Miracle-dz/UniFAR-FTUs huggingface dataset:
Simultaneous 3D Construction and Imaging of Plant Cells Using Plasmonic Nanoprobe-Assisted Multimodal Nonlinear Optical Microscopy

Nonlinear optical (NLO) imaging has emerged as a promising plant cell imaging technique due to its large optical penetration, inherent 3D spatial resolution, and reduced photodamage; exogenous nanoprobes are usually needed for nonsignal target cell analysis. Here, we report in vivo, simultaneous 3D labeling and imaging of potato cell structures using plasmonic nanoprobe-assisted multimodal NLO microscopy. Experimental results show that the complete cell structure can be imaged via the combination of second-harmonic generation (SHG) and two-photon luminescence (TPL) when noble metal silver or gold ions are added. In contrast, without the noble metal ion solution, no NLO signals from the cell wall were acquired. The mechanism can be attributed to noble metal nanoprobes with strong nonlinear optical responses formed along the cell walls via a femtosecond laser scan. During the SHG-TPL imaging process, noble metal ions that crossed the cell wall were rapidly reduced to plasmonic nanoparticles with the fs laser and selectively anchored onto both sides of the cell wall, thereby leading to simultaneous 3D labeling and imaging of the potato cells. Compared with the traditional labeling technique that needs in vitro nanoprobe fabrication and cell labeling, our approach allows for one-step, in vivo labeling of plant cells, thus providing a rapid, cost-effective method for cellular structure construction and imaging.
Risk-Minimizing Two-Player Zero-Sum Stochastic Differential Game via Path Integral Control

This paper addresses a continuous-time risk-minimizing two-player zero-sum stochastic differential game (SDG), in which each player aims to minimize its probability of failure. Failure occurs in the event when the state of the game enters into predefined undesirable domains, and one player's failure is the other's success. We derive a sufficient condition for this game to have a saddle-point equilibrium and show that it can be solved via a Hamilton-Jacobi-Isaacs (HJI) partial differential equation (PDE) with a Dirichlet boundary condition. Under certain assumptions on the system dynamics and cost function, we establish the existence and uniqueness of the saddle-point of the game. We provide explicit expressions for the saddle-point policies which can be numerically evaluated using path integral control. This allows us to solve the game online via Monte Carlo sampling of system trajectories. We implement our control synthesis framework on two classes of risk-minimizing zero-sum SDGs: a disturbance attenuation problem and a pursuit-evasion game. Simulation studies are presented to validate the proposed control synthesis framework.
Achievable Sum-rate of variants of QAM over Gaussian Multiple Access Channel with and without security

The performance of next generation wireless systems (5G/6G and beyond) at the physical layer is primarily driven by the choice of digital modulation techniques that are bandwidth and power efficient, while maintaining high data rates. Achievable rates for Gaussian input and some finite constellations (BPSK/QPSK/QAM) are well studied in the literature. However, new variants of Quadrature Amplitude Modulation (QAM) such as Cross-QAM (XQAM), Star-QAM (S-QAM), Amplitude and phase shift keying (APSK), and Hexagonal Quadrature Amplitude Modulation (H-QAM) are not studied in the context of achievable rates for meeting the demand of high data rates. In this paper, we study achievable rate region for different variants of M-QAM like Cross-QAM, H-QAM, Star-QAM and APSK. We also compute mutual information corresponding to the sum rate of Gaussian Multiple Access Channel (G-MAC), for hybrid constellation scheme, e.g., user 1 transmits using Star-QAM and user 2 by H-QAM. From the results, it is observed that S-QAM gives the maximum sum-rate when users transmit same constellations. Also, it has been found that when hybrid constellation is used, the combination of Star-QAM \&H-QAM gives the maximum rate. In the next part of the paper, we consider a scenario wherein an adversary is also present at the receiver side and is trying to decode the information. We model this scenario as Gaussian Multiple Access Wiretap Channel (G-MAW-WT). We then compute the achievable secrecy sum rate of two user G-MAC-WT with discrete inputs from different variants of QAM (viz, X-QAM, H-QAM and S-QAM). It has been found that at higher values of SNR, S-QAM gives better values of SSR than the other variants. For hybrid inputs of QAM, at lower values of SNR, combination of APSK and S-QAM gives better results and at higher values of SNR, combination of HQAM and APSK gives greater value of SSR.
Mode Combinability: Exploring Convex Combinations of Permutation Aligned Models

