Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid mmWave-THz Networks with User-Centric Clustering

This paper investigates a user-centric clustering model for a hybrid network comprising both millimeter-wave (mmWave) and terahertz (THz) base stations (BSs). Based on the proposed model, a user can choose to be cooperatively served by multiple mmWave or multiple THz BSs depending on their link quality. Besides, to maximize the cooperation gains, the serving clusters are dynamically adjusted to user's channel conditions pertaining to the different properties of the mmWave and THz networks. Finally, we evaluate the coverage probability of the hybrid network by using stochastic geometric tools and validate the analysis through numerical simulations.
Assured Learning-Based Optimal Control subject to Timed Temporal Logic Constraints

We develop an algorithm for the optimal control of systems governed by unknown, nonlinear dynamics, to deliver tasks expressed as timed temporal logic constraints. The algorithm first computes a sequence of points in the operating environment, along with associated time stamps, so that the system completes its task if it follows the sequence. For the algorithm’s second step, we develop a data-driven, on-the-fly control mechanism that learns how to transition from a point in the sequence to the next within a pre-specified time horizon. This algorithm accounts for the unknown dynamics, any unsafe zones in the environment and additional optimality criteria. We show that, after a finite period of data gathering, the resulting controller guarantees that the system indeed follows the sequence of points, leading to the satisfaction of the task.
Perfect Secrecy in the Bounded Storage Model

In this paper, we propose a new provably secure cryptosystem for two party communication that provides security in the face of new technological breakthroughs. Most of the practical cryptosystems in use today can be breached in the future with new sophisticated methods. This jeopardizes the security of older but highly confidential messages. Our protocol is based on the bounded storage model first introduced in [1]. The protocol is secure as long as there is bound on the storage, however large it may be. We also suggest methods to extend the protocol to unbounded storage models where access to adversary is limited. Our protocol is a substantial improvement over previously known protocols and uses short key and optimal number of public random bits size of which is independent of message length. The smaller and constant length of key and public random string makes the scheme more practical. The protocol generates key using elements of the additive group $\mathbb{Z}_{\mathrm{n}}$. Our protocol is very generalized and the protocol in [1] is a special case of our protocol. Our protocol is a step forward in making provably secure cryptosystems practical. An important open problem raised in [2] was designing an algorithm with short key and size of public random string $O(\mathcal{B})$ where $\mathcal{B}$ bounds the storage of adversary. Our protocol satisfies the conditions and is easy to implement.
A Novel Image Classification Method Based on Multi-layer Dictionary Learning

Deep learning is widely used in image classification due to the huge potential of multi-layer training. Combining deep learning with traditional dictionary learning has become a hot topic. However, the existing methods update dictionary learning and classifier as two independent modules, which limits the classification performance. In this paper, a novel image classification method based on multi-layer dictionary learning is proposed to meet this challenge. First, multi-layer training is introduced to extract the multi-layer nonlinear features of image data. Then, the discriminant label constraint term is designed to optimize the dictionary and classifier synchronously. The proposed method improves classification accuracy via enhancing the coding coefficient features and screening the beneficial classification feature. Finally, we examine the performance of our model on two real-world image data sets. The correctness and effectiveness of the proposed model are verified through convergence, accuracy and parameter sensitivity.
Resilient Homomorphic Encryption Scheme for Cyber-Physical Systems

In this paper, a resilient homomorphic encryption scheme for cyber-physical systems (CPS) is presented. The proposed approach allows the calculation process of feedback controllers to be carried out in an encrypted environment. Moreover, the proposed approach is able to neutralize the effect of attacks injected in the encrypted signals sent over the network, so that the controller can still get the true sensor information. We shall first show that the proposed resilient homomorphic encryption scheme can calculate the multiplication of a matrix with a vector directly based on the ciphertexts, whose result after decryption is the same as the matrix-vector product got based on plaintexts. By transforming the control law into a matrix-vector product, the evaluation of the controller can be carried out with ciphertexts. As a result, the resilient homomorphic encryption scheme ensures the confidentiality of both the signals sent over the network and the controller parameters. In addition, a CPS utilizing the proposed encryption scheme is resilient to additive attacks, as long as the attacks are inside the resilience range. The proposed approach is illustrated through the well-established quadruple-tank benchmark process.
Adaptive Beam Alignment in Mm-Wave Networks: A Deep Variational Autoencoder Architecture

