Miracle-dz/UniFAR-FTUs huggingface dataset:
A 1.1-pJ/b 8-to-16-Gb/s Receiver With Stochastic CTLE Adaptation

This brief presents an 8-to-16-Gb/s referenceless receiver with a stochastic continuous-time linear equalizer (CTLE) adaptation. The proposed stochastic CTLE gain selector (SCGS) achieves a maximum horizontal eye margin and avoids sub-optimal settling by utilizing sequential edge and data samples. The proposed SCGS detects the optimum CTLE coefficient with the weighted summation of the histograms obtained under various data patterns and channel conditions. The stochastic CTLE adaptation shares the deserialized edge and data samples used for the CDR. Therefore, it does not require additional hardware in the analog domain, achieving low power consumption. The golden weight of the SCGS is obtained through an epsilon-constraint weight searching algorithm. A prototype chip fabricated in 28-nm CMOS technology occupies an active area of 0.029 mm2. The measured CTLE adaptation behavior shows that the maximum eye width is achieved with the proposed SCGS. The prototype chip achieves BER over a channel with 14-dB loss at 8 GHz and consumes 17.7 mW at 16 Gb/s, exhibiting a power efficiency of 1.1 pJ/b.
GAMMA-RAY BURSTS: TEMPORAL SCALES AND THE BULK LORENTZ FACTOR

For a sample of Swift and Fermi gamma-ray bursts, we show that the minimum variability timescale and the spectral lag of the prompt emission is related to the bulk Lorentz factor in a complex manner. For small Γ's, the variability timescale exhibits a shallow (plateau) region. For large Γ's, the variability timescale declines steeply as a function of Γ ( δ T ∝ Γ − 4.05 ± 0.64 ? > ). Evidence is also presented for an intriguing correlation between the peak times, tp, of the afterglow emission and the prompt emission variability timescale.
Policy learning and change during crisis: COVID‐19 policy responses across six states

Abstract Whereas policy change is often characterized as a gradual and incremental process, effective crisis response necessitates that organizations adapt to evolving problems in near real time. Nowhere is this dynamic more evident than in the case of COVID‐19, which forced subnational governments to constantly adjust and recalibrate public health and disease mitigation measures in the face of changing patterns of viral transmission and the emergence of new information. This study assesses (a) the extent to which subnational policies changed over the course of the pandemic; (b) whether these changes are emblematic of policy learning; and (c) the drivers of these changes, namely changing political and public health conditions. Using a novel dataset analyzing each policy's content, including its timing of enactment, substantive focus, stringency, and similar variables, results indicate the pandemic response varied significantly across states. The states examined were responsive to both changing public health and political conditions. This study identifies patterns of preemptive policy learning, which denotes learning in anticipation of an emerging hazard. In doing so, the study provides important insights into the dynamics of policy learning and change during disaster.
The role of the reflection fraction in constraining black hole spin

In many active galaxies, the X-ray reflection features from the innermost regions of the accretion disc are relativistically distorted. This distortion allows us to measure parameters of the black hole such as its spin. The ratio in flux between the direct and the reflected radiation, the so-called reflection fraction, is determined directly from the geometry and location of primary source of radiation. We calculate the reflection fraction in the lamp post geometry in order to determine its maximal possible value for a given value of black hole spin. We show that high reflection fractions in excess of 2 are only possible for rapidly rotating black holes, suggesting that the high spin sources produce the strongest relativistic reflection features. Using simulations we show that taking this constraint into account does significantly improve the determination of the spin values. We make software routines for the most popular X-ray data analysis packages available that incorporate these additional constraints.
Using deflation in the pole assignment problem with output feedback

A direct algorithm is suggested for the computation of a linear output feedback for a multi input, multi output system such that the resultant closed-loop matrix has eigenvalues that include a specified set of eigenvalues. The algorithm uses deflation based on unitary similarity transformations. Thus researchers hope the algorithm is numerically stable; however, this has not been proven as yet.
On the Deployment of Clustered Power Beacons in Random Wireless Powered Communication