We explore element-wise convex combinations of two permutation-aligned neural network parameter vectors ΘA and ΘB of size d. We conduct extensive experiments by examining various distributions of such model combinations parametrized by elements of the hypercube [0,1]d and its vicinity. Our findings reveal that broad regions of the hypercube form surfaces of low loss values, indicating that the notion of linear mode connectivity extends to a more general phenomenon which we call mode combinability. We also make several novel observations regarding linear mode connectivity and model re-basin. We demonstrate a transitivity property: two models re-based to a common third model are also linear mode connected, and a robustness property: even with significant perturbations of the neuron matchings the resulting combinations continue to form a working model. Moreover, we analyze the functional and weight similarity of model combinations and show that such combinations are non-vacuous in the sense that there are significant functional differences between the resulting models.
Object Detection Difficulty: Suppressing Over-aggregation for Faster and Better Video Object Detection

Current video object detection (VOD) models often encounter issues with over-aggregation due to redundant aggregation strategies, which perform feature aggregation on every frame. This results in suboptimal performance and increased computational complexity. In this work, we propose an image-level Object Detection Difficulty (ODD) metric to quantify the difficulty of detecting objects in a given image. The derived ODD scores can be used in the VOD process to mitigate over-aggregation. Specifically, we train an ODD predictor as an auxiliary head of a still-image object detector to compute the ODD score for each image based on the discrepancies between detection results and ground-truth bounding boxes. The ODD score enhances the VOD system in two ways: 1) it enables the VOD system to select superior global reference frames, thereby improving overall accuracy; and 2) it serves as an indicator in the newly designed ODD Scheduler to eliminate the aggregation of frames that are easy to detect, thus accelerating the VOD process. Comprehensive experiments demonstrate that, when utilized for selecting global reference frames, ODD-VOD consistently enhances the accuracy of Global-frame-based VOD models. When employed for acceleration, ODD-VOD consistently improves the frames per second (FPS) by an average of 73.3% across 8 different VOD models without sacrificing accuracy. When combined, ODD-VOD attains state-of-the-art performance when competing with many VOD methods in both accuracy and speed. Our work represents a significant advancement towards making VOD more practical for real-world applications. The code will be released at https://github.com/bingqingzhang/odd-vod.
Design of conical Wolter-I geometry with sectioned secondary mirrors for x-ray telescopes

Abstract. Conical Wolter-I geometry is employed for many x-ray telescopes to lower their cost and fabrication difficulty at the expense of angular resolution. Owing to the conic error, the angular resolution of conical Wolter-I geometry is much worse than that of Wolter-I geometry, especially for the telescopes with large diameter. We optimized the conical Wolter-I geometry to significantly improve the angular resolution. We designed a conical Wolter-I geometry with sectioned secondary mirrors. Based on the normal conical Wolter-I geometry, we divided the secondary mirror into two equal sections along the optical axis. In this case, the collecting area was reduced by 5% because of the interval between the two sections. Meanwhile, the conic error was reduced by about 50%, indicating a great improvement in angular resolution. Regarding our improvement in the thermal slumping technique, it is feasible to fabricate sectioned mirrors, thus improving the angular resolution by 50% at the cost of a 5%-reduction in collecting area. In addition, a hybrid geometry, comprising the sectioned and nonsectioned geometries, is proposed as an alternative for x-ray telescopes with a large amount of nested shells, to obtain both a large collecting area and decent angular resolution.
Optimal transport for types and convex analysis for definable predicates in tracial $\mathrm{W}^*$-algebras