This paper proposes a dual timescale learning and adaptation framework to learn a probabilistic model of beam dynamics and concurrently exploit this model to design adaptive beam-training with low overhead: on a long timescale, a deep recurrent variational autoencoder (DR-VAE) uses noisy beam-training observations to learn a probabilistic model of beam dynamics; on a short timescale, an adaptive beam-training procedure is formulated as a partially observable Markov decision process and optimized using point-based value iteration by leveraging beam-training feedback and probabilistic predictions of the strongest beam pair provided by the DR-VAE. In turn, beam-training observations are used to refine the DR-VAE via stochastic gradient ascent in a continuous process of learning and adaptation. It is shown that the proposed DR-VAE learning framework learns accurate beam dynamics and, as learning progresses, the training overhead decreases and the spectral efficiency increases. Moreover, the proposed dual timescale approach achieves near-optimal spectral efficiency, with a gain of 85% over a policy that scans exhaustively over the dominant beam pairs, and of 18% over a state-of-the-art POMDP policy.
IGCN: Infected Graph Convolutional Network based Source Identification

Source identification has a wide range of applications in daily life, including locating the rumor source in online social networks and finding origins of a rolling blackout in smart grids. Despite great success over the past decade, most prior arts are proposed based an assumption that the underlying propagation model is known in advance. However, this assumption may be impracticable on real scenarios, since it is usually difficult to acquire the actual underlying propagation model. To avoid this limitation, in this paper, we propose the Infected Graph Convolutional Network (IGCN) layer by combining infection network with GCN (Graph Convolutional Network) layers to locate the rumor source without prior knowledge of underlying propagation model. For the first time, we define the problem of source identification as a special graph classification problem with source node as the label. By introducing the feature update method of GCN layer with the idea of attention, we build an IGCN model to adapt the infection networks such that the prediction accuracy on the source is improved under model independent scenarios. We conduct experiments on several real datasets and the results show the superiority of IGCN model to baseline algorlthms.
Analog Eigen-Beamforming for mmWave Systems: Performance under practical constraints

Many millimeter wave systems embed phased array antennas to overcome the large path loss that is experienced at such high frequencies. Digital Fourier Transform based beam-forming (DFT-BF) is often implemented as a low-complexity and very limited feedback technique. Although DFT-BF is optimal in pure Line-of-Sight scenarios, it is substantially outperformed in richer scattering environments by more advanced approaches such as Eigenvector-based beamforming (Eigen-BF). The latter is harder to implement in practical scenarios due to its computational complexity and amount of feedback. In this paper, we propose several solutions to alleviate those drawbacks and analyze various performance-complexity trade-offs in the perspective of a practical usage of Eigen-BF solutions.
FPGA Placement Optimization with Deep Reinforcement Learning

The Simulated annealing algorithm has been widely used in FPGA placement. In this paper, we use deep reinforcement learning to enhance the simulation annealing algorithm. We use the Deep Q-Networks based on graph convolution to implement deep reinforcement learning algorithm instead of random method to select the moving block when placer swaps blocks. Experiments show that our method can get a better placement faster than the original VTR in early stage of the placement process.
A 0.1V Input Energy Harvesting Charge Pump with Dynamic Gate Biasing and Capacitance Scaling

This paper reports the investigation in the pumping capacitance to the charge pump performance. The work proposed a capacitance optimization technique on cascading charge pump which achieves higher power conversion efficiency (PCE) with a lower capacitance. This paper also reports a cross-couple charge pump (CCCP) with 40% (PCE) at 100mV. It features a dynamic gate-bias technique which can improve forward current drivability and reduce reverse-leakage current. The circuit is simulated in 65-nm CMOS technology.
Distributed DRL-based Resource Allocation for Multicast D2D Communications

Device-to-device (D2D) communication is one of the promising solutions to improve spectrum efficiency and alleviate the mobile traffic explosion. However, interference mitigation and resource allocation in the underlying cellular network is a challenging task. In this paper, we propose a distributed deep reinforcement learning (DRL) based scheme to solve the interference mitigation and resource allocation problem. According to the channel status, each cellular user (CU) and D2D transmitter (D2D TX) will determine the appropriate reused channel and transmit power to maximize the system throughput. We propose a distributed DRL scheme and integrate two hotbooting algorithms into the scheme to improve the system throughput at the early stage of training. Simulation results show that the proposed distributed DRL with hotbooting outperforms the baselines regarding running time, message overhead, and throughput.
Multi-page Menu Recommendation in Cascade Model with Externalities

In this paper, we consider a variant of the cascade model of customer behavior, where the customer browses through a multi-page menu, scanning each page from top to the bottom predominantly. Each page is assigned items belonging to a specific class out of a set of classes. He/she adopts the first most attractive content, which generates some revenue. We aim at maximizing the total revenue by finding an optimal index-based policy for ranking the content when the customer preferences and patience levels are known. When we have no prior information about the customer, we design the Online Greedy Algorithm (OGA) which we prove to be asymptotically converging to the optimal solution with probability one. We also provide high probability finite-time convergence bounds for the same.
Rapid Decision-Making Tool for Powertrain Sizing

