Miracle-dz/UniFAR-FTUs huggingface dataset:
Weight Matrix-Based Least Mean Square Algorithm for Target Detection in Passive Radars

Ambiguity function analysis is the most expensive process for target detection in passive radars. The computational cost is attributed to the extensive range-Doppler field required to evaluate the cross-correlation function. Some tools like fast Fourier transform or batching algorithm are employed to partially reduce the computational effort. In this paper a different generalization of least mean square algorithm is utilized for target detection. The basic idea is to employ the properties of the computed weight matrix to extract target coordinates. The algorithm performance is investigated by computer simulation using some practical simulated FM stereo signal. The results reveal the lower computational complexity of the presented procedure compared to existing methods.
Gender Identification via Voice Processing

Recognizing the sexual orientation of a person via speech has an assortment of utilizations going via speech investigation to customizing machine and human collaborations. Speech Recognition Technology can be installed in different continuous applications so as to expand the human-PC association. From apply autonomy to social insurance and aviation, from intelligent voice reaction frameworks to versatile communication and telematics, speech acknowledgment innovation have upgraded the human-machine connection. Sexual orientation acknowledgment is a critical part for the application implanting discourse acknowledgment as it lessens the computational intricacy for the further preparing in these applications. This paper involves extracting the constituent parts such as frequencies and interquartile ranges which are in light of a legitimate concern for distinguishing the speaker's sexual orientation with as meager discourse as could reasonably be expected
PHYLOGENY AND BIOGEOGRPHY OF THE TEA FAMILY 1 1 Title page 2 Title : Phytogeographic history of the Tea family inferred through high-resolution phylogeny and 3 fossils 4 Running title : Phylogeny and biogeography of the tea family 5 *

4 Running title: Phylogeny and biogeography of the tea family 5 *Yujing Yan1,2, *Charles C. Davis2, Dimitar Dimitrov1,3, Zhiheng Wang4, Carsten Rahbek5,1,4,6,7, Michael 6 Krabbe Borregaard1 7 1. Center for Macroecology, Evolution and Climate, GLOBE Institute, University of Copenhagen, 8 Universitetsparken 15, 2100, Copenhagen, Denmark 9 2. Department of Organismic and Evolutionary Biology, Harvard University Herbaria, 22 Divinity Ave, 10 Cambridge, MA 02138, USA 11 3. Department of Natural History, University Museum of Bergen, University of Bergen, P.O. Box 7800, 5020 12 Bergen, Norway 13 4. Institute of Ecology, College of Urban and Environmental Sciences, Key Laboratory of Earth Surface 14 Processes of Ministry of Education, Peking University, Beijing 100871, China 15 5. Center for Global Mountain Biodiversity, GLOBE Institute, University of Copenhagen, Universitetsparken 16 15, 2100 Copenhagen, Denmark 17 6. Department of Life Sciences, Imperial College London, Silkwood Park campus, Ascot SL5 7PY, UK 18 7. Danish Institute for Advanced Study, University of Southern Denmark, Odense, Denmark. 19 20 * Corresponding authors: 21 Yujing Yan, email: africarugu@gmail.com 22 Charles C. Davis, email: cdavis@oeb.harvard. edu 23 24 25 26 Page 2 of 46 Systematic Biology
SleepTalk: Textual DeepDream for NLP Model Interpretability

We propose SleepTalk, a technique for improving interpretability of pre-trained NLP models. Greater intepretability of black box neural networks is imperative for mitigating bias, developing trust in implemented models, and advancing intuition for better transfer learning. Thus, SleepTalk provides an approach to gain human-interpretable data on the learned representations of neurons in large neural networks. We also augment SleepTalk to be used on the adjacent task of unsupervised textual style transfer; synthesizing output text from just a content reference input and a style reference input. We assess the interpretations of SleepTalk and its behavior at different layers and qualify its resulting outputs of pre-trained NLP models. Finally, via results from SleepTalk, we suggest similarities betwen NLP neural network models and layers of the human brain’s temporal and parietal lobe, structures critical to the formation of thoughts.
Implementation of the SMS Gateway in the Flood Early Warning Information System for Village Warning and Community Information

