Miracle-dz/UniFAR-FTUs huggingface dataset:
On communication of analog data from a bounded source space

We consider the problem of the transmission of discrete-time analog data with a variety of fidelity criteria. The outputs of the analog source are assumed to belong to a bounded set. Bounds on the minimum achievable average distortion for memoryless sources are derived both for the case where the coding delay is infinite (an extension of the Shannon Theory) and also for some cases where the coding delay is finite. Several examples are given, for which the upper and lower bounds coincide. Further, we discuss the case where the assumption of the existence of a probabilistic model for the source is dropped. We adopt as our fidelity criterion the supremum over all possible source-output n-sequences x, of the conditional expectation of the distortion given x (“guaranteed distortion”). The Shannon Theory is not directly applicable in determining the minimum guaranteed distortion. We do obtain results for two important cases. Some generalizations and applications are also discussed.
Flow structure from an oscillating cylinder Part 1. Mechanisms of phase shift and recovery in the near wake

Cylinders of various cross-section were subjected to controlled oscillations in a direction transverse to the incident flow. Excitation was at frequency fe, relative to the formation frequency f*0 of large-scale vortices from the corresponding stationary cylinder, and at Reynolds numbers in the range 584 [les ] Re [les ] 1300. Modifications of the near wake were characterized by visualization of the instantaneous flow structure in conjunction with body displacement-flow velocity correlations. At fe/f*0 = ½, corresponding to subharmonic excitation, as well as at fe/f*0 = 1, the near wake structure is phase-locked (synchronized) to the cylinder motion. However, the synchronization mechanism is distinctly different in these two regimes. Near or at fe/f*0 = 1, the phase of the shed vortex with respect to the cylinder displacement switches by approximately π. Characteristics of this phase switch are related to cylinder geometry. It does not occur if the cylinder has significant afterbody. Over a wide range of fe/f*0, the perturbed near wake rapidly recovers to a largescale antisymmetrical mode similar in form to the well-known Kármán vortex street. The mechanisms of small-scale (fe) vortex interaction leading to recovery of the large-scale (f0) vortices are highly ordered and repeatable, though distinctly different, for superharmonic excitation (fe/f*0 = n = 2, 3, 4) and non-harmonic excitation (non-integer values of fe/f*0). The frequency f0 of the recovered vortex street downstream of the body shows substantial departure from the shedding frequency f*0 from the corresponding stationary body. It locks-on to resonant modes corresponding to f0/fe = 1/n. This wake response involves strictly hydrodynamic phenomena. It shows, however, a resonant behaviour analogous to that of coupled flow-acoustic systems where the shear layer is convectively unstable
Improved laser-to-proton conversion efficiency in isolated reduced mass targets

We present experimental results of laser-to-proton conversion efficiency as a function of lateral confinement of the refluxing electrons. Experiments were carried out using the T-Cubed laser at the Center for Ultrafast Optical Science, University of Michigan. We demonstrate that the laser-to-proton conversion efficiency increases by 50% with increased confinement of the target from surroundings with respect to a flat target of the same thickness. Three-dimensional hybrid particle-in-cell simulations using LSP code agree with the experimental data. The adopted target design is suitable for high repetition rate operation as well as for Inertial Confinement Fusion applications.
Estimation and Testing of Learning Curves

This article describes a new approach to learning curve estimation. Our approach is to formulate statistical procedures that conform to alternative learning curve theories. This leads to the development of nonlinear statistical models of the learning curves. For the three data sets analyzed, autocorrelation seems to be an important problem. Parameter estimates were derived using the maximum likelihood principle in the presence of first-order autocorrelation. Nonnested tests were used to select the appropriate formulation of the learning curve. Research conclusions are to use unit data when estimating a learning curve and to be prepared to treat autocorrelation if present.
Monitoring the hepatobiliary function using image techniques and labeled cholephilic compounds

Evaluation of the hepatobiliary function is critical for the clinicians, not only for the diagnosis of a large variety of liver diseases but also in the follow-up and management of some patients, for instance, those with different degrees of cholestasis suffering from a drug-induced liver injury (DILI) or scheduled for liver resection. Currently, the determination of global liver function mainly relies on laboratory tests, clinical scores, and data from images obtained with ultrasonography, computed tomography (CT), or magnetic resonance. Nuclear medicine scanning, displaying either planar or three-dimensional spatial distribution of liver function, is enhanced when using hepatotropic tracers based on classical radioisotopes such as technetium-99m (99mTc) and with higher resolution using metabolized probes such as those based on monosaccharide derivatives labeled with 18F. Other cholephilic compounds, and hence selectively secreted into bile, have been proposed to visualize the correct function of the liver parenchyma and the associated secretory machinery. This review aims to summarize the state-of-the-art regarding the techniques and chemical probes available to monitor liver and gallbladder function, in some cases based on imaging techniques reflecting the dynamic of labeled cholephilic compounds.
Expected frequencies of DNA patterns using whittle's formula