We investigate the connections between continuous model theory, free probability, and optimal transport/convex analysis in the context of tracial von Neumann algebras. In particular, we give an analog of Monge-Kantorovich duality for optimal couplings where the role of probability distributions on $\mathbb{C}^n$ is played by model-theoretic types, the role of real-valued continuous functions is played by definable predicates, and the role of continuous function $\mathbb{C}^n \to \mathbb{C}^n$ is played by definable functions. In the process, we also advance the understanding of definable predicates and definable functions by showing that all definable predicates can be approximated by"$C^1$ definable predicates"whose gradients are definable functions. As a consequence, we show that every element in the definable closure of $\mathrm{W}^*(x_1,\dots,x_n)$ can be expressed as a definable function of $(x_1,\dots,x_n)$. We give several classes of examples showing that the definable closure can be much larger than $\mathrm{W}^*(x_1,\dots,x_n)$ in general.
Minwise-Independent Permutations with Insertion and Deletion of Features

In their seminal work, Broder \textit{et. al.}~\citep{BroderCFM98} introduces the $\mathrm{minHash}$ algorithm that computes a low-dimensional sketch of high-dimensional binary data that closely approximates pairwise Jaccard similarity. Since its invention, $\mathrm{minHash}$ has been commonly used by practitioners in various big data applications. Further, the data is dynamic in many real-life scenarios, and their feature sets evolve over time. We consider the case when features are dynamically inserted and deleted in the dataset. We note that a naive solution to this problem is to repeatedly recompute $\mathrm{minHash}$ with respect to the updated dimension. However, this is an expensive task as it requires generating fresh random permutations. To the best of our knowledge, no systematic study of $\mathrm{minHash}$ is recorded in the context of dynamic insertion and deletion of features. In this work, we initiate this study and suggest algorithms that make the $\mathrm{minHash}$ sketches adaptable to the dynamic insertion and deletion of features. We show a rigorous theoretical analysis of our algorithms and complement it with extensive experiments on several real-world datasets. Empirically we observe a significant speed-up in the running time while simultaneously offering comparable performance with respect to running $\mathrm{minHash}$ from scratch. Our proposal is efficient, accurate, and easy to implement in practice.
Multi-Source Domain Adaptation for Cross-Domain Fault Diagnosis of Chemical Processes

Fault diagnosis is an essential component in process supervision. Indeed, it determines which kind of fault has occurred, given that it has been previously detected, allowing for appropriate intervention. Automatic fault diagnosis systems use machine learning for predicting the fault type from sensor readings. Nonetheless, these models are sensible to changes in the data distributions, which may be caused by changes in the monitored process, such as changes in the mode of operation. This scenario is known as Cross-Domain Fault Diagnosis (CDFD). We provide an extensive comparison of single and multi-source unsupervised domain adaptation (SSDA and MSDA respectively) algorithms for CDFD. We study these methods in the context of the Tennessee-Eastmann Process, a widely used benchmark in the chemical industry. We show that using multiple domains during training has a positive effect, even when no adaptation is employed. As such, the MSDA baseline improves over the SSDA baseline classification accuracy by 23% on average. In addition, under the multiple-sources scenario, we improve classification accuracy of the no adaptation setting by 8.4% on average.
Associations of protein intake, sources and distribution on muscle strength in community-dwelling older adults living in Auckland, New Zealand

Abstract Protein intake, sources and distribution impact on muscle protein synthesis and muscle mass in older adults. However, it is less clear whether dietary protein influences muscle strength. Data were obtained from the Researching Eating Activity and Cognitive Health (REACH) study, a cross-sectional study aimed at investigating dietary patterns, cognitive function and metabolic syndrome in older adults aged 65–74 years. Dietary intake was assessed using a 4-d food record and muscle strength using a handgrip strength dynamometer. After adjusting for confounders, in female older adults (n 212), total protein intake (β = 0⋅22, P < 0⋅01); protein from dairy and eggs (β = 0⋅21, P = 0⋅03) and plant food sources (β = 0⋅60, P < 0⋅01); and frequently consuming at least 0⋅4 g/kg BW per meal (β = 0⋅08, P < 0⋅01) were associated with higher BMI-adjusted muscle strength. However, protein from meat and fish intake and the coefficient of variance of protein intake were not related to BMI-muscle strength in female older adults. No statistically significant associations were observed in male participants (n = 113). There may be sex differences when investigating associations between protein intake and muscle strength in older adults. Further research is needed to investigate these sex differences.
Simulation Analysis to Estimate Performances of Automated Parking System