In the early stages of electric vehicle design, the decision-making process concerns the component sizing and trade-offs between various configurations to achieve or update initial target specifications. Motorcycle performance and range are more sensitive to added mass due to configuration selection. Hence, there is a need to quantify the high-level implications of different powertrain configurations and component selection, affecting the total vehicle mass, vehicle range, vehicle acceleration performance. An Excel spreadsheet-based model is described in this paper to aid decision-makers at the early stages of vehicle design. The tool is capable of sizing battery pack, inverter, traction, motor and gearbox, and making performance estimations of a powertrain configuration and component selection. The tool is validated against the listed ranges of two motorcycles and predicts energy usage with an error of 5% for low-speed drive cycles.
A Digital Random Number Generator Based on Regular Sampling of Double Scroll Chaos

This paper presents the design of a novel digital random number generator (RNG) based on the distribution of regular samples from a double scroll chaotic system. In this study, a double scroll chaotic system is designed by using the Euler approximation and the nonlinearity feature of sawtooth wave in a 3rd order ordinary differential equation (ODE). By examining the distribution of the values of chaotic state variables, it is demonstrated that the distribution has at least two regions and a binary sequence can be obtained by exploiting these two regions. The XOR is used to combine the resulting binary sequences obtained from these two regions, and thus, the RNG is designed. The RNG output satisfies all statistical randomness tests in the NIST 800–22 package without post-processing. The proposed RNG is implemented using Verilog hardware description language and prototyped on a KCU105 Evaluation Board Xilinx Ultrascale Field Programmable Gate Array (FPGA) with less than 1% LUT and Flip-Flop utilization and data throughput of up to 1.3125 Mbps.
On Preamble-based FBMC/OQAM Highly Frequency Selective Channel Estimation Without Guard Symbols

Intrinsic interference is known to present a challenge for signal processing tasks in systems employing offset quadrature amplitude modulation-based filter bank multicarrier (FBMC/OQAM) transmission. In preamble-based channel estimation, in particular, this otherwise called self interference will corrupt the pilot symbols, thus preventing accurate estimation, unless special means, such as inserting sufficiently many null guard FBMC symbols between the training and the information part, are employed, with a consequent loss in spectral efficiency. Iterative procedures that alternatingly estimate the channel and the interfering data symbols have been proposed to tackle this problem, and they are almost exclusively intended for channels of relatively (to the FBMC symbol duration) low delay spread. In this paper, the problem of preamble-based channel estimation without guard symbols is re-visited, with no simplifying assumption on the relative frequency selectivity of the channel. An optimal (in the mean squared error (MSE) sense) preamble in the presence of data interference is designed, assuming the availability of the second-order statistics of the channel and the signal-to-noise ratio. The effect of adopting this interference-aware preamble in an iterative estimation/detection procedure is assessed via simulations with realistic channels. A significant gain in the detection performance is demonstrated compared to the use of the preamble that is only optimal in the absence of interference.
A Modern Encyclopedia

In striking contrast to Sam Johnson's erratic dictionary commented on in the last issue of the Bulletin is the Encyclopedia of the Social Sciences whose second volume appeared during October (Macmillan). The book covers parts of the alphabet letters “A” and “B” — beginning with an article on alliances by Professor Sidney B. Fay of Harvard University and ending with brigandage, by Carleton Beals.
Merging pruning and neuroevolution: towards robust and efficient controllers for modular soft robots

Abstract Artificial neural networks (ANNs) can be employed as controllers for robotic agents. Their structure is often complex, with many neurons and connections, especially when the robots have many sensors and actuators distributed across their bodies and/or when high expressive power is desirable. Pruning (removing neurons or connections) reduces the complexity of the ANN, thus increasing its energy efficiency, and has been reported to improve the generalization capability, in some cases. In addition, it is well-known that pruning in biological neural networks plays a fundamental role in the development of brains and their ability to learn. In this study, we consider the evolutionary optimization of neural controllers for the case study of Voxel-based soft robots, a kind of modular, bio-inspired soft robots, applying pruning during fitness evaluation. For a locomotion task, and for centralized as well as distributed controllers, we experimentally characterize the effect of different forms of pruning on after-pruning effectiveness, life-long effectiveness, adaptability to new terrains, and behavior. We find that incorporating some forms of pruning in neuroevolution leads to almost equally effective controllers as those evolved without pruning, with the benefit of higher robustness to pruning. We also observe occasional improvements in generalization ability.
ADSORPTION FOR THE REMOVAL OF CHROMIUM USING FLY ASH

