Miracle-dz/UniFAR-FTUs huggingface dataset:
Lymphoid EVA1 Expression Is Required for DN1-DN3 Thymocytes Transition

Background Thymus organogenesis and T lymphocyte development are accomplished together during fetal life. Proper development and maintenance of thymus architecture depend on signals generated by a sustained crosstalk between developing thymocytes and stromal elements. Any maturation impairment occurring in either cellular component leads to an aberrant thymic development. Gene expression occurring during T lymphocyte differentiation must be coordinated in a spatio-temporal fashion; one way in which this is achieved is through the regulation by cell-cell adhesion and interactions. Principal Findings We examined the role played by Epithelial V-like Antigen 1 (EVA1), an Ig adhesion molecule expressed on thymus epithelial cells (TEC) and immature thymocytes, in T cell development by employing RNA interference in vitro and in vivo models. Fetal liver derived haematopoietic progenitors depleted of Eva1, displayed a delayed DN1-DN3 transition and failed to generate CD4CD8 double positive T cells in OP9-DL1 coculture system. In addition, we could observe a coordinated Eva1 up-regulation in stromal and haematopoietic cells in coculture control experiments, suggesting a possible EVA1 involvement in TEC-haematopoietic cells crosstalk mechanisms. Similarly, Rag2-γc double knock out mice, transplanted with Eva1 depleted haematopoietic progenitors displayed a 10-fold reduction in thymus reconstitution and a time delayed thymocytes maturation compared to controls. Conclusions Our findings show that modulation of Eva1 expression in thymocytes is crucial for lymphocyte physiological developmental progression and stromal differentiation.
Summary Report

Due to the immense growth in the number of mobile base stations throughout the world, there is a growing concern for human exposure to electromagnetic fields. Standards have been implemented in order to protect humans from the detrimental effects of electromagnetic radiation hazard exposure and the Australian Radiation Protection Standard, which is implemented throughout Australian industry, has been used as the authority throughout this project. This project has created an electromagnetic radiation hazard monitor which is to be used to survey the electric field strengths within building 16 of the Australian Defence Force Academy at frequencies of 850 MHz to 900 MHz. This monitor has been used to observe the electric fields in multiple locations, confirming that there is no concern for personal safety regarding exposure to electromagnetic radiation hazards.
Life time—circadian clocks, mitochondria and metabolism

A functional circadian clock has long been considered a selective advantage. Accumulating evidence shows that the clock coordinates a variety of physiological processes in order to schedule them to the optimal time of day and thus to synchronize metabolism to changes in external conditions. In mitochondria, both metabolic and cellular defense mechanisms are carefully regulated. Abnormal clock function, might influence mitochondrial function, resulting in decreased fitness of an organism.
Particulate trace metal dynamics in response to increased CO2 and iron availability in a coastal mesocosm experiment

Abstract. Rising concentrations of atmospheric carbon dioxide are causing ocean acidification and will influence marine processes and trace metal biogeochemistry. In June 2012, in the Raunefjord (Bergen, Norway), we performed a mesocosm experiment, comprised of a fully factorial design of ambient and elevated pCO2 and/or an addition of the siderophore desferrioxamine B (DFB). In addition, the macronutrient concentrations were manipulated to enhance a bloom of the coccolithophore Emiliania huxleyi. We report the changes in particulate trace metal concentrations during this experiment. Our results show that particulate Ti and Fe were dominated by lithogenic material, while particulate Cu, Co, Mn, Zn, Mo and Cd had a strong biogenic component. Furthermore, significant correlations were found between particulate concentrations of Cu, Co, Zn, Cd, Mn, Mo and P in seawater and phytoplankton biomass (µgC L−1), supporting a significant influence of the bloom in the distribution of these particulate elements. The concentrations of these biogenic metals in the E. huxleyi bloom were ranked as follows: Zn < Cu ≈ Mn < Mo < Co < Cd. Changes in CO2 affected total particulate concentrations and biogenic metal ratios (Me : P) for some metals, while the addition of DFB only significantly affected the concentrations of some particulate metals (mol L−1). Variations in CO2 had the most clear and significant effect on particulate Fe concentrations, decreasing its concentration under high CO2. Indeed, high CO2 and/or DFB promoted the dissolution of particulate Fe, and the presence of this siderophore helped in maintaining high dissolved Fe. This shift between particulate and dissolved Fe concentrations in the presence of DFB, promoted a massive bloom of E. huxleyi in the treatments with ambient CO2. Furthermore, high CO2 decreased the Me : P ratios of Co, Zn and Mn while increasing the Cu : P ratios. These findings support theoretical predictions that the molar ratios of metal to phosphorous (Me : P ratios) of metals whose seawater dissolved speciation is dominated by free ions (e.g., Co, Zn and Mn) will likely decrease or stay constant under ocean acidification. In contrast, high CO2 is predicted to shift the speciation of dissolved metals associated with carbonates such as Cu, increasing their bioavailability and resulting in higher Me : P ratios.
A Scalable and Parallelizable Digital Twin Framework for Sustainable Sim2Real Transition of Multi-Agent Reinforcement Learning Systems