Given a realisation of a Markov chain, one can count the numbers of state transitions of each type. One can ask how many realisations are there with these transition counts and the same initial state. Whittle (1955) has answered this question, by finding an explicit though complicated formula, and has also shown that each realisation is equally likely. In the analysis of DNA sequences which comprise letters from the set {A, C, G, T}, it is often useful to count the frequency of a pattern, say ACGCT, in a long sequence and compare this with the expected frequency for all sequences having the same start letter and the same transition counts (or ‘dinucleotide counts' as they are called in the molecular biology literature). To date, no exact method exists; this paper rectifies that deficiency.
Effect of Natural Variations in Rain Drop Size Distributions on Rain Rate Estimators of 3 cm Wavelength Polarimetric Radar

Statistical errors of rain rate estimators due to natural variations in raindrop size distribution (DSD) are studied for 3-cm wavelength polarimetric radar. Four types of estimators are examined: A classical estimator R(ZH), and three types of polarimetric radar estimators R(KDP), R(ZH, ZDR), and R(KDP, ZDR), where R is the rain rate, ZH is the reflectivity factor at horizontal polarization, KDP is the specific differential phase, and ZDR is the differential reflectivity. The T-matrix method is employed for the scattering calculations, and a total of 7,664 one-minute raindrop size spectra, measured with a Joss-Waldvogel type disdrometer are used.According to simulation results, the normalized errors (NEs) of R(ZH), R(KDP), R(KDP,ZDR), and R(ZH,ZDR) for all DSD samples are 25%, 14%, 9%, and 10%, respectively. The NEs of all estimators, except R(ZH), tend to decrease with increasing rain rate. For rain rates larger than 10 mmh−1, e.g., the average NEs of R(ZH), R(KDP), R(KDP, ZDR), and R(ZH,ZDR) are 25%, 9%, 5%, and 7%, respectively. The simulation results show that the classical estimator R(ZH) is the most sensitive to variations in DSD and the estimator R(KDP, ZDR) is the least sensitive.The lowest sensitivity of the rain estimator R(KDP, ZDR) to variations in DSD can be explained by the following facts. The difference in the forward-scattering amplitudes at horizontal and vertical polarizations, which contributes KDP, is proportional to the 4.78th power of the drop diameter. On the other hand, the exponent of the backscatter cross section, which contributes to ZH, is proportional to the 6.38th power of the drop diameter. Because the rain rate R is proportional to the 3.67th power of the drop diameter, KDP is less sensitive to DSD variations than ZH. However, DSD spectra with unusually large median volume diameter D0 can increase the estimation error of R(KDP). The differential reflectivity ZDR reduces the effect of unusual D0 and is useful for further improvement of the estimator R(KDP). This is due to the fact that ZDR itself is a good measure of D0.
Recent results with the Phoenix booster at ISOLDE

At ISOLDE, CERN the development of charge breeding techniques is one of the key points for numerous future experiments. We report in this article on the latest online tests of the Daresbury Phoenix booster at ISOLDE. The performances of the continuous mode were investigated for different injected stable beams. Preliminary results were obtained with the afterglow pulsing method and two injected noble gas isotopes. These latter tests are of particular interest for the postacceleration of ISOL-type beams.
Operational multivariate ocean data assimilation

A fully three‐dimensional, multivariate, optimum‐interpolation ocean data assimilation system has been developed that produces simultaneous analyses of temperature, salinity, geopotential and vector velocity. The system is run in real‐time, and can be executed as a stand‐alone analysis or cycled with an ocean forecast model in a sequential incremental update cycle. Additional capabilities have been built into the system, including flow‐dependent background‐error correlations and background‐error variances that vary in space and evolve from one analysis cycle to the next. The ocean data types assimilated include: remotely sensed sea surface temperature, sea surface height, and sea‐ice concentration; plus in situ surface and sub‐surface observations of temperature, salinity, and currents from a variety of sources, such as ships, buoys, expendable bathythermographs, conductivity–temperature–depth sensors, and profiling floats. An ocean data quality‐control system is fully integrated with the multivariate analysis, and includes feedback of forecast fields and prediction errors in the quality control of new observations. The system is operational at the US Navy oceanographic production centres both in global and in regional applications. It is being implemented as the data assimilation component of the Hybrid Coordinate Ocean Model as part of the US contribution to the Global Ocean Data Assimilation Experiment, and in a limited‐area ensemble‐based forecasting system that will be used in an adaptive sampling, targeted observation application. Copyright © 2005 Royal Meteorological Society
Messenger lectures and video lessons as mathematics interventions for modular distance learning