Among various organic and inorganic pollutants, heavy metal ions are very toxic and carcinogenic in nature. The presence of heavy metals in the aquatic environment has been of great concern because of their toxicity at lower concentrations. In India, several other industrial effluents contribute to contamination of river and groundwater by Cr. Chromium in excess amounts can be toxic especially the hexavalent form. Fly ash is one of the most abundant industrial waste materials and it can be used for the removal of toxic metal ions. In this study, adsorption of Chromium (VI) was investigated by fly ash from aqueous solutions. Fly ash samples were collected from Shri Ram Fertilizers and Chemicals Ltd. (SRFC) Kota, Rajasthan. Physico – chemical analysis was performed of collected fly ash samples. The sorption of Cr (VI) ions by batch method is carried out. U.V. Visible spectrophotometric method was used for Chromium (VI) determination. The study investigates the effect of initial metal ion concentration, adsorbent doses and pH of solution. The result indicate that the adsorption increases with increasing amount of fly ash and initial chromium concentration. Adsorption is maximum on pH 2 and decreases with increasing pH. Various adsorption kinetic models were tested in understanding the adsorption kinetics and it was found that the adsorption was controlled by intra-particle diffusion model.
Chapter VI: School Policy Culture and State Decentralization

The role of the state in shaping school policy has been central to American education because the U.S. Constitution reserves authority over education to the states and because Dillon's Rule still lives. (Dillon's Rule refers to a nineteenth century judicial opinion that characterizes the basic tie between state and local governments. In essence, the state can create and destroy all local units and it can grant or withhold authority for them to act.) Yet, how one approaches the study of the role of the state for analytical purposes is not so clear. In the last fifty years, political scientists have studied the state's role in policy making from such perspectives as the legal basis of authority, the institutionalization of that authority, the group pressure matrix within which policy is made, the impact of environmental and structural aspects of states upon their policy outcomes, and, most recendy, state policy within an intergovernmental web of relations, This chapter departs in two ways from these analytical approaches and from the traditional data on the analysis of school policy. First, it explor~ the extent of differences among the fifty states in the degree of centralization of control over local school policy by the state government. This is an "output" rarely developed except in case study fashion, 1 even though the authoritative distribution of authority would seem a significant variable shaping
Repoliticising the future of work: automation and the end of techno-optimism

ABSTRACT This review article of Aaron Benanav’s Automation and the Future of Work (2020) and Jason Smith’s Smart Machines and Service Work (2020) reads both works as an effort to repoliticise the question of unemployment, which has too often been ascribed to technological innovation, especially by proponents of automation theory. It places their works within current debates surrounding the question of automation and its political reverberations across the political spectrum. In the end, we show that the shortcomings of automation discourse reside in their economic analyses of the future of work and employment and that automation theorists encourage a depoliticisation of the question of employment through technocracy, while Benanav and Smith open the way for thinking about the future of work as a collective and social endeavour.
Four Dammarane Triterpenes and Their Inhibitory Properties Against Eight Receptor Tyrosine Kinases

 In recent years, tyrosine kinases (TKs) have been the target to combat cancers, and most of the developed inhibitors are of synthetic origin. Natural compounds that have the properties as the TK’s inhibitors are very limited. This paper described the isolation of a new dammarane triterpene from the tree bark of Sandoricum koetjape, along with three known related dammaranes from the damar resin of Shorea javanica, as well as their inhibitory properties against eight receptor TKs (RTKs: EGFR, HER2, HER4, IGF1R, InsR, KDR, PDGFR, and PDGFR). Based on the NMR and mass spectral data the new compound was identified as (12,20S)-12,20dihydroxy-3,4-seco-dammaran-4,24-dien-3-oic acid (12-hydroxydammarenolic acid) (1), while the three known compounds were identified as (20S)-20-hydroxy-3,4-seco-dammaran-4,24-dien-3-oic acid (dammarenolic acid) (2), (3,20S)-3,20-dihydroxydammaran-24-ene (3), and (20S)-3-oxo-20-hydroxydammaran-24-ene (4). The tyrosine kinase assay of the four compounds resulted only 1 and 2 at concentration of 10 M that had weak activity against EGFR and InsR, with their % inhibitory were 30%, 27% (1), 45%, and 32% (2), respectively. The results suggested that the presence of a linear carboxylic acid group in both compounds could be of significance to the inhibitory properties against the two RTKs.