This work presents a sustainable multi-agent deep reinforcement learning framework capable of selectively scaling parallelized training workloads on-demand, and transferring the trained policies from simulation to reality using minimal hardware resources. We introduce AutoDRIVE Ecosystem as an enabling digital twin framework to train, deploy, and transfer cooperative as well as competitive multi-agent reinforcement learning policies from simulation to reality. Particularly, we first investigate an intersection traversal problem of 4 cooperative vehicles (Nigel) that share limited state information in single as well as multi-agent learning settings using a common policy approach. We then investigate an adversarial autonomous racing problem of 2 vehicles (F1TENTH) using an individual policy approach. In either set of experiments, a decentralized learning architecture was adopted, which allowed robust training and testing of the policies in stochastic environments. The agents were provided with realistically sparse observation spaces, and were restricted to sample control actions that implicitly satisfied the imposed kinodynamic and safety constraints. The experimental results for both problem statements are reported in terms of quantitative metrics and qualitative remarks for training as well as deployment phases. We also discuss agent and environment parallelization techniques adopted to efficiently accelerate MARL training, while analyzing their computational performance. Finally, we demonstrate a resource-aware transition of the trained policies from simulation to reality using the proposed digital twin framework.
SF-MMCN: A Low Power Re-configurable Server Flow Convolution Neural Network Accelerator

Convolution Neural Network (CNN) accelerators have been developed rapidly in recent studies. There are lots of CNN accelerators equipped with a variety of function and algorithm which results in low power and high-speed performances. However, the scale of a PE array in traditional CNN accelerators is too big, which costs the most energy consumption while conducting multiply and accumulation (MAC) computations. The other issue is that due to the advance of CNN models, there are enormous models consist of parallel structures such as residual block in Residual Network (ResNet). The appearance of parallel structure in CNN models gives a challenge to the design of CNN accelerators owing to impacts on both operation and area efficiency. This study proposed SF-MMCN structure. The scale of PE array in proposed designs is reduced by pipeline technique in a PE. Proposed SF structure successfully make proposed SF-MMCN operate in high efficiency when facing parallel structures in CNN models. Proposed design is implemented with TSMC 90nm technology on VGG-16 and ResNet-18 environments. The performance of proposed design achieves 76% energy saving, 55% area saving and increases operation and are efficiency 9.25 times and 4.92 times respectively.
Order-One Rolling Shutter Cameras

Rolling shutter (RS) cameras dominate consumer and smartphone markets. Several methods for computing the absolute pose of RS cameras have appeared in the last 20 years, but the relative pose problem has not been fully solved yet. We provide a unified theory for the important class of order-one rolling shutter (RS$_1$) cameras. These cameras generalize the perspective projection to RS cameras, projecting a generic space point to exactly one image point via a rational map. We introduce a new back-projection RS camera model, characterize RS$_1$ cameras, construct explicit parameterizations of such cameras, and determine the image of a space line. We classify all minimal problems for solving the relative camera pose problem with linear RS$_1$ cameras and discover new practical cases. Finally, we show how the theory can be used to explain RS models previously used for absolute pose computation.
QuantumLeak: Stealing Quantum Neural Networks from Cloud-based NISQ Machines