Wireless powered communication (WPC) is able to provide wireless devices (WDs) practically infinite energy by using the radio frequency energy harvesting technique. The electricity of WDs for the wireless information transmission (WIT) is converted from the received electromagnetic signals of wireless energy transfer (WET). Power beacon (PB) is usually deployed as the dedicated energy source in WPC networks and the performance of WET is sensitive to the path-loss effect. Thus, both WET and WIT are significantly influenced by the locations of the PBs. To enable efficient transmission in WPC, this paper analytically investigates the impact of the PB deployment strategies on the WPC performance when WDs are clustered around the access points (APs). Specifically, we propose the PB deployment strategy based on the truncated Poisson cluster process (PCP) model. The distribution of PBs is correlated to the locations of WDs by choosing APs as their common parent points. Then, we derive the semi-closed form expressions for the energy outage probability and the transmission success probability. We further present the expressions of the meta distributions of WET and WIT, and investigate the proportion of the WDs in one cluster that achieves successful transmission while satisfying the reliability constraint. Moreover, the distance distribution between the WD and the intra-cluster PBs is analytically derived for the proposed correlated PCP model. By comparing the performance with other deployment strategies, it is shown that the preference of the PB deployment strategy depends on the PB density. The analytical and numerical results can provide insightful guidelines for designing practical WPC systems.
Privacy-Enhanced Intrusion Detection and Defense for Cyber-Physical Systems: A Deep Reinforcement Learning Approach

Cyber-physical systems (CPSs) will play an important role in future real-world applications through the deep integration of computing, communication, and control technologies. CPSs are increasingly deployed in critical infrastructure, industry, and homes to achieve a smart grid, smart transportation, and smart healthcare and to bring many benefits to citizens, businesses, and governments. However, the openness and complexity brought by network and wireless communication technology, as well as the intelligence and dynamic of network intrusions make CPS more vulnerable to network intrusions and bring more serious threats to human life, enterprise productivity, and national security. Therefore, intrusion detection and defense in CPS have attracted considerable attention and have become a fundamental aspect of CPS security. However, a new challenging problem arises: how to improve the efficiency and accuracy of intrusion detection while protecting user privacy during the intrusion detection process. To address this challenge, we propose a deep reinforcement learning-based privacy-enhanced intrusion detection and defense mechanism (PIDD) for CPS. The PIDD is composed of three modules: privacy-enhanced topology graphs generation module, graph convolutional networks-based user evaluation module, and the deep reinforcement learning-based intruder identification and handling module. The experimental results show that the proposed PIDD achieves excellent performance in intrusion detection accuracy, intrusion defense percentage, and privacy protection.
Nanocone-Array-Based Platinum-Iridium Oxide Neural Microelectrodes: Structure, Electrochemistry, Durability and Biocompatibility Study

Neural interfaces provide a window for bio-signal modulation and recording with the assistance of neural microelectrodes. However, shrinking the size of electrodes results in high electrochemical impedance and low capacitance, thus limiting the stimulation/recording efficiency. In order to achieve critical stability and low power consumption, here, nanocone-shaped platinum (Pt) with an extensive surface area is proposed as an adhesive layer on a bare Pt substrate, followed by the deposition of a thin layer of iridium oxide (IrOx) to fabricate high-performance nanocone-array-based Pt-IrOx neural microelectrodes (200 μm in diameter). A uniform nanocone-shaped Pt with significant roughness is created via controlling the ratio of NH4+ and Pt4+ ions in the electrolyte, which can be widely applicable for batch production on multichannel flexible microelectrode arrays (fMEAs) and various substrates with different dimensions. The Pt-IrOx nanocomposite-coated microelectrode presents a significantly low impedance down to 0.72 ± 0.04 Ω cm2 at 1 kHz (reduction of ~92.95%). The cathodic charge storage capacity (CSCc) and charge injection capacity (CIC) reaches up to 52.44 ± 2.53 mC cm−2 and 4.39 ± 0.36 mC cm−2, respectively. Moreover, superior chronic stability and biocompatibility are also observed. The modified microelectrodes significantly enhance the adhesion of microglia, the major immune cells in the central nervous system. Therefore, such a coating strategy presents great potential for biomedical and other practical applications.
Impact of antagonist peptides and chelators on the diagnostic performance of PET/CT using gallium-68–labeled somatostatin receptor antagonists

