Miracle-dz/UniFAR-FTUs huggingface dataset:
<inline-formula><tex-math notation="LaTeX">$K$</tex-math><alternatives><mml:math><mml:mi>K</mml:mi></mml:math><inline-graphic xlink:href="qi-ieq1-3082567.gif"/></alternatives></inline-formula>-Shot Contrastive Learning of Visual Features With Multiple Instance Augmentations

In this paper, we propose the <inline-formula><tex-math notation="LaTeX">$K$</tex-math><alternatives><mml:math><mml:mi>K</mml:mi></mml:math><inline-graphic xlink:href="qi-ieq2-3082567.gif"/></alternatives></inline-formula>-Shot Contrastive Learning (KSCL) of visual features by applying multiple augmentations to investigate the sample variations within individual instances. It aims to combine the advantages of <italic>inter-instance discrimination</italic> by learning discriminative features to distinguish between different instances, as well as <italic>intra-instance variations</italic> by matching queries against the variants of augmented samples over instances. Particularly, for each instance, it constructs an instance subspace to model the configuration of how the significant factors of variations in <inline-formula><tex-math notation="LaTeX">$K$</tex-math><alternatives><mml:math><mml:mi>K</mml:mi></mml:math><inline-graphic xlink:href="qi-ieq3-3082567.gif"/></alternatives></inline-formula>-shot augmentations can be combined to form the variants of augmentations. Given a query, the most relevant variant of instances is then retrieved by projecting the query onto their subspaces to predict the positive instance class. This generalizes the existing contrastive learning that can be viewed as a special one-shot case. An eigenvalue decomposition is performed to configure instance subspaces, and the embedding network can be trained end-to-end through the differentiable subspace configuration. Experiment results demonstrate the proposed <inline-formula><tex-math notation="LaTeX">$K$</tex-math><alternatives><mml:math><mml:mi>K</mml:mi></mml:math><inline-graphic xlink:href="qi-ieq4-3082567.gif"/></alternatives></inline-formula>-shot contrastive learning achieves superior performances to the state-of-the-art unsupervised methods.
Effects of temperature on photosynthesis and plant growth in the assimilation shoots of a rose

Abstract The effects of temperature on photosynthesis, ribulose-bisphosphate carboxylase (Rubisco) content and whole plant growth were investigated in the assimilation shoots of a rose (Rosa hybrida L.). Assimilation shoots were grown at two different day/night temperature regimes of 20/15°C (LT) and 30/25°C (HT) for 42 days after 1-month growth. Although LT initially suppressed the photosynthetic rate during the first 7 days, prolonged growth at LT enhanced potential photosynthesis. This was associated with increases in Rubisco and N contents at the level of a single leaf. Rubisco content and the photosynthetic rate at 25°C were 2.8-fold and 1.6-fold higher in the LT plants than in the HT plants at day 42, respectively. The relative growth rate at the level of the whole plant was lower in the LT plants during the first 28 days and the leaf area ratio was smaller in the LT plants throughout the experiment. However, enhanced photosynthesis during growth at LT led to increases in the net assimilation rate at the level of the whole plant, and final biomass at day 42 did not differ between the two temperature treatments. To enhance the photosynthetic capacity in assimilation shoots of a rose, cultivation at 20/15°C is better than cultivation at 30/25°C.
Uncovering Magnetic Turbulence in Young Supernova Remnants with Polarized X-Ray Imaging

