Miracle-dz/UniFAR-FTUs huggingface dataset:
Statistics of Dark Matter Substructure: III. Halo-to-Halo Variance

We present a study of unprecedented statistical power regarding the halo-to-halo variance of dark matter substructure. Using a combination of N-body simulations and a semi-analytical model, we investigate the variance in subhalo mass fractions and subhalo occupation numbers, with an emphasis on how these statistics scale with halo formation time. We demonstrate that the subhalo mass fraction, f_sub, is mainly a function of halo formation time, with earlier forming haloes having less substructure. At fixed formation redshift, the average f_sub is virtually independent of halo mass, and the mass dependence of f_sub is therefore mainly a manifestation of more massive haloes assembling later. We compare observational constraints on f_sub from gravitational lensing to our model predictions and simulation results. Although the inferred f_sub are substantially higher than the median LCDM predictions, they fall within the 95th percentile due to halo-to-halo variance. We show that while the halo occupation distribution of subhaloes, P(N|M), is super-Poissonian for large , a well established result, it becomes sub-Poissonian for < 2. Ignoring the non-Poissonity results in systematic errors of the clustering of galaxies of a few percent, and with a complicated scale- and luminosity-dependence. Earlier-formed haloes have P(N|M) closer to a Poisson distribution, suggesting that the dynamical evolution of subhaloes drives the statistics towards Poissonian. Contrary to a recent claim, the non-Poissonity of subhalo occupation statistics does not vanish by selecting haloes with fixed mass and fixed formation redshift. Finally, we use subhalo occupation statistics to put loose constraints on the mass and formation redshift of the Milky Way halo. Using observational constraints on the V_max of the most massive satellites, we infer that 0.25
Identification of biomarkers related to tumorigenesis and prognosis in breast cancer

Background The aim of the present study was to identify the central genes and prognostic index of breast cancer, and to determine the relationship between prognostic index and immune infiltration levels to provide useful information for the diagnosis and treatment of breast cancer. Methods The Cancer Genome Atlas breast cancer dataset and 2 microarray datasets were applied to screen overlapping differentially expressed genes (DEGs) between breast cancer tissue and normal breast tissue samples. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were conducted through the Database for Annotation, Visualization, and Integrated Discovery. Protein-protein interaction (PPI) networks were used to screen hub genes of the overlapping DEGs. Gene Expression Profiling Interactive Analysis (GEPIA), The University of ALabama at Birmingham CANcer data analysis Portal (UALCAN), and The Human Protein Atlas (HPA) databases were used to validate their expression. The correlation of hub genes with immune infiltration was analyzed using TISIDB software. Kaplan-Meier Plotter was used to analyze the prognosis of hub genes. Results Ten hub genes [cyclin A2 (CCNA2), cyclin dependent kinase 1 (CDK1), centromere protein F (CENPF), kinesin family member 2C (KIF2C), kinesin family member 4A (KIF4A), maternal embryonic leucine zipper kinase (MELK), PDZ binding kinase (PBK), protein regulator of cytokinesis 1 (PRC1), DNA topoisomerase II alpha (TOP2A), and TPX2 microtubule nucleation factor (TPX2)] were selected and their overexpression in breast cancer tissue was verified. All were associated with a poor prognosis for breast cancer. CDK1, CENPF, KIF2C, KIF4A, MELK, PBK, PRC1, and TPX2 were correlated with CD4 T cells in breast cancer, while TOP2A was correlated with CD8 T cells. Conclusions The findings indicated that the 10 hub genes could be potential biomarkers for progression in breast cancer.
Non-Gaussianity and the Cosmic Microwave Background Anisotropies

We review in a pedagogical way the present status of the impact of non-Gaussianity (NG) on the Cosmic Microwave Background (CMB) anisotropies. We first show how to set the initial conditions at second-order for the (gauge invariant) CMB anisotropies when some primordial NG is present. However, there are many sources of NG in CMB anisotropies, beyond the primordial one, which can contaminate the primordial signal. We mainly focus on the NG generated from the post-inflationary evolution of the CMB anisotropies at second-order in perturbation theory at large and small angular scales, such as the ones generated at the recombination epoch. We show how to derive the equations to study the second-order CMB anisotropies and provide analytical computations to evaluate their contamination to primordial NG (complemented with numerical examples). We also offer a brief summary of other secondary effects. This review requires basic knowledge of the theory of cosmological perturbations at the linear level.
Towards a practical programming language based on dependent type theory