Flooding is a national disaster that often occurs in Indonesia. Flood disasters require long-term and short-term action. In the short-term system, the government currently emphasizes state and private institutions to jointly reduce flood victims by developing a flood disaster early warning system. Therefore, this study discusses the making of flood early warning information systems by utilizing GSM communication systems as a means of communication between clients and servers. The GSM communication service used is the SMS Gateway. The SMS gateway service is used for the first time sending data from a flood detection system to a flood information system. Second, disseminating flood information to the public. In this study, the flood warning system for flood early warning works with the integration of three modes.The three systems are flood detection systems, flood alarm systems, and flood early warning information systems. Flood detection systems are built using ultrasonic sensors and rain sensors as inputs, Arduino Uno as data processors and GSM SIM900 modules as outputs. The alarm system consists of GSM SIM900 module as Input, Arduino Uno as processor and electric alarm as output. The flood early warning information system was built using a Wavecom GSM modem, and data processing using PHP, MySQL DBMS, and Gammu. The communication system between each system uses SMS data. This method as a whole began in a flood detection system that sends flood and rain data to the flood early warning information system. And the flood warning system sends alarm activation data to the alarm system. Finally, the system distributes flood information to the public via SMS Gateway. This research is expected to help the community in anticipating more victims with flood information previously obtained
Efficiency of Dynamic Coattention with Character Level Embeddings

Question answering has long been a difficult task for computers to perform well at, as it requires a deep understanding of language and nuance. However, recent developments in neural networks have yielded significant strides in how well computers are able to answer abstract questions; concepts like dynamic coattention and character level embeddings have helped machines with abstract tasks like reading comprehension. Despite these strides, training models utilizing these techniques remains cumbersome and exceedingly time consuming. Within the scope of this project, we attempt to replicate the techniques mentioned above in conjunction with each other while simultaneously removing aspects of the training process that extend its time frame unnecessarily; our goal was to optimize not only for accuracy of the model but also for the speed of training after discovering our initial baseline model took approximately 15 hours to train on a remote GPU.
Quantum Electrodynamics with a Nonmoving Dielectric Sphere: Quantizing Lorenz-Mie Scattering

We quantize the electromagnetic field in the presence of a nonmoving dielectric sphere in vacuum. The sphere is assumed to be lossless, dispersionless, isotropic, and homogeneous. The quantization is performed using normalized eigenmodes as well as plane-wave modes. We specify two useful alternative bases of normalized eigenmodes: spherical eigenmodes and scattering eigenmodes. A canonical transformation between plane-wave modes and normalized eigenmodes is derived. This formalism is employed to study the scattering of a single photon, coherent squeezed light, and two-photon states off a dielectric sphere. In the latter case we calculate the second-order correlation function of the scattered field, thereby unveiling the angular distribution of the Hong-Ou-Mandel interference for a dielectric sphere acting as a three-dimensional beam splitter. Our results are analytically derived for an arbitrary size of the dielectric sphere with a particular emphasis on the small-particle limit. This work sets the theoretical foundation for describing the quantum interaction between light and the motional, rotational and vibrational degrees of freedom of a dielectric sphere.
Hardness of Samples Is All You Need: Protecting Deep Learning Models Using Hardness of Samples