Variational quantum circuits (VQCs) have become a powerful tool for implementing Quantum Neural Networks (QNNs), addressing a wide range of complex problems. Well-trained VQCs serve as valuable intellectual assets hosted on cloud-based Noisy Intermediate Scale Quantum (NISQ) computers, making them susceptible to malicious VQC stealing attacks. However, traditional model extraction techniques designed for classical machine learning models encounter challenges when applied to NISQ computers due to significant noise in current devices. In this paper, we introduce QuantumLeak, an effective and accurate QNN model extraction technique from cloud-based NISQ machines. Compared to existing classical model stealing techniques, QuantumLeak improves local VQC accuracy by 4.99\%$\sim$7.35\% across diverse datasets and VQC architectures.
Environmental cues and genes involved in establishment of the superinfective Pf4 phage of Pseudomonas aeruginosa

Biofilm development in Pseudomonas aeruginosa is in part dependent on a filamentous phage, Pf4, which contributes to biofilm maturation, cell death, dispersal and variant formation, e.g., small colony variants (SCVs). These biofilm phenotypes correlate with the conversion of the Pf4 phage into a superinfection (SI) variant that reinfects and kills the prophage carrying host, in contrast to other filamentous phage that normally replicate without killing their host. Here we have investigated the physiological cues and genes that may be responsible for this conversion. Flow through biofilms typically developed SI phage approximately days 4 or 5 of development and corresponded with dispersal. Starvation for carbon or nitrogen did not lead to the development of SI phage. In contrast, exposure of the biofilm to nitric oxide, H2O2 or the DNA damaging agent, mitomycin C, showed a trend of increased numbers of SI phage, suggesting that reactive oxygen or nitrogen species (RONS) played a role in the formation of SI phage. In support of this, mutation of oxyR, the major oxidative stress regulator in P. aeruginosa, resulted in higher level of and earlier superinfection compared to the wild-type (WT). Similarly, inactivation of mutS, a DNA mismatch repair gene, resulted in the early appearance of the SI phage and this was four log higher than the WT. In contrast, loss of recA, which is important for DNA repair and the SOS response, also resulted in a delayed and decreased production of SI phage. Treatments or mutations that increased superinfection also correlated with an increase in the production of morphotypic variants. The results suggest that the accumulation of RONS by the biofilm may result in DNA lesions in the Pf4 phage, leading to the formation of SI phage, which subsequently selects for morphotypic variants, such as SCVs.
Stimulation of ethylene production in bean leaf discs by the pseudomonad phytotoxin coronatine.

Coronatine is a toxin produced by Pseudomonas syringae pv. glycinea which induces the same chlorotic response in bean leaves as does infection by the bacterial pathogen. Although the structure of coronatine is known, the biological mode of action is not. One possible clue to its activity is the ethyl-substituted cyclopropane side chain of the molecule. This part structure (1-amino-2-ethycyclopropane-1-carboxylic acid or AEC) is an analog of the ethylene precursor 1-aminocyclopropane-1-carboxylic acid (ACC). When coronatine was applied to bean leaf discs in solution, or to intact leaves through prick application, a substantial stimulation of ethylene production was measured. This stimulation was concomitant with an increase in ACC content of the tissue, and occurred under the same conditions as did the chlorotic response to the toxin. The stimulation of ethylene production was inhibited by aminoethoxyvinylglycine, an inhibitor of ACC synthesis. These results, along with those of experiments using l-[U-(14)C]methionine, indicated that the stimulation involved de novo production of ethylene via the methionine pathway.The whole, unhydrolyzed coronatine molecule is probably necessary to elicit both the ethylene and chlorosis responses since neither hydrolysis product (coronafacic acid and coronamic acid AEC]) is effective alone. A naturally occurring analog of coronatine, coronafacoylvaline, also stimulated ethylene production and caused chlorosis. However, the unrelated pseudomonad phytotoxin phaseolotoxin, which also causes chlorosis, did not stimulate ethylene production. Ethylene thus may have a specific role in the coronatine toxic syndrome.
Expansion of strongly interacting dipolar bosons in 1D optical lattices