Observations of young supernova remnants (SNRs) in X-rays and γ-rays have provided conclusive evidence for particle acceleration to at least TeV energies. Analysis of high-spatial-resolution X-ray maps of young SNRs has indicated that the particle acceleration process is accompanied by strong nonadiabatic amplification of magnetic fields. If Fermi acceleration is the mechanism producing the energetic cosmic rays (CRs), the amplified magnetic field must be turbulent, and CR-driven instabilities are among the most probable mechanisms for converting the shock ram pressure into magnetic turbulence. The development and evolution of strong magnetic turbulence in collisionless plasmas forming SNR shells are complicated phenomena which include the amplification of magnetic modes, anisotropic mode transformations at shocks, as well as the nonlinear physics of turbulent cascades. Polarized X-ray synchrotron radiation from ultrarelativistic electrons accelerated in the SNR shock is produced in a thin layer immediately behind the shock and is not subject to the Faraday depolarization effect. These factors open up possibilities to study some properties of magnetic turbulence, and here we present polarized X-ray synchrotron maps of SNR shells assuming different models of magnetic turbulence cascades. It is shown that different models of anisotropic turbulence can be distinguished by measuring the predominant polarization angle direction. We discuss the detection of these features in Tycho’s SNR with the coming generation of X-ray polarimeters such as the Imaging X-ray Polarimetry Explorer.
Deep Photometric Stereo for Non-Lambertian Surfaces

This paper addresses the problem of photometric stereo, in both calibrated and uncalibrated scenarios, for non-Lambertian surfaces based on deep learning. We first introduce a fully convolutional deep network for calibrated photometric stereo, which we call PS-FCN. Unlike traditional approaches that adopt simplified reflectance models to make the problem tractable, our method directly learns the mapping from reflectance observations to surface normal, and is able to handle surfaces with general and unknown isotropic reflectance. At test time, PS-FCN takes an arbitrary number of images and their associated light directions as input and predicts a surface normal map of the scene in a fast feed-forward pass. To deal with the uncalibrated scenario where light directions are unknown, we introduce a new convolutional network, named LCNet, to estimate light directions from input images. The estimated light directions and the input images are then fed to PS-FCN to determine the surface normals. Our method does not require a pre-defined set of light directions and can handle multiple images in an order-agnostic manner. Thorough evaluation of our approach on both synthetic and real datasets shows that it outperforms state-of-the-art methods in both calibrated and uncalibrated scenarios.
Algorithms for Globally-Optimal Secure Signaling Over Gaussian MIMO Wiretap Channels Under Interference Constraints

Multi-user Gaussian MIMO wiretap channel is considered under interference power constraints (IPC), in addition to the total transmit power constraint (TPC). Algorithms for global maximization of its secrecy rate are proposed. Their convergence to the secrecy capacity is rigorously proved and a number of properties are established analytically. Unlike known algorithms, the proposed ones are not limited to the MISO case and are proved to converge to a global rather than local optimum in the general MIMO case, even when the channel is not degraded. In practice, the convergence is fast as only a small to moderate number of Newton steps is required to achieve a high precision level. The interplay of TPC and IPC is shown to result in an unusual property when an optimal point of the max-min problem does not provide an optimal transmit covariance matrix in some (singular) cases. To address this issue, an algorithm is developed to compute an optimal transmit covariance matrix in those singular cases. It is shown that this algorithm also solves the dual (nonconvex) problems of globally minimizing the total transmit power subject to the secrecy and interference constraints; it provides the minimum transmit power and respective signaling strategy needed to achieve the secrecy capacity, hence allowing power savings.
Determination of mutagenic potencies of aqueous extracts of Thermopsis turcica by Ames test

Thermopsis turcica is an endemic species of Turkey popularly known as Eber Sarisi or Piyam; it is a member of the Thermopsis genus, which contains plants considered to be medicinal throughout the world. In this study, the mutagenic effect of aqueous extracts obtained from the leaves, root, stem, and flowers of T. turcica were evaluated by the Ames test, using Salmonella typhimurium TA97, TA98, TA100, and TA102 strains with and without an S9 activation system. The results obtained were analyzed statistically using SPSS for Windows, and the Mann-Whitney test was performed for comparisons. The results showed that all of the aqueous extracts of T. turcica were mutagenic for S. typhimurium TA98 with S9 mix, and, at a dosage of 3750 µg/plate, the leaf extracts were also found to be mutagenic for S. typhimurium TA102 with or without S9 mix. The results further demonstrated that, in relation to the concentration, leaf extracts of T. turcica increased the frequency of reverse mutation in S. typhimurium TA102 with S9 mix.
U2-ONet: A Two-level Nested Octave U-structure with Multiscale Attention Mechanism for Moving Instances Segmentation