Several recent studies have shown that Deep Neural Network (DNN)-based classiﬁers are vulnerable against model extraction attacks. In model extraction attacks, an adversary exploits the target classiﬁer to create a surrogate classiﬁer imitating the target classiﬁer with respect to some criteria. In this paper, we investigate the hardness degree of samples and demonstrate that the hardness degree histogram of model extraction attacks samples is distinguishable from the hardness degree histogram of normal samples. Normal samples come from the target classiﬁer’s training data distribution. As the training process of DNN-based classiﬁers is done in several epochs, we can consider this process as a sequence of sub-classiﬁers so that each subclassiﬁer is created at the end of an epoch. We use the sequence of subclassiﬁers to calculate the hardness degree of samples. We investigate the relation between hardness degree of samples and the trust in the classiﬁer outputs. We propose Hardness-Oriented Detection Approach (HODA) to detect the sample sequences of model extraction attacks. The results demonstrate that HODA can detect the sample sequences of model extraction attacks with a high success rate by only watching 100 attack samples. We also investigate the hardness degree of adversarial examples and indicate that the hardness degree histogram of adversarial examples is distinct from the hardness degree histogram of normal samples.
Fog computing state of the art: concept and classification of platforms to support distributed computing systems

As the Internet of Things (IoT) becomes a part of our daily life, there is a rapid growth in the connected devices. A well-established approach based on cloud computing technologies cannot provide the necessary quality of service in such an environment, particularly in terms of reducing data latency. Today, fog computing technology is seen as a novel approach for processing large amounts of critical and time-sensitive data. This article reviews cloud computing technology and analyzes the prerequisites for the evolution of this approach and the emergence of the concept of fog computing. As part of an overview of the critical features of fog computing, we analyze the frequent confusion of the concepts of fog and edge computing. We provide an overview of fog computing technologies: virtualization, containerization, orchestration, scalability, parallel computing environments, as well as systematic analysis of the most popular platforms that support fog computing. As a result of the analysis, we offer two approaches to classification of the fog computing platforms: by the principle of openness/closure of components and by the three-level classification based on the provided platform functionality (Deploy-, Platform- and Ecosystem as a Service).
The Fornax 3D project: PNe populations and stellar metallicity in edge-on galaxies

Context. Extragalactic planetary nebulae (PNe) are useful distance indicators and are often used to trace the dark-matter content in external galaxies. At the same time, PNe can also be used as probes of their host galaxy stellar populations and to help understand the later stages of stellar evolution. Previous works have indicated that a specific number of PNe per stellar luminosity can vary across different galaxies and as a function of stellar-population properties, for instance increasing with decreasing stellar metallicity. Aims. In this study we further explore the importance of stellar metallicity in driving the properties of the PNe population in early-type galaxies, using three edge-on galaxies in the Fornax cluster offering a clear view into their predominantly metal-rich and metal-poor regions near the equatorial plane or both below and above it, respectively. Methods. Using very large telescope-multi unit spectroscopic explorer (VLT-MUSE) integral-field observations and dedicated PNe detection procedures, we constructed the PNe luminosity function and computed the luminosity-specific number of PNe α in both in- and off-plane regions of our edge-on systems. Results. Comparing these α values with metallicity measurements also based on the same MUSE data, we find no evidence for an increase in the specific abundance of PNe when transitioning between metal-rich and metal-poor regions. Conclusions. Our analysis highlights the importance of ensuring spatial consistency to avoid misleading results when investigating the link between PNe and their parent stellar populations, and suggest that in passively evolving systems variations in the specific number of PNe may pertain to rather extreme metallicity regimes found either in the innermost or outermost regions of galaxies.
Optimal control in linear advertising models with memory

This paper deals with a class of optimal control problems which arises in advertising models with Volterra Ornstein-Uhlenbeck process representing the product goodwill. Such choice of the model can be regarded as a stochastic modification of the classical NerloveArrow model that allows to incorporate both presence of uncertainty and empirically observed memory effects such as carryover or distributed forgetting. We present an approach to solve such optimal control problems based on an infinite dimensional lift which allows us to recover Markov properties by formulating a optimization problem equivalent to the original one in a Hilbert space. Such technique, however, requires the Volterra kernel from the forward equation to have a representation of a particular form that may be challenging to obtain in practice. We overcome this issue for Hölder continuous kernels by approximating them with Bernstein polynomials (which turn out to enjoy a simple representation of the required type) and then solving the optimal control problem for the forward process with approximated kernel instead of the original one. The approach is illustrated with simulations.
A Comprehensive Exploration of Pre-training Language Models