We numerically study the expansion dynamics of initially localized dipolar bosons in a homogeneous 1D optical lattice for different initial states. Comparison is made to interacting bosons with contact interaction. For shallow lattices the expansion is unimodal and ballistic, while strong lattices suppress tunneling. However for intermediate lattice depths a strong interplay between dipolar interaction and lattice depth occurs. The expansion is found to be bimodal, the central cloud expansion can be distinguished from the outer halo structure. In the regime of strongly interactions dipolar bosons exhibit two time scales, with an initial diffusion and then arrested transport in the long time; while strongly interacting bosons in the fermionized limit exhibit ballistic expansion. Our study highlights how different lattice depths and initial states can be manipulated to control tunneling dynamics.
Neural-Kernel Conditional Mean Embeddings

Kernel conditional mean embeddings (CMEs) offer a powerful framework for representing conditional distribution, but they often face scalability and expressiveness challenges. In this work, we propose a new method that effectively combines the strengths of deep learning with CMEs in order to address these challenges. Specifically, our approach leverages the end-to-end neural network (NN) optimization framework using a kernel-based objective. This design circumvents the computationally expensive Gram matrix inversion required by current CME methods. To further enhance performance, we provide efficient strategies to optimize the remaining kernel hyperparameters. In conditional density estimation tasks, our NN-CME hybrid achieves competitive performance and often surpasses existing deep learning-based methods. Lastly, we showcase its remarkable versatility by seamlessly integrating it into reinforcement learning (RL) contexts. Building on Q-learning, our approach naturally leads to a new variant of distributional RL methods, which demonstrates consistent effectiveness across different environments.
Expanding the Resolution Boundary of Outcome-Based Imperfect-Recall Abstraction in Games with Ordered Signals

In the development of advanced Texas Hold'em AI systems, abstraction technology has garnered widespread attention due to its significant effect in simplifying game complexity. This study adopts a more specific model, the games of ordered signal, to describe Texas Hold'em-style games and optimizes this model to streamline its mathematical representation and broaden its applicability. By transitioning from a broad imperfect information game model to a game with ordered signals model, we have separated the previously intertwined infoset abstraction and action abstraction into independent signal abstraction and action abstraction. Importantly, this signal abstraction provides a mathematical framework for the hand abstraction task, which is emphatically discussed in this paper. Additionally, a novel common refinement principle is introduced, revealing the limit performance of hand abstraction algorithms. We introduce potential outcome isomorphism (POI) and pinpoint that it suffers from the issue of excessive abstraction. Futher, We demonstrate that POI serves as a common refinement for leading outcome-based hand abstraction algorithms, such as E[HS] and PA\&PAEMD. Consequently, excessive abstraction also inherently affects these algorithms, leading to suboptimal performance. Our investigation reveals the omission of historical data as a primary contributor to excessive abstraction. To remedy this, we propose the K-Recall Outcome Isomorphism (KROI) to incorporate the missing information. Compared with POI, KROI more accurately mirrors lossless isomorphism (LI), the ground truth, offering enhanced signal abstraction resolution. Experimental results in the Numeral211 Hold'em indicate that strategies developed through KROI approximate the exploitability of those developed through LI more closely than those trained through POI.
An Incremental Floorplanning Algorithm with Less Flexibility First Principles