Most scenes in practical applications are dynamic scenes containing moving objects, so segmenting accurately moving objects is crucial for many computer vision applications. In order to efficiently segment out all moving objects in the scene, regardless of whether the object has a predefined semantic label, we propose a two-level nested Octave U-structure network with a multiscale attention mechanism called U2-ONet. Each stage of U2-ONet is filled with our newly designed Octave ReSidual U-block (ORSU) to enhance the ability to obtain more context information at different scales while reducing spatial redundancy of feature maps. In order to efficiently train our multi-scale deep network, we introduce a hierarchical training supervision strategy that calculates the loss at each level while adding a knowledge matching loss to keep the optimization consistency. Experimental results show that our method achieves state-of-the-art performance in several general moving objects segmentation datasets.
Multi-Tone Signaling and ADC-Based Digital Receiver for High-Speed Wireline Serial Links

The exponential growth of Internet traffic and related demands for higher communication speed pushes processor-to-processor and processor-to-memory interconnects to provide further higher data-rate. Often time, processor-to-memory interconnects’ speed is limited by reflections due to their multi-drop bus (MDB) channel nature, and processor-to-processor interconnects’ speed is limited by inter-symbol interference due to the slowly-decaying channel pulse response and the operating speed and/or power efficiency of the equalization circuits. For the data-rate to continue its exponential growth, more complex modulation and equalization techniques should be employed in the transmitter (TX) and receiver (RX) circuits, given the power and silicon area budget. This thesis presents a signaling scheme designed to overcome equalization challenges related to reflection-limited interfaces such as MDB interfaces. By shaping the spectrum of the transmitted signal appropriately to the channel’s frequency characteristics, the proposed signaling enables minimization of energy-loss due to reflections. While the data-rate of conventional non-return-to-zero (NRZ) signaling over MDB is limited by its first notch frequency unless power-hungry decision-feedback equalizer (DFE) is employed, the proposed signaling scheme allows overcoming such limitation by simple encoding and decoding. Analog multi-tone (AMT) signaling with single-sideband (SSB) radio-frequency (RF)-bands for efficient bandwidth usage is proposed for lossy point-to-point interfaces. The mutuallyorthogonal RF sub-bands feature self-equalization throughout the propagation through the communication medium and down-conversion at the RX, without necessitating the presence of conventional equalizers. To enable communication with higher modulation order for more efficient bandwidth utilization, an analog-to-digital converter (ADC)-based discrete multi-tone (DMT) RX is designed and implemented. Thanks to the high bandwidth efficiency of approximately 5.33 bits/Hz (including the cyclic prefix of DMT symbol) using 64-level quadrature amplitude modulation (64-QAM), the implemented RX undergoes less channel attenuation as compared to its 4-level pulse amplitude modulation (PAM-4) counterpart given target communication speed through the same channel. Moreover, the inherently parallel RX architecture of DMT signaling allows the RX digital signal processing (DSP) elements to be designed in semi-custom style without stringent timing constraints. Finally, a PAM-4 RX based on ADC with fully-digital equalization is presented. With register-transfer level (RTL) modeling of the designed digital equalizer, the results demonstrate the feasibility of frequency-domain equalization of the time-domain signal using Fourier transform and its inversion. The proposed frequency-domain equalization technique removes the necessity
Hardware Implementation of Hyperbolic Tangent Function using Catmull-Rom Spline Interpolation

Deep neural networks yield the state of the art results in many computer vision and human machine interface tasks such as object recognition, speech recognition etc. Since, these networks are computationally expensive, customized accelerators are designed for achieving the required performance at lower cost and power. One of the key building blocks of these neural networks is non-linear activation function such as sigmoid, hyperbolic tangent (tanh), and ReLU. A low complexity accurate hardware implementation of the activation function is required to meet the performance and area targets of the neural network accelerators. This paper presents an implementation of tanh function using the Catmull-Rom spline interpolation. State of the art results are achieved using this method with comparatively smaller logic area.
Data-efficient visuomotor policy training using reinforcement learning and generative models