Recently, the development of pre-trained language models has brought natural language processing (NLP) tasks to the new state-of-the-art. In this paper we explore the efficiency of various pre-trained language models. We pre-train a list of transformer-based models with the same amount of text and the same training steps. The experimental results shows that the most improvement upon the origin BERT is adding the RNN-layer to capture more contextual information for the transformer-encoder layers.
Content Addressable Parallel Processors on a FPGA

In this short article, we report on the implementation of a Content Addressable Parallel Processor using a FPGA. While Content addressable memories have been implemented in FPGAs, to our knowledge this is the first implementation in FPGA of Caxton C. Foster's vision of parallel processing, particularly the notions of parallel write as well as the combining of output values, which are usually missing in more typical CAM implementations, such as the ones designed for network routing. The resulting CAPP is made accessible to a host computer over a USB/UART interface, using a straightforward serial protocol that is demonstrated using a Python-based driver.
Deep3DPose: Realtime Reconstruction of Arbitrarily Posed Human Bodies from Single RGB Images

We introduce an approach that accurately reconstructs 3D human poses and detailed 3D full-body geometric models from single images in realtime. The key idea of our approach is a novel end-to-end multi-task deep learning framework that uses single images to predict five outputs simultaneously: foreground segmentation mask, 2D joints positions, semantic body partitions, 3D part orientations and uv coordinates (uv map). The multi-task network architecture not only generates more visual cues for reconstruction, but also makes each individual prediction more accurate. The CNN regressor is further combined with an optimization based algorithm for accurate kinematic pose reconstruction and full-body shape modeling. We show that the realtime reconstruction reaches accurate fitting that has not been seen before, especially for wild images. We demonstrate the results of our realtime 3D pose and human body reconstruction system on various challenging in-the-wild videos. We show the system advances the frontier of 3D human body and pose reconstruction from single images by quantitative evaluations and comparisons with state-of-the-art methods.
Kernel Clustering with Sigmoid Regularization for Efficient Segmentation of Sequential Data

The segmentation of sequential data can be formulated as a clustering problem, where the data samples are grouped into non-overlapping clusters with the constraint that all members of each cluster are in a successive order. A popular algorithm for optimally solving this problem is dynamic programming (DP), which has quadratic computation and memory requirements. Given that sequences in practice are too long, this algorithm is not a practical approach. Although many heuristic algorithms have been proposed to approximate the optimal segmentation, they have no guarantee on the quality of their solutions. In this paper, we take a differentiable approach to alleviate the aforementioned issues. First, we introduce a novel sigmoid-based regularization to smoothly approximate the constraints. Combining it with objective of the balanced kernel clustering, we formulate a differentiable model termed Kernel clustering with sigmoid-based regularization (KCSR), where the gradient-based algorithm can be exploited to obtain the optimal segmentation. Second, we develop a stochastic variant of the proposed model. By using the stochastic gradient descent algorithm, which has much lower time and space complexities, for optimization, the second model can perform segmentation on overlong data sequences. Finally, for simultaneously segmenting multiple data sequences, we slightly modify the sigmoid-based regularization to further introduce an extended variant of the proposed model. Through extensive experiments on various types of data sequences performances of our models are evaluated and compared with those of the existing methods. The experimental results validate advantages of the proposed models. Our Matlab source code is available on github.
A stochastic linearized proximal method of multipliers for convex stochastic optimization with expectation constraints