Dependent type theories have a long history of being used for theorem proving. One aspect of type theory which makes it very powerful as a proof language is that it mixes deduction with computation. This also makes type theory a good candidate for programming---the strength of the type system allows properties of programs to be stated and established, and the computational properties provide semantics for the programs. This thesis is concerned with bridging the gap between the theoretical presentations of type theory and the requirements on a practical programming language. Although there are many challenging research problems left to solve before we have an industrial scale programming language based on type theory, this thesis takes us a good step along the way.
Deep Reinforcement Learning for Aerial Data Collection in Hybrid-Powered NOMA-IoT Networks

With the help of unmanned aerial vehicle (UAV), remote terminals that out of wireless coverage can be connected to the Internet of Things (IoT) networks. Currently, the IoT relies on a large number of low-cost wireless sensors with limited energy supply to realize ubiquitous monitoring and intelligent control. The hybrid-powered networks composed of wireless-powered communication (WPC) terminal and solar-powered UAV can solve the energy supply problem of the IoT networks, and the nonorthogonal multiple access (NOMA) technique can solve the massive access problem of IoT terminals. Exploiting these benefits, we investigate joint UAV 3-D trajectory design and time allocation for aerial data collection in hybrid-powered NOMA-IoT networks. To maximize the total fair network throughput, we jointly consider energy limitation, Quality of Service (QoS) requirements, and flight conditions. The problem is nonconvex and time-dimension coupled which is intractable to solve by traditional optimization methods. Therefore, we develop a deep reinforcement learning (DRL) algorithm called fair communication is accomplished by trajectory design and time allocation (FC-TDTA), which uses the deep deterministic policy gradient (DDPG) as its basis. Simulation results show that our proposed algorithm performs better than benchmarks in fair throughput maximization. The proposed FC-TDTA algorithm can make the UAV: 1) fly in appropriate direction and speed, so that the UAV can arrive at the charging station before the energy runs out and 2) conduct WPC energy transmission and data collection to achieve fair communication.
Why Do Some Post-Tropical Cyclones Impact Europe?

Post-tropical cyclones (PTCs) can bring high winds and extreme precipitation to Europe. Although the structure and intensity of observed Europe-impacting PTCs has been investigated in previous studies, a quantitative understanding of the factors important for PTCs to reach Europe has not been established. By tracking and identifying the full life cycle of tropical cyclones (TCs) in the ERA5 reanalysis, we investigate why some PTCs impact Europe and why others do not, using a composite analysis. We show that PTCs that impact Europe are typically ∼4–6 m s−1 stronger at their lifetime maximum intensity and throughout the extratropical transition process. They are also twice as likely to reintensify in the midlatitudes. During ET, the Europe-impacting PTCs interact more strongly with an upstream upper-level trough in a significantly more baroclinic environment. The Europe-impacting PTCs are steered on a more poleward trajectory across a midlatitude jet streak. It is during the crossing of the jet that these cyclones often undergo their reintensification. Using contingency table analysis, TC lifetime maximum intensity, and whether post-ET reintensification occurs are shown to be significantly associated with the odds that a PTC reaches Europe. This supports our composite analysis and further indicates that TC intensity and reintensification both modulate the likelihood that a PTC will impact Europe. Some post-tropical cyclones (PTCs) reach Europe, often associated with extreme precipitation and high winds. It is currently unclear what factors allow this to occur. In this study, we track cyclones in two reanalyses using a feature tracking scheme and identify the PTCs by matching (in space and time) reanalysis tracks with observed tracks. Using a composite analysis, we show that 1) tropical cyclones (TCs) that are more intense, and 2) TCs that reintensify after extratropical transition, are more likely to reach Europe. TCs that reintensify interact strongly with an upper-level upstream trough and cross a midlatitude jet streak. Reintensification occurs as the cyclones cross this jet streak.
A 12V/24V-to-1V PWM-Controlled DSD Converter With Delay-Insensitive and Dual-Phase Charging Techniques for Fast Transient Responses

A 12V/24V-to-1V pulsewidth modulation (PWM) controlled double step-down (DSD) power converter with delay-insensitive and dual-phase charging techniques is presented. The proposed PWM controller has two feedback loops to regulate the output voltage and the voltage across the flying capacitor, respectively. With the proposed delay-insensitive technique, near-optimal transient responses are achieved by using the PWM control regardless of the time of occurrence of the transient. Moreover, the responses are further improved by two times by using the proposed dual-phase charging technique. The converter was fabricated in a 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> BCD process with all power transistors integrated on chip. For 12V-to-1V conversion and 24V-to-1V conversion, the measured peak efficiencies at 1-MHz switching frequency are 88.3% and 83.5%, respectively. With a load current step of 3 A and a rise time of 20 ns, the measured voltage droop and 1% settling time are 56 mV and 0.9 <inline-formula> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula>, respectively.
Exergy analysis of a seawater reverse osmosis plant in Jeddah, Saudi Arabia