Automated parking systems make it possible to alleviate the parking problem, improve effective investment in real estate, and reduce the area devoted to parking. Currently, there are several types of automated parking systems, depending on the manufacturer. This article focuses on the pallet type and robot type, and the main factors in the selection of an automatic parking system are cost and system performance. Generally, the performance of an automatic parking system is calculated based on the average processing time between the nearest and the farthest parking at only two positions, which results in low reliability. Therefore, the objective of this paper is to study and estimate the performance of automatic parking systems by comparing the pallet-type and robot-type systems through computer simulations using SIMIO software and analyzing the costs. The results show that the waiting time to entry of the robot type is at least 47% more efficient than that of the pallet type. However, the waiting time for retrieval of the pallet type is at least 16% more efficient than that of the robot type, and the total cost of the robot type is about 4% lower than that of the pallet type.
Kilohertz Transcranial Magnetic Perturbation (kTMP): A New Non-invasive Method to Modulate Cortical Excitability

Background Non-invasive brain stimulation (NIBS) provides a method for safely perturbing brain activity, and has been employed in basic research to test hypotheses concerning brain-behavior relationships with increasing translational applications. Objective We introduce and evaluate a novel subthreshold NIBS method: kilohertz transcranial magnetic perturbation (kTMP). KTMP is a magnetic induction method that delivers continuous kHz-frequency cortical electric fields which may be amplitude-modulated to potentially mimic electrical activity at endogenous frequencies. Methods We used TMS to compare the amplitude of motor-evoked potentials (MEPs) in a hand muscle before and after kTMP. In Experiment 1, we applied kTMP for 10 min over motor cortex to induce an E-field amplitude of approximately 2.0 V/m, comparing the effects of waveforms at frequencies of 2.0, 3.5, or 5.0 kHz. In Experiments 2 and 3 we used two forms of amplitude-modulated kTMP with a carrier frequency at 3.5 kHz and modulation frequencies of either 20 or 140 Hz. Results The only percept associated with kTMP was an auditory tone, making kTMP amenable for double-blind experimentation. Relative to sham stimulation, non-modulated kTMP at 2.0 and 3.5 kHz resulted in an increase in cortical excitability, with Experiments 2 and 3 providing a replication of this effect for the 3.5 kHz condition. Although amplitude-modulated kTMP increased MEP amplitude compared to sham, no enhancement was found compared to non-modulated kTMP. Conclusions kTMP opens a new experimental NIBS space inducing relatively large amplitude subthreshold E-fields able to increase cortical excitability with minimal sensation.
Leading and Following: Noise Differently Affects Semantic 1 and Acoustic Processing during Naturalistic Speech 2 Comprehension

23 While our daily verbal communication is challenging with unavoidable environmental noise, the neural 24 mechanisms underlying speech-in-noise comprehension remain to be elucidated. The present study 25 investigated the neural tracking of acoustic and semantic speech information during noisy naturalistic 26 speech comprehension. Participants listened to narrative audios mixed with spectrally matched stationary 27 noise at three signal-to-ratio (SNR) levels and 60-channel electroencephalography signals were recorded. 28 A temporal response function (TRF) method was employed to derive event-related-like responses to the 29 continuous speech stream at both the acoustic and the semantic levels. Whereas the amplitude envelope of 30 the naturalistic speech was taken as the acoustic feature, word entropy and word surprisal were extracted 31 via the natural language processing method as two semantic features. Theta-band frontocentral TRF 32 responses to the acoustic feature were observed at around 400 ms following speech fluctuation onset over 33 all three SNR levels, and the response latency was more delayed with increasing SNR. Delta-band frontal 34 TRF responses to the semantic feature of word entropy were observed at around 200 to 600 ms leading to 35 speech fluctuation onset over all three SNR levels, and the response latency became more leading with 36 increasing SNR. While the following responses to speech acoustics were consistent with previous studies, 37 the leading responses to speech semantics suggest our brain could be taking an active approach to deal with 38 noisy speech comprehension. 39
Pedestrian Trajectory Prediction in Heterogeneous Traffic Using Pose Keypoints-Based Convolutional Encoder-Decoder Network

