Variations in Gonadosomatic Index, Gonadal Development and Spawning Induction of Spotted Scat Scatophagus argus (Linnaeus, 1766)

This study evaluated variations in gonadosomatic index (GSI), gonadal development stages, and hormonal spawning induction of Scatophagus argus in captivity. Male and female fish were cultured separately in net cages in Tam Giang lagoon, Central Vietnam, from January to December 2020. Five fish of each sex were randomly sampled monthly. Gonads were collected, GSI determined, and subsequently prepared for histology. Gamete quality was assessed with a light microscope. Spawning was hormonally induced with different doses of human chorionic gonadotropin (hCG) and luteinizing hormone-release hormone (LHRH-A2). Gonadal development started to increase in March, peaking in July. The peak spawning period of the fish was from June to August, displaying the highest GSI value, sperm motility, and oocyte diameter. Only female GSI fluctuated significantly month-by-month (P<0.05). Histological examination indicated that S. argus is a multiple-spawner. Application of LHRH-A2 (70 µg/kg) stimulated spawning and resulted in better latency periods, fertilization, and hatching rates

General regularization in covariate shift adaptation

Sample reweighting is one of the most widely used methods for correcting the error of least squares learning algorithms in reproducing kernel Hilbert spaces (RKHS), that is caused by future data distributions that are different from the training data distribution. In practical situations, the sample weights are determined by values of the estimated Radon-Nikod\'ym derivative, of the future data distribution w.r.t.~the training data distribution. In this work, we review known error bounds for reweighted kernel regression in RKHS and obtain, by combination, novel results. We show under weak smoothness conditions, that the amount of samples, needed to achieve the same order of accuracy as in the standard supervised learning without differences in data distributions, is smaller than proven by state-of-the-art analyses

EnSolver: Uncertainty-Aware CAPTCHA Solver Using Deep Ensembles

The popularity of text-based CAPTCHA as a security mechanism to protect websites from automated bots has prompted researches in CAPTCHA solvers, with the aim of understanding its failure cases and subsequently making CAPTCHAs more secure. Recently proposed solvers, built on advances in deep learning, are able to crack even the very challenging CAPTCHAs with high accuracy. However, these solvers often perform poorly on out-of-distribution samples that contain visual features different from those in the training set. Furthermore, they lack the ability to detect and avoid such samples, making them susceptible to being locked out by defense systems after a certain number of failed attempts. In this paper, we propose EnSolver, a novel CAPTCHA solver that utilizes deep ensemble uncertainty estimation to detect and skip out-of-distribution CAPTCHAs, making it harder to be detected. We demonstrate the use of our solver with object detection models and show empirically that it performs well on both in-distribution and out-of-distribution data, achieving up to 98.1% accuracy when detecting out-of-distribution data and up to 93% success rate when solving in-distribution CAPTCHAs.Comment: Epistemic Uncertainty - E-pi UAI 2023 Worksho

A Simultaneous Extraction of Context and Community from pervasive signals using nested Dirichlet process

Understanding user contexts and group structures plays a central role in pervasive computing. These contexts and community structures are complex to mine from data collected in the wild due to the unprecedented growth of data, noise, uncertainties and complexities. Typical existing approaches would first extract the latent patterns to explain human dynamics or behaviors and then use them as a way to consistently formulate numerical representations for community detection, often via a clustering method. While being able to capture high-order and complex representations, these two steps are performed separately. More importantly, they face a fundamental difficulty in determining the correct number of latent patterns and communities. This paper presents an approach that seamlessly addresses these challenges to simultaneously discover latent patterns and communities in a unified Bayesian nonparametric framework. Our Simultaneous Extraction of Context and Community (SECC) model roots in the nested Dirichlet process theory which allows a nested structure to be built to summarize data at multiple levels. We demonstrate our framework on five datasets where the advantages of the proposed approach are validated