To support incremental changes on the existing floorplan derived from some changes in the high level synthesis is becoming an increasingly important problem in order to cope with the complexity of the merging of VLSI design phases and the necessary of alternation between synthesis and physical design. In this paper, we present an incremental floorplanning algorithm with less flexibility first (LFF) principles (LFF_IFP algorithm). LFF principle is a deterministic algorithm through human's accumulated experience. In this paper, the horizontal and vertical constraint graphs are built up based on the packing of the initial floorplanning result within linear time, and the accumulated slack distances among blocks are computed. The flexibility of blocks to be inserted to the initial floorplan ('incremental block' is also called here for short) is computed and every slack space belongs to the block in the initial floorplan also has different degree of flexibility for a given incremental block. The experimental results demonstrate that the algorithm is effective and promising in building block layout application
JUMBO: Fully Asynchronous BFT Consensus Made Truly Scalable

Recent progresses in asynchronous Byzantine fault-tolerant (BFT) consensus, e.g. Dumbo-NG (CCS' 22) and Tusk (EuroSys' 22), show promising performance through decoupling transaction dissemination and block agreement. However, when executed with a larger number $n$ of nodes, like several hundreds, they would suffer from significant degradation in performance. Their dominating scalability bottleneck is the huge authenticator complexity: each node has to multicast $\bigO(n)$ quorum certificates (QCs) and subsequently verify them for each block. This paper systematically investigates and resolves the above scalability issue. We first propose a signature-free asynchronous BFT consensus FIN-NG that adapts a recent signature-free asynchronous common subset protocol FIN (CCS' 23) into the state-of-the-art framework of concurrent broadcast and agreement. The liveness of FIN-NG relies on our non-trivial redesign of FIN's multi-valued validated Byzantine agreement towards achieving optimal quality. FIN-NG greatly improves the performance of FIN and already outperforms Dumbo-NG in most deployment settings. To further overcome the scalability limit of FIN-NG due to $\bigO(n^3)$ messages, we propose JUMBO, a scalable instantiation of Dumbo-NG, with only $\bigO(n^2)$ complexities for both authenticators and messages. We use various aggregation and dispersal techniques for QCs to significantly reduce the authenticator complexity of original Dumbo-NG implementations by up to $\bigO(n^2)$ orders. We also propose a ``fairness'' patch for JUMBO, thus preventing a flooding adversary from controlling an overwhelming portion of transactions in its output.
Autoregressive Queries for Adaptive Tracking with Spatio-TemporalTransformers

The rich spatio-temporal information is crucial to capture the complicated target appearance variations in visual tracking. However, most top-performing tracking algorithms rely on many hand-crafted components for spatio-temporal information aggregation. Consequently, the spatio-temporal information is far away from being fully explored. To alleviate this issue, we propose an adaptive tracker with spatio-temporal transformers (named AQATrack), which adopts simple autoregressive queries to effectively learn spatio-temporal information without many hand-designed components. Firstly, we introduce a set of learnable and autoregressive queries to capture the instantaneous target appearance changes in a sliding window fashion. Then, we design a novel attention mechanism for the interaction of existing queries to generate a new query in current frame. Finally, based on the initial target template and learnt autoregressive queries, a spatio-temporal information fusion module (STM) is designed for spatiotemporal formation aggregation to locate a target object. Benefiting from the STM, we can effectively combine the static appearance and instantaneous changes to guide robust tracking. Extensive experiments show that our method significantly improves the tracker's performance on six popular tracking benchmarks: LaSOT, LaSOText, TrackingNet, GOT-10k, TNL2K, and UAV123.
Cannabis Seed Variant Detection using Faster R-CNN

Analyzing and detecting cannabis seed variants is crucial for the agriculture industry. It enables precision breeding, allowing cultivators to selectively enhance desirable traits. Accurate identification of seed variants also ensures regulatory compliance, facilitating the cultivation of specific cannabis strains with defined characteristics, ultimately improving agricultural productivity and meeting diverse market demands. This paper presents a study on cannabis seed variant detection by employing a state-of-the-art object detection model Faster R-CNN. This study implemented the model on a locally sourced cannabis seed dataset in Thailand, comprising 17 distinct classes. We evaluate six Faster R-CNN models by comparing performance on various metrics and achieving a mAP score of 94.08\% and an F1 score of 95.66\%. This paper presents the first known application of deep neural network object detection models to the novel task of visually identifying cannabis seed types.