Providing self-learning modules and other printed materials under modular distance learning in the Philippines does not guarantee the acquisition of learning competencies, particularly in mathematics, wherein explanation and illustration of concepts, principles, and procedures demand the teacher’s clarification. To address this, Facebook Messenger was used as a platform to communicate with the students and deliver interventions like lectures through posting slides with voice clips and video lessons. This study aimed to establish the effectiveness of the said interventions for grade 7 mathematics using practical action research. Test material, questionnaire, and interview guide were the instruments used in collecting data validated by a master teacher and two head teachers in mathematics education. Quantitative data were analyzed using SPSS version 23 and qualitative data were analyzed using thematic analysis. Based on the findings, lectures and video lessons through Facebook Messenger helped students understand the mathematical concepts and procedures for solving mathematical problems. It means that the said interventions via Facebook Messenger were adequate for the students to cope with the learning challenges in mathematics brought by modular distance learning. However, the students suggested that sustain the interventions for a longer time.
Moro Noun Class Morphology

We provide a description and classification of all the noun classes in Moro, identifying class prefixes and concord consonants, and semantic connotations of classes when such are present. The major challenge posed by noun classes in Moro concerns the status of vowel-initial nouns. Noun class prefixes normally consist of a single consonant as in (1a-d), but vowel-initial nouns do occur (1e-g). It is not obvious whether an initial vowel constitutes a prefix, or whether the vowel is part of the root, and there is no noun class prefix. We claim that in fact both patterns exist. We will argue that the singular classes in (1e-f) have historically lost an initial velar class prefix which is still evident in the concord patterns; the initial vowels here are root vowels. Even though these vowels do not always appear in the plural, there is independent evidence for vowel reduction and deletion in Moro. In contrast, the class pair in (1g) has vowel prefixes in both singular and plural.
Supersaturation and Precipitation Applicated in Drug Delivery Systems: Development Strategies and Evaluation Approaches

Supersaturation is a promising strategy to improve gastrointestinal absorption of poorly water-soluble drugs. Supersaturation is a metastable state and therefore dissolved drugs often quickly precipitate again. Precipitation inhibitors can prolong the metastable state. Supersaturating drug delivery systems (SDDS) are commonly formulated with precipitation inhibitors, hence the supersaturation is effectively prolonged for absorption, leading to improved bioavailability. This review summarizes the theory of and systemic insight into supersaturation, with the emphasis on biopharmaceutical aspects. Supersaturation research has developed from the generation of supersaturation (pH-shift, prodrug and SDDS) and the inhibition of precipitation (the mechanism of precipitation, the character of precipitation inhibitors and screening precipitation inhibitors). Then, the evaluation approaches to SDDS are discussed, including in vitro, in vivo and in silico studies and in vitro–in vivo correlations. In vitro aspects involve biorelevant medium, biomimetic apparatus and characterization instruments; in vivo aspects involve oral absorption, intestinal perfusion and intestinal content aspiration and in silico aspects involve molecular dynamics simulation and pharmacokinetic simulation. More physiological data of in vitro studies should be taken into account to simulate the in vivo environment. The supersaturation theory should be further completed, especially with regard to physiological conditions.
Single-domain antibody for binding the conserved epitope in the SARS-CoV-2 spike protein receptor-binding domain

Several COVID-19 vaccines have been developed in the last three years using various tecnhiques. Multiple virus-neutralizing antibodies against SARS-CoV-2 have been also obtained to combat the pandemic. However, the use of these medications for prevention and potential treatment faces significant challenges due to the emergence of new mutant virus variants, both more contagious and escaping neutralization by the immune system, that is why it is necessary to continuously renew the vaccines and develop new therapeutic antibodies. The study was aimed to use the technology of generating single-domain antibodies (nanobodies) to target the surface spike (S) protein RBD conserved epitope of the broad spectrum of SARS-CoV-2 variants. Recombinant proteins that corresponded to RBDs of three important SARS-СoV-2 strains and the full-length S protein (Wuhan) were used as antigens for immunization of a camel in order to induce production of appropriate antibodies and/or as immobilized proteins for further cross selection of the nanobody clones with pre-set specificity by the phage display. A nanobody capable of effectively recognizing the conservative region in the S protein RBDs of the broad spectrum of pandemic SARS-CoV-2 variants, including Omicron, was selected from the generated immune library. Along with conventional use in immunoassays and diagnosis, the generated nanobody can be potentially used as a module for target-specific binding used to trap coronavirus in human upper airways during the development of novel combination antiviral drugs.
Distributed Multi-Agent Deep Q-Learning for Load Balancing User Association in Dense Networks