Objective: Different SSTR2 antagonists have been developed. This study aims to evaluate the impact of different peptides and chelators on the diagnostic performance of SSTR2 antagonists in well-differentiated NETs. Methods: In this prospective study, participants were equally randomized into 2 arms: arm A, participants would undergo a whole-body 68Ga-NODAGA-LM3 PET/CT scan on the first day and 68Ga-DOTA-LM3 PET/CT scan on the second day; arm B, participants would undergo a whole-body 68Ga-NODAGA-LM3 PET/CT scan on the first day and 68Ga-NODAGA-JR11 PET/CT scan on the second day. Biodistribution in normal organs, lesion detection ability, and tumor uptakes were compared within each arm. Results: A total of 40 participants (age, 49.5 ± 13.4, 21 men), 20 in each arm, were recruited in the study. In arm A, 68Ga-DOTA-LM3 showed lower background. However, the lesion detection ability (overall lesion detected, 445 vs 548; P = .005) and the lesion uptake (overall lesions SUVmax, 19.8 ± 17.2 vs 35.3 ± 28.8; P < .001) was significantly lower than those of 68Ga-NODAGA-LM3. In arm B, both 68Ga-NODAGA-LM3 and 68Ga-NODAGA-JR11 showed similar biodistribution and lesion uptake (SUVmax, 28.5 ± 23.8 vs 25.0 ± 20.0; P < .001) despite minor differences. The lesion detection ability was the same between these 2 tracers (overall lesion detected, 503 vs 503). Conclusions: The diagnostic performance of SSTR2 antagonists was sensitive to chelators. Both 68Ga-NODAGA-LM3 and 68Ga-NODAGA-JR11 outperformed 68Ga-DOTA-LM3 with higher lesion uptake and detection ability, of which 68Ga-NODAGA-LM3 had marginally but significantly higher lesion uptake.
LiDAR Intensity Completion: Fully Exploiting the Message from LiDAR Sensors

Light Detection and Ranging (LiDAR) systems are novel sensors that provide robust distance and reflection strength by active pulsed laser beams. They have significant advantages over visual cameras by providing active depth and intensity measurements that are robust to ambient illumination. However, the systemsstill pay limited attention to intensity measurements since the output intensity maps of LiDAR sensors are different from conventional cameras and are too sparse. In this work, we propose exploiting the information from both intensity and depth measurements simultaneously to complete the LiDAR intensity maps. With the completed intensity maps, mature computer vision techniques can work well on the LiDAR data without any specific adjustment. We propose an end-to-end convolutional neural network named LiDAR-Net to jointly complete the sparse intensity and depth measurements by exploiting their correlations. For network training, an intensity fusion method is proposed to generate the ground truth. Experiment results indicate that intensity–depth fusion can benefit the task and improve performance. We further apply an off-the-shelf object (lane) segmentation algorithm to the completed intensity maps, which delivers consistent robust to ambient illumination performance. We believe that the intensity completion method allows LiDAR sensors to cope with a broader range of practice applications.
A Parallel Classification Model for Marine Mammal Sounds Based on Multi-Dimensional Feature Extraction and Data Augmentation

Due to the poor visibility of the deep-sea environment, acoustic signals are often collected and analyzed to explore the behavior of marine species. With the progress of underwater signal-acquisition technology, the amount of acoustic data obtained from the ocean has exceeded the limit that human can process manually, so designing efficient marine-mammal classification algorithms has become a research hotspot. In this paper, we design a classification model based on a multi-channel parallel structure, which can process multi-dimensional acoustic features extracted from audio samples, and fuse the prediction results of different channels through a trainable full connection layer. It uses transfer learning to obtain faster convergence speed, and introduces data augmentation to improve the classification accuracy. The k-fold cross-validation method was used to segment the data set to comprehensively evaluate the prediction accuracy and robustness of the model. The evaluation results showed that the model can achieve a mean accuracy of 95.21% while maintaining a standard deviation of 0.65%. There was excellent consistency in performance over multiple tests.
Cosmic acceleration from modified gravity with Palatini formalism