On regularized Radon-Nikodym differentiation

We discuss the problem of estimating Radon-Nikodym derivatives. This problem appears in various applications, such as covariate shift adaptation, likelihood-ratio testing, mutual information estimation, and conditional probability estimation. To address the above problem, we employ the general regularization scheme in reproducing kernel Hilbert spaces. The convergence rate of the corresponding regularized algorithm is established by taking into account both the smoothness of the derivative and the capacity of the space in which it is estimated. This is done in terms of general source conditions and the regularized Christoffel functions. We also find that the reconstruction of Radon-Nikodym derivatives at any particular point can be done with high order of accuracy. Our theoretical results are illustrated by numerical simulations.Comment: arXiv admin note: text overlap with arXiv:2307.1150

Seeing the world from its words: All-embracing Transformers for fingerprint-based indoor localization

In this paper, we present all-embracing Transformers (AaTs) that are capable of deftly manipulating attention mechanism for Received Signal Strength (RSS) fingerprints in order to invigorate localizing performance. Since most machine learning models applied to the RSS modality do not possess any attention mechanism, they can merely capture superficial representations. Moreover, compared to textual and visual modalities, the RSS modality is inherently notorious for its sensitivity to environmental dynamics. Such adversities inhibit their access to subtle but distinct representations that characterize the corresponding location, ultimately resulting in significant degradation in the testing phase. In contrast, a major appeal of AaTs is the ability to focus exclusively on relevant anchors in RSS sequences, allowing full rein to the exploitation of subtle and distinct representations for specific locations. This also facilitates disregarding redundant clues formed by noisy ambient conditions, thus enhancing accuracy in localization. Apart from that, explicitly resolving the representation collapse (i.e., none-informative or homogeneous features, and gradient vanishing) can further invigorate the self-attention process in transformer blocks, by which subtle but distinct representations to specific locations are radically captured with ease. For that purpose, we first enhance our proposed model with two sub-constraints, namely covariance and variance losses at the Anchor2Vec. The proposed constraints are automatically mediated with the primary task towards a novel multi-task learning manner. In an advanced manner, we present further the ultimate in design with a few simple tweaks carefully crafted for transformer encoder blocks. This effort aims to promote representation augmentation via stabilizing the inflow of gradients to these blocks. Thus, the problems of representation collapse in regular Transformers can be tackled. To evaluate our AaTs, we compare the models with the state-of-the-art (SoTA) methods on three benchmark indoor localization datasets. The experimental results confirm our hypothesis and show that our proposed models could deliver much higher and more stable accuracy

Empirically Investigating the Impact of Antenna Polarization and Modulation Parameters on Subsoil Communication Range in LoRa Networks

The Long Range (LoRa) network has been widely acknowledged for its efficiency and reliability in terrestrial sensing applications. However, building a robust LoRa network in the subsoil environment, which presents challenges for radio communication, remains challenging. This study evaluates the impact of antenna polarization and LoRa modulation parameters, such as bandwidth and spreading factor, on subsoil communication ranges. Based on the results of our experiments, we propose practical LoRa network configurations for the seamless transmission of subsoil sensory data to the surface.</p

Design and characterization of SiON integrated optics components for optical coherence tomography

Optical coherence tomography (OCT) is a technique for high resolution imaging of biological tissues with a depth range of a few millimeters. OCT is based on interferometry to enable depth ranging. Currently, optical components for OCT are rather bulky and expensive; the use of integrated optical circuits presents a great opportunity to reduce costs and enhance system functionality and performance. We present the design and characterization of SiON-based integrated optics waveguides, splitters, couplers and interferometers for OCT operating at a wavelength of 1.3 um

Probability of Task Completion and Energy Consumption in Cooperative Pervasive Mobile Computing

It is challenging for multiple smartphones to complete a given task in large-scale pervasive sensing systems cooperatively. Sensing paradigms such as opportunistic sensing, participatory sensing, and hybrid sensing have been used for smartphones to work together seamlessly under different contexts. However, these existing paradigms do not incorporate the energy problem and sharing sensory resources of applications. In this paper, we revisit sensing paradigms regarding the probability of task completion and energy consumption for smartphones to cooperatively complete a sensing task. In addition, we propose a symbiotic sensing paradigm that can significantly save smartphone batteries while maintaining equivalent performance to existing paradigms, provided that the smartphones allow applications to share sensing resources. We also quantitatively evaluate our probabilistic models with a realistic case study. This work is a useful aid to designing and evaluating large-scale smartphone-based sensing systems before deployment, which saves money and effort