Distributed learning can lead to effective user association with low overhead, but faces significant challenges in incorporating load balancing at all base stations (BS) because of coupling constraints. In this letter, we propose a distributed multi-agent deep Q-learning model for user association to satisfy the load balancing constraint at every BS. Specifically, we design a deep Q-network (DQN) with target Q-network and experience replay buffer at each user as an agent. We also propose a multi-agent matching policy to control the number of users connected to each BS for load balancing. The policy enhances network throughput by implementing a novel updating rule for the preference list at each BS. The proposed multi-agent DQN model operates in a fully distributed manner, where each agent only uses local information and requires no information exchange between agents. Simulation results demonstrate that our proposed algorithm outperforms a conventional distributed load balancing algorithm and approaches a centralized scheme performance, while exhibiting fast convergence and high adaptability to channel changes.
Using the Proteomics Toolbox to Resolve Topology and Dynamics of Compartmentalized cAMP Signaling

cAMP is a second messenger that regulates a myriad of cellular functions in response to multiple extracellular stimuli. New developments in the field have provided exciting insights into how cAMP utilizes compartmentalization to ensure specificity when the message conveyed to the cell by an extracellular stimulus is translated into the appropriate functional outcome. cAMP compartmentalization relies on the formation of local signaling domains where the subset of cAMP signaling effectors, regulators and targets involved in a specific cellular response cluster together. These domains are dynamic in nature and underpin the exacting spatiotemporal regulation of cAMP signaling. In this review, we focus on how the proteomics toolbox can be utilized to identify the molecular components of these domains and to define the dynamic cellular cAMP signaling landscape. From a therapeutic perspective, compiling data on compartmentalized cAMP signaling in physiological and pathological conditions will help define the signaling events underlying disease and may reveal domain-specific targets for the development of precision medicine interventions.
White light interferometric profile measurement system using spectral coherence

It is well known that white light interferometry (WLI) is important to nano-scale 3-D profile measurement technology. To archive cost-effective and accurate measurements, the researches for WLI are widely spreading. Our objective is to build up a 3-D micro-structure profile measurement system based on WLI, for micro-mechatronic, micro-optical, and semi-conductor devices. This paper briefly reviews related WLI theory and then the principle of spectral coherence is employed to improve the system design. Specifically, proper spectral filters can be used to extend the coherence length of the light source to the order of several ten micrometers. That is, the coherence length of the filtered light source is longer than that of the original source. In this paper, Michelson interference experiments are conducted with filtered and unfiltered white light sources, to show the feasibility of the concept of spectral coherence. The Michelson interferometer is adopted due to its convenience of optical installation and its acceptable tolerance to noise. The experiment results indicate that our approach is feasible and thus it can improve the WLI measurement performance.
Developing novel machine-learning-based fire weather indices

Accurate wildfire risk estimation is an essential yet challenging task. As the frequency of extreme fire weather and wildfires is on the rise, forest managers and firefighters require accurate wildfire risk estimations to successfully implement forest management and firefighting strategies. Wildfire risk depends on non-linear interactions between multiple factors; therefore, the performance of linear models in its estimation is limited. To date, several traditional fire weather indices (FWIs) have been commonly used by weather services, such as the Canadian FWI. @Traditional FWIs are primarily based on empirical and statistical analyses. In this paper, we propose a novel FWI that was developed using machine learning—the machine learning based fire weather index (MLFWI). We present the performance of the MLFWI and compare it with various traditional FWIs. We find that the MLFWI significantly outperforms traditional indices in predicting wildfire occurrence, achieving an area under the curve score of 0.99 compared to 0.62–0.80. We recommend applying the MLFWI in wildfire warning systems.
Automatic Seabed Target Segmentation of AUV via Multilevel Adversarial Network and Marginal Distribution Adaptation

The real-time segmentation results of side-scan sonar (SSS) images can realize the intelligent perception of autonomous underwater vehicles (AUVs). However, there remains a compelling challenge that the models trained with training data may not generalize well in practical applications due to the various marine conditions/working devices. This article proposes a real-time cross-domain segmentation adaptation scheme for SSS images based on adversarial training via min–max training to distribute SSS images in the output space and marginal distribution adaption to improve the segmentation performance by minimizing the measured distribution distance. To further enhance the adapted model, the multilayer feature discriminators are established to realize the fine-grained alignment. Experiments on the real-world SSS datasets demonstrate that our proposed model outperforms state-of-the-art methods and achieves the actual industrial application for AUV.