Future pedestrian trajectory prediction offers great prospects for many practical applications. Most existing methods focus on social interaction among pedestrians but ignore the factors that heterogeneous traffic objects (cars, dogs, bicycles, motorcycles, etc.) have significant influence on the future trajectory of a subject pedestrian. Also, the walking direction intention of a pedestrian may be referred by his/her pose keypoints. Considering this, this work proposes to predict a pedestrian’s future trajectory by jointly using neighboring heterogeneous traffic information and his/her pose keypoints. To fulfill this, an end-to-end pose keypoints-based convolutional encoder-decoder network (PK-CEN) is designed, in which the heterogeneous traffic and pose keypoints are modeled as input. After training, PK-CEN is evaluated on manifold crowded video sequences collected from the public dataset MOT16, MOT17 and MOT20. Experimental results demonstrate that it outperforms state-of-the-art approaches, in terms of prediction errors.
Squaraine nanoparticles for optoacoustic imaging-guided synergistic cancer phototherapy

Abstract The unique optical properties of squaraine dyes make them promising for cancer phototheranostics, but the reported squaraines for in vivo treatments mainly rely on their photothermal effect, where monotherapy cannot achieve the desired therapeutic effect. Here we generated a type of squaraine capable of killing tumors through both photothermal and photodynamic effects. We optimized squaraine structure with selenium modulation and formulated it into nanoparticles that showed strong absorption of infrared light, negligible fluorescence, good photothermal conversion (66.6 %), and strong photodynamic effects even after several irradiation cycles. In addition, the nanoparticles could be tracked through their strong optoacoustic signal. In mice, the nanoparticles effectively accumulated in tumors and eliminated them upon irradiation, without causing adverse effects. Our work demonstrates the potential of selenium modulation of squaraine for precise cancer diagnosis and treatment through synergistic photothermal and photodynamic effects.
Characterization of pediatric porcine pulmonary valves as a model for tissue engineered heart valves

Heart valve tissue engineering holds the potential to transform the surgical management of congenital heart defects affecting the pediatric pulmonary valve (PV) by offering a viable valve replacement with the compositional, architectural and mechanical properties required to function in situ. While aiming to recapitulate the native valve, the minimum requirement for tissue engineered heart valves (TEHVs) has historically been adequate mechanical function at implantation. However, long-term in situ functionality of TEHVs remains elusive, suggesting that a closer approximation of the native valve is required. The realization of biomimetic engineered pediatric PV is impeded by insufficient characterization of healthy pediatric tissue. In this study, we comprehensively characterized the planar biaxial tensile behaviour, extracellular matrix (ECM) composition and organization, and valvular interstitial cell (VIC) phenotypes of PVs from piglets to provide benchmarks for TEHVs. The piglet PV possessed an anisotropic and non-linear tension-strain profile from which material constants for a predictive constitutive model were derived. Further, the ECM of the pediatric PV possessed a trilayer organization populated by collagen, glycosaminoglycans, and elastin. Biochemical quantification of ECM proteins normalized to wet weight and DNA content of PV tissue revealed homogenous distribution of proteins across sampled regions of the leaflet. Finally, the predominant phenotype of VICs in the piglet PV was quiescent vimentin-expressing fibroblasts, with a small proportion of activated α-smooth muscle actin-expressing myofibroblasts residing primarily at the base of the leaflet. Overall, the properties characterized in this study can be used to inform TEHV design parameters towards generation of biomimetic pediatric PVs. Graphical Abstract