We present a data-efficient framework for solving deep visuomotor sequential decision-making problems which exploits the combination of reinforcement learning (RL) with the latent variable generative models. Our framework trains deep visuomotor policies by introducing an action latent variable such that the feed-forward policy search can be divided into two parts: (1) training a sub-policy that outputs a distribution over the action latent variable given a state of the system, and (2) training a generative model that outputs a sequence of motor actions given a latent action representation. Our approach enables safe exploration and alleviates the data-inefficiency problem as it exploits prior knowledge about valid sequences of motor actions. Moreover, by evaluating the quality of the generative models we are able to predict the performance of the RL policy training prior to the actual training on the physical robot. We achieve this by defining two novel measures, disentanglement and local linearity, for assessing the quality of generative models' latent spaces, and complementing them with the existing measures for evaluation of generative models. We demonstrate the efficiency of our approach on a picking task using several different generative models and determine which of their properties have the most influence on the final policy training.
PowerGAN: Synthesizing Appliance Power Signatures Using Generative Adversarial Networks

Non-intrusive load monitoring (NILM) allows users and energy providers to gain insight into home appliance electricity consumption using only the building's smart meter. Most current techniques for NILM are trained using significant amounts of labeled appliances power data. The collection of such data is challenging, making data a major bottleneck in creating well generalizing NILM solutions. To help mitigate the data limitations, we present the first truly synthetic appliance power signature generator. Our solution, PowerGAN, is based on conditional, progressively growing, 1-D Wasserstein generative adversarial network (GAN). Using PowerGAN, we are able to synthesise truly random and realistic appliance power data signatures. We evaluate the samples generated by PowerGAN in a qualitative way as well as numerically by using traditional GAN evaluation methods such as the Inception score.
Approaches of large-scale images recognition with more than 50, 000 categoris

Though current CV models have been able to achieve high levels of accuracy on small-scale images classification dataset with hundreds or thousands of categories, many models become infeasible in computational or space consumption when it comes to large-scale dataset with more than 50,000 categories. In this paper, we provide a viable solution for classifying large-scale species datasets using traditional CV techniques such as.features extraction and processing, BOVW(Bag of Visual Words) and some statistical learning technics like Mini-Batch K-Means,SVM which are used in our works. And then mixed with a neural network model. When applying these techniques, we have done some optimization in time and memory consumption, so that it can be feasible for large-scale dataset. And we also use some technics to reduce the impact of mislabeling data. We use a dataset with more than 50, 000 categories, and all operations are done on common computer with l 6GB RAM and a CPU of 3. OGHz. Our contributions are: 1) analysis what problems may meet in the training processes, and presents several feasible ways to solve these problems. 2) Make traditional CV models combined with neural network models provide some feasible scenarios for training large-scale classified datasets within the constraints of time and spatial resources.
Deep CHORES: Estimating Hallmark Measures of Physical Activity Using Deep Learning

Wrist accelerometers for assessing hallmark measures of physical activity (PA) are rapidly growing with the advent of smartwatch technology. Given the growing popularity of wrist-worn accelerometers, there needs to be a rigorous evaluation for recognizing (PA) type and estimating energy expenditure (EE) across the lifespan. Participants (66% women, aged 20-89 yrs) performed a battery of 33 daily activities in a standardized laboratory setting while a tri-axial accelerometer collected data from the right wrist. A portable metabolic unit was worn to measure metabolic intensity. We built deep learning networks to extract spatial and temporal representations from the time-series data, and used them to recognize PA type and estimate EE. The deep learning models resulted in high performance; the F1 score was: 0.82, 0.81, and 95 for recognizing sedentary, locomotor, and lifestyle activities, respectively. The root mean square error was 1.1 (+/-0.13) for the estimation of EE.
MMDF: Mobile Microscopy Deep Framework