This paper considers the problem of minimizing a convex expectation function with a set of inequality convex expectation constraints. We present a computable stochastic approximation type algorithm, namely the stochastic linearized proximal method of multipliers, to solve this convex stochastic optimization problem. This algorithm can be roughly viewed as a hybrid of stochastic approximation and the traditional proximal method of multipliers. Under mild conditions, we show that this algorithm exhibits O ( K − 1 / 2 ) expected convergence rates for both objective reduction and constraint violation if parameters in the algorithm are properly chosen, where K denotes the number of iterations. Moreover, we show that, with high probability, the algorithm has O (log( K ) K − 1 / 2 ) constraint violation bound and O (log 3 / 2 ( K ) K − 1 / 2 ) objective bound. Some preliminary numerical results demonstrate the performance of the proposed algorithm.
Off-Policy Reinforcement Learning with Delayed Rewards

We study deep reinforcement learning (RL) algorithms with delayed rewards. In many real-world tasks, instant rewards are often not readily accessible or even defined immediately after the agent performs actions. In this work, we first formally define the environment with delayed rewards and discuss the challenges raised due to the non-Markovian nature of such environments. Then, we introduce a general off-policy RL framework with a new Q-function formulation that can handle the delayed rewards with theoretical convergence guarantees. For practical tasks with high dimensional state spaces, we further introduce the HC-decomposition rule of the Q-function in our framework which naturally leads to an approximation scheme that helps boost the training efficiency and stability. We finally conduct extensive experiments to demonstrate the superior performance of our algorithms over the existing work and their variants.
A New Channel Estimation Strategy in Intelligent Reflecting Surface Assisted Networks

Channel estimation is the main hurdle to reaping the benefits promised by the intelligent reflecting surface (IRS), due to its absence of ability to transmit/receive pilot signals as well as the huge number of channel coefficients associated with its reflecting elements. Recently, a breakthrough was made in reducing the channel estimation overhead by revealing that the IRS-BS (base station) channels are common in the cascaded user-IRS-BS channels of all the users, and if the cascaded channel of one typical user is estimated, the other users' cascaded channels can be estimated very quickly based on their correlation with the typical user's channel [1]. One limitation of this strategy, however, is the waste of user energy, because many users need to keep silent when the typical user's channel is estimated. In this paper, we reveal another correlation hidden in the cascaded user-IRS-BS channels by observing that the user-IRS channel is common in all the cascaded channels from users to each BS antenna as well. Building upon this finding, we propose a novel two-phase channel estimation protocol in the uplink communication. Specifically, in Phase I, the correlation coefficients between the channels of a typical BS antenna and those of the other antennas are estimated; while in Phase II, the cascaded channel of the typical antenna is estimated. In particular, all the users can transmit throughput Phase I and Phase II. Under this strategy, it is theoretically shown that the minimum number of time instants required for perfect channel estimation is the same as that of the aforementioned strategy in the ideal case without BS noise. Then, in the case with BS noise, we show by simulation that the channel estimation error of our proposed scheme is significantly reduced thanks to the full exploitation of the user energy.
DFT Study on the Mechanism of the Electrochemical Reduction of CO2 Catalyzed by Cobalt Porphyrins

The electrochemical reduction of CO2 is a promising way to store renewable energy in fuels or other chemicals. However, the high overpotential and low efficiency of the reaction hinder the development of the area. More work is needed on the investigation of the mechanism in order to obtain new insights into developing efficient catalysts. We report here a density functional theory (DFT) study of the electrochemical reduction of CO2 on cobalt porphyrin. The CO2– anion adduct is demonstrated to be the key intermediate formed only when the cobalt center of the complex is in the CoI oxidation state. We find that formic acid can be produced as minor product through a [Co(P)–(OCHO)] intermediate, while CO is the main product through a decoupled proton–electron transfer. CH4 is produced as minor product from subsequent CO reduction by concerted proton-coupled electron transfers assumed for each electrochemical step. Our theoretical interpretations are consistent with the experimental results presented in our rec.