Low HER2 in Human Breast Cancer: An Effort to Define New Neoplastic Subtypes

HER2-low and ultra-low breast cancer (BC) have been recently proposed as new subcategories of HER2 BC, supporting a re-consideration of immunohistochemical negative scores of 0, 1+ and the 2+/in situ hybridization (ISH) negative phenotype. In the present review, we outline the criteria needed to exactly distinguish HER2-low and ultra-low BC. Recent clinical trials have demonstrated significant clinical benefits of novel HER2 directing antibody–drug conjugates (ADCs) in treating these groups of tumors. In particular, trastuzumab-deruxtecan (T-Dxd), a HER2-directing ADC, has been recently approved by the US Food and Drug Administration as the first targeted therapy to treat HER2-low BC. Furthermore, ongoing trials, such as the DESTINY-Breast06 trial, are currently evaluating ADCs in patients with HER2-ultra low BC. Finally, we hope that new guidelines may help to codify HER2-low and ultra-low BC, increasing our knowledge of tumor biology and improving a targetable new therapeutical treatment.
Solar Cycles Forced Southern Westerly Wind Migrations During the Holocene

Despite small direct changes to radiative forcing, solar sunspot cycles are observed in climate records because of climate system amplification that primarily affects wind and precipitation belts. We present a proxy record resolving the dominant sub‐millennial periodicities across the entire Holocene in the Southern Westerly Winds (SWW), whose migrations are linked to ocean‐atmosphere heat and carbon exchange. We use X‐ray fluorescence core scanning to examine a rapidly accumulating sediment record (6 m/kyr) recovered from the Chilean margin, yielding unprecedented <2‐year resolution for the Holocene. We show that variations in terrigenous inputs to the site are linked to precipitation, which is controlled by SWW latitudinal migrations. Superimposed on a long‐term decreasing trend throughout the Holocene, we detect significant centennial cycles in the terrestrial input consistent with solar periodicities. We then propose a mechanism by which southward (northward) SWW movement in response to increasing (decreasing) total solar irradiance cools (warms) Antarctic temperatures.
Joint Learning Feature and Model Adaptation for Unsupervised Acoustic Modelling of Child Speech

Due to the high acoustic variability of child speech and the lack of publicly available datasets, acoustic modeling for child speech is challenging. In this work, we address these challenges by leveraging the large amounts of resources for adult speech (well-trained acoustic models and transcribed speech dataset) and proposing a joint acoustic feature and model adaptation framework to minimize acoustic mismatch between adult and child speech. Empirical results on three tasks of speech recognition, pronunciation assessment, and fluency assessment show that our proposed approach consistently outperforms competitive baselines, achieving up to 31.18% phone error reduction on speech recognition and around 7% gains on speech evaluation tasks.
An Immunocompetent Adult Male Presented with a Liver Abscess Caused by the Mycobacterium tuberculosis: A Case Report from India

A liver abscess is a collection of purulent fluid in the liver parenchyma caused by a variety of etiological organisms such as bacteria, protozoa, and in rare occasions fungi. Mycobacterium tuberculosis (MTB) is a frequent and widespread infection in underdeveloped nations such as India, which can manifest in many ways. Tuberculosis mostly affects the lungs, although it can also affect any organ in the body. We are reporting a case of liver abscess caused by MTB infection in order to raise general awareness among physicians about the importance of suspecting and ruling out tuberculosis as a cause of liver abscess. To the best of our knowledge, there have been very few such cases reported from India/the rest of the world.
Comparing Hand-Crafted Features to Spectrograms for Autism Severity Estimation

In this work, we compared two different input approaches to estimate autism severity using speech signals. We analyzed 127 audio recordings of young children obtained during the Autism Diagnostic Observation Schedule 2nd edition (ADOS-2) administration. Two different sets of features were extracted from each recording: 1) hand-crafted features, which included acoustic and prosodic features, and 2) log-mel spectrograms, which give the time-frequency representation. We examined two different Convolutional Neural Network (CNN) architectures for each of the two inputs and compared the autism severity estimation performance. We showed that the hand-crafted features yielded lower prediction error (normalized RMSE) in most examined configurations than the log-mel spectrograms. Moreover, fusing the estimated autism severity scores of the two feature extraction methods yielded the best results, where both architectures exhibited similar performance (Pearson R=0.66, normalized RMSE=0.24).
A challenging diagnosis of plasmablastic lymphoma: importance of integrating morpholgy immunohistochemistry and flow cytometry findings (case report)

Plasmablastic lymphoma (PBL) is a rare clinicopathological entity that still raises many diagnostic and management difficulties, particularly due to the overlap between plasmablastic lymphomas and myeloma features. We report a clinical presentation of PBL affecting bone marrow in a 43-year-old patient who was admitted for B symptoms, hepatosplenomegaly, and bicytopenia investigation. Based on these findings, acute leukemia was suspected. Bone marrow morphology immunohistochemistry and flow cytometry contributed to establishing the diagnosis of medullary PBL. The patient deteriorated and died due to septic shock. This pathology requires collaboration between clinicians, pathologists, and biologists to confirm the diagnosis early. Nevertheless, a delayed diagnosis may contribute to worsening the prognosis particularly due to advanced stage consultation. Our reported case illustrates a rare clinical presentation affecting bone marrow. In our context, a confrontation between flow cytometry and immunohistochemistry was of interest as it helped to detect the immunological features of this neoplasm.