We study new FRW type cosmological models of modified gravity treated on the background of Palatini approach. These models are generalization of Einstein gravity by the presence of a scalar field non-minimally coupled to the curvature. The models employ Starobinsky's term in the Lagrangian and dust matter. Therefore, as a by-product, an exhausted cosmological analysis of general relativity amended by quadratic term is presented. We investigate dynamics of our models, confront them with the currently available astrophysical data as well as against LCDM model. We have used the dynamical system methods in order to investigate dynamics of the models. It reveals the presence of a final sudden singularity. Fitting free parameters we have demonstrated by statistical analysis that this class of models is in a very good agreement with the data (including CMB measurements) as well as with the standard LCDM model predictions. One has to use statefinder diagnostic in order to discriminate among them. Therefore Bayesian methods of model selection have been employed in order to indicate preferred model. Only in the light of CMB data the concordance model remains invincible.
Effect of addition of carbon and probiotics on water quality, production performance, and health of catfish (Clarias gariepinus) in biofloc systems

This study aimed to evaluate the use of organic carbon and probiotics on water quality, production performance, and haematological status in sangkuriang catfish. This experiment used a completely randomized design with four treatments: addition of organic carbon (C), the addition of probiotics (P), the addition of organic carbon and probiotics (CP)
Japonamides A and B, Two New Cyclohexadepsipeptides from the Marine-Sponge-Derived Fungus Aspergillus japonicus and Their Synergistic Antifungal Activities

Two new cyclohexadepsipeptides japonamides A (1) and B (2) were isolated from the ethyl acetate extract of a marine-sponge-derived fungus Aspergillus japonicus based on molecular networking. Their structures were elucidated by comprehensive spectral analysis and their absolute configurations were confirmed by Marfey’s method. Compounds 1 and 2 showed no antifungal activities against Candida albicans SC5314 measured by the broth microdilution method but exhibited prominent synergistic antifungal activities in combination with fluconazole, ketoconazole, or rapamycin. The Minimum inhibitory concentrations (MICs) of rapamycin, fluconazole, and ketoconazole were significantly decreased from 0.5 to 0.002 μM, from 0.25 to 0.063 μM, and from 0.016 to 0.002 μM, in the presence of compounds 1 or 2 at 3.125 μM, 12.5 μM, and 6.25 μM, respectively. Surprisingly, the combination of compounds 1 or 2 with rapamycin showed a strong synergistic effect, with fractional inhibitory concentration index (FICI) values of 0.03.
YOUNG STARS AND IONIZED NEBULAE IN M83: COMPARING CHEMICAL ABUNDANCES AT HIGH METALLICITY

We present spectra of 14 A-type supergiants in the metal-rich spiral galaxy M83. We derive stellar parameters and metallicities and measure a spectroscopic distance modulus (4.9 ± 0.2 Mpc), in agreement with other methods. We use the stellar characteristic metallicity of M83 and other systems to discuss a version of the galaxy mass–metallicity relation that is independent of the analysis of nebular emission lines and the associated systematic uncertainties. We reproduce the radial metallicity gradient of M83, which flattens at large radii, with a chemical evolution model, constraining gas inflow and outflow processes. We carry out a comparative analysis of the metallicities we derive from the stellar spectra and published H ii region line fluxes, utilizing both the direct, -based method and different strong-line abundance diagnostics. The direct abundances are in relatively good agreement with the stellar metallicities, once we apply a modest correction to the nebular oxygen abundance due to depletion onto dust. Popular empirically calibrated strong-line diagnostics tend to provide nebular abundances that underestimate the stellar metallicities above the solar value by ∼0.2 dex. This result could be related to difficulties in selecting calibration samples at high metallicity. The O3N2 method calibrated by Pettini and Pagel gives the best agreement with our stellar metallicities. We confirm that metal recombination lines yield nebular abundances that agree with the stellar abundances for high-metallicity systems, but find evidence that in more metal-poor environments they tend to underestimate the stellar metallicities by a significant amount, opposite to the behavior of the direct method.
Charge Carrier Recombination Dynamics in MAPb(Br1−yIy)3 Single Crystals