Mobile microscopy is a promising technology to assist and to accelerate disease diagnostics, with its widespread adoption being hindered by the mediocre quality of acquired images. Although some paired image translation and super-resolution approaches for mobile microscopy have emerged, a set of essential challenges, necessary for automating it in a high-throughput setting, still await to be addressed. The issues like in-focus/out-of-focus classification, fast scanning deblurring, focus-stacking, etc. all have specific peculiarities when the data are recorded using a mobile device. In this work, we aspire to create a comprehensive pipeline by connecting a set of methods purposely tuned to mobile microscopy: (1) a CNN model for stable in-focus / out-of-focus classification, (2) modified DeblurGAN architecture for image deblurring, (3) FuseGAN model for combining in-focus parts from multiple images to boost the detail. We discuss the limitations of the existing solutions developed for professional clinical microscopes, propose corresponding improvements, and compare to the other state-of-the-art mobile analytics solutions.
cAMP Signaling in Chemosensory Transduction

of a dissertation at the University of Miami. Dissertation supervised by Professor Stephen Roper. No. of pages in text. (96) cAMP is a second messenger in a variety of chemosensory receptors, including taste buds and glucose-sensitive pancreatic β-cells. cAMP is modulated during taste transduction, yet the significance of cAMP changes and the taste cell types in which they occur (Type I glial-like; Type II Receptor; Type III Presynaptic) remain unclear. I developed techniques to image real-time changes in intracellular cAMP in taste cells using genetically-encoded cAMP reporters. This FRET-based reporter permits one to measure single-cell cAMP levels with excellent spatial and temporal resolution (Zaccolo & Pozzan 2002, Science 295:1711). Using a biolistic approach I have transfected rat fungiform taste buds with cAMP reporter plasmids. Focal application of bitter tastant to living fungiform tastebuds in situ produced a decrease in [cAMP]i within individual taste receptor cells. These results are qualitatively similar to previous biochemical measurements from homogenized taste tissue (Yan et al. 2001, Am J Physiol Cell Physiol 280:C742) but are now allowing us to examine the cAMP response in individual, identified cells. I next explored the effect of elevating cAMP on Ca levels, using Fura-2 imaging of isolated mouse vallate taste buds. Elevating [cAMP]i in taste buds evoked Ca responses in presynaptic/Type III taste cells, which do not express GAD1. cAMPinduced responses were generated by Ca influx. Using pharmacological antagonists, I determined that the Ca influx triggered by cAMP is through L-type Ca channels, whereas influx following depolarization is primarily through P/Q-type Ca channels. Consistent with these data, single cell RT-PCR showed that the L-type subunit (α1C) was expressed primarily in GAD-negative Presynaptic cells, while the P/Q-type (α1A) was expressed in all Presynaptic cells. Thus, cAMP may modulate the function of synapses in
Robust and Generalizable Visual Representation Learning via Random Convolutions

While successful for various computer vision tasks, deep neural networks have shown to be vulnerable to texture style shifts and small perturbations to which humans are robust. Hence, our goal is to train models in such a way that improves their robustness to these perturbations. We are motivated by the approximately shape-preserving property of randomized convolutions, which is due to distance preservation under random linear transforms. Intuitively, randomized convolutions create an infinite number of new domains with similar object shapes but random local texture. Therefore, we explore using outputs of multi-scale random convolutions as new images or mixing them with the original images during training. When applying a network trained with our approach to unseen domains, our method consistently improves the performance on domain generalization benchmarks and is scalable to ImageNet. Especially for the challenging scenario of generalizing to the sketch domain in PACS and to ImageNet-Sketch, our method outperforms state-of-art methods by a large margin. More interestingly, our method can benefit downstream tasks by providing a more robust pretrained visual representation.
Repdigits in Narayana's Cows Sequence and their Consequences

Narayana's cows sequence satisfies the third-order linear recurrence relation $N_n=N_{n-1}+N_{n-3}$ for $n \geq 3$ with initial conditions $N_0=0$ and $N_1=N_2=1$. In this paper, we study $b$-repdigits which are sums of two Narayana numbers. We explicitly determine these numbers for the bases $2\le b\leq100$ as an illustration. We also obtain results on the existence of Mersenne prime numbers, 10-repdigits, and numbers with distinct blocks of digits in the Narayana sequence. The proof of our main theorem uses lower bounds for linear forms in logarithms and a version of the Baker-Davenport reduction method in Diophantine approximation.