Studying the carrier recombination process in MAPb(Br1−yIy)3 single crystals (SCs) is important for its application in the optoelectronic field. In this work, a series of MAPb(Br1−yIy)3 SCs with varied Br/I compositions have been studied. Steady-state photoluminescence (PL) spectra, time-resolved photoluminescence (TRPL) spectra and time-resolved microwave photoconductivity (TRMC) were used to understand the radiative and non-radiative recombination processes of MAPb(Br1−yIy)3 SCs. By comparing the dynamics of TRPL and TRMC, we conclude that the dynamics of TRPL is dominated by the electron trapping process, which is in accordance with the fast decay component of TRMC kinetics, whereas the slower decay component in TRMC is dominated by the hole trapping process. Moreover, we find both the electron and hole trapping rates in mixed-halide perovskite MAPb(Br1−yIy)3 (0 < y < 1) SCs are higher than that of mono-halide perovskite MAPbBr3 SCs and MAPbI3 SCs. This suggests mixed-halide crystals could introduce additional electron and hole trapping densities, which could be related to the fluctuation of Br/I compositions in the crystals. This work is helpful for understanding carrier recombination process in mixed-halide perovskite SCs.
ON THE O/H, Mg/H, Si/H, AND Fe/H GAS AND DUST ABUNDANCE RATIOS IN GALACTIC AND EXTRAGALACTIC H ii REGIONS

We derive the Mg/H ratio in the Orion nebula and in 30 Doradus. We also derive the O/H and the Fe/O ratios in the extremely metal-poor galaxy SBS 0335−052 E. We estimate the dust depletions of Mg, Si, and Fe in Galactic and extragalactic H ii regions. From these depletions we estimate the fraction of O atoms embedded in dust as a function of the O/H ratio. We find an increasing depletion of O with increasing O/H. The O depletion increases from about 0.08 dex, for the metal poorest H ii regions known, to about 0.12 dex, for metal-rich H ii regions. This depletion has to be considered to compare nebular with stellar abundances.
Virtual-Range-Forwarding-Based Opportunistic Routing for Mine Goaf Sensor Networks

As a vital technology of the mine Internet of Things (MIoT), mine goaf sensor networks (MGSNs) have strong self-organization and invulnerability, which has the potential to realize the safety monitoring of the goaf. However, the adverse characteristics of MGSNs need to be addressed, such as time-varying wireless channels, serious signal attenuation, vulnerable network nodes, and limited nonrechargeable battery power. This article proposes a virtual-range-forwarding-based opportunistic routing (VFBOR) method for MGSNs. First, according to the actual environment of the goaf, the novel, appropriate deployment of low-cost routing nodes is designed among sensors to construct hierarchical, invulnerable self-organized MGSNs. Moreover, following the principles of opportunistic routing theory, VFBOR synthetically evaluates the distance between nodes with regard to the residual energy to select the appropriate routing nodes. In addition, VFBOR adopts a recovery mechanism to avoid the void region problem during the data packet forwarding process of nodes and improve the network connectivity. Finally, a realistic implementation plan is proposed. The simulation results confirm that VFBOR significantly outperforms some existing routing protocols, in terms of energy saving, wireless communication connectivity, and network throughput.
Indirect exchange interaction between magnetic adatoms in graphene

We present a theoretical study of indirect exchange interaction between magnetic adatoms in graphene. The coupling between the adatoms to a graphene sheet is described in the framework of tunneling Hamiltonian. We account for the possibility of this coupling being of resonant character if a bound state of the adatom effectively interacts with the continuum of 2D delocalized states in graphene. In this case the indirect exchange between the adatoms mediated by the 2D carriers appears to be substantially enhanced compared to the results known from Ruderman-Kittel-Kasuya-Yosida (RKKY) theory. Moreover, unlike the results of RKKY calculations in the case of resonant exchange the magnetic coupling between the adatoms sitting over different graphene sublattices do not cancel each other. Thus, for a random distribution of the magnetic adatoms over graphene surface a non-zero magnetic interaction is expected. We also suggest an idea of controlling the magnetism by driving the tunnel coupling in and out of resonance by a gate voltage.