In this work the performance of a seawater reverse osmosis plant, located in Jeddah, Saudi Arabia, is analyzed by using the exergy analysis. Recently developed seawater property correlations at high pressures have been used. To get the most accurate results, seawater has been modeled as a non-ideal solution. In addition to physical exergy, chemical and mixing exergies have also been taken into account. The activity coefficients have been modeled as salinity dependent. Exergy flowrates of streams for major process equipment are calculated and exergy destruction around each of the equipment is estimated. MS Excel spreadsheet is used to calculate mass, energy, and exergy balances at a heavy load of distillate production (~1,200 kg/s) at 35°C in the summer. It is found that the maximum exergy destruction occurs at the pumps (~51%) and reverse osmosis modules (~21%) followed by the dual media filters (~13%) and the mixers (~12%). The SEC is calculated to be 4.88 kW/m3. The results have been compared with the previous studies. The second-law efficiency of the plant is determined to be 29.4%. This efficiency is low and several measures can be done to improve it by decreasing inlet exergy to the plant and controlling exergy losses during process operation.
Is time continuous

Conventional time is modelled as the one dimensional continuum R^1 of real numbers. This continuity, however, does {\em not} stem from {\em any} fundamental principle. On the other hand, natural time is {\em not} continuous and its values as well as those of the energy, form {\em countable} sets, i.e., with cardinalities either finite or equal to \aleph_0, where this symbol stands for the {\em transfinite} number of natural numbers. For infinitely large number of events, the values of natural time form a {\em denumerable} set, i.e., its cardinality is exactly \aleph_0, while those of conventional time an {\em uncountable} set. This has a drastically larger cardinality, which in the light of the continuum hypothesis becomes equal to 2^{\aleph_0}.
Constraints on particle acceleration sites in the Crab nebula from relativistic magnetohydrodynamic simulations

The Crab Nebula is one of the most efficient accelerators in the Galaxy and the only galactic source showing direct evidence of PeV particles. In spite of this, the physical process behind such effective acceleration is still a deep mystery. While particle acceleration, at least at the highest energies, is commonly thought to occur at the pulsar wind termination shock, the properties of the upstream flow are thought to be non-uniform along the shock surface, and important constraints on the mechanism at work come from exact knowledge of where along this surface particles are being accelerated. Here we use axisymmetric relativistic MHD simulations to obtain constraints on the acceleration site(s) of particles of different energies in the Crab Nebula. Various scenarios are considered for the injection of particles responsible for synchrotron radiation in the different frequency bands, radio, optical and X-rays. The resulting emission properties are compared with available data on the multi wavelength time variability of the inner nebula. Our main result is that the X-ray emitting particles are accelerated in the equatorial region of the pulsar wind. Possible implications on the nature of the acceleration mechanism are discussed.
Hybrid Renewable Energy System Controlled with Intelligent Direct Power Control

The paper suggests a novel DPC approach to ameliorate the management and HRES “hybrid renewable energy system” control composed of photovoltaic and wind systems. In the conventional DPC technique, the “switching table” is founded on a “hysteresis comparator”, which poses the problem of fluctuations on the HRES various output variables. The approach proposed here is based on an FLC and shown to diminish the ripples in the “active” and “reactive” powers waveforms. The comprehensive HRES and the advised “control schemes” are implemented using “MATLAB/Simulink” and the results indicate that the advised method had preferable performance over the conventional DPC.
Investigating Safety in Numbers in Cycling After the Entry of Dock-Based Bikeshare Programs in Three U.S. Cities

This paper investigates the safety in numbers effect in cycling by comparing monthly bicycle crash trends before and after public bikeshare’s launch in Philadelphia and Pittsburgh, PA, from 2010 to 2019, and in Portland, OR, from 2014 to 2019. To estimate the bicycle crash trend in each city, the authors conduct time series analysis with a regression discontinuity design using generalized linear quasi-Poisson models. The entries of dock-based public bikeshare programs signal an increase in biking activity on the road and serve as the point of discontinuity in the analysis. For each city, the analysis models pre- and post-bikeshare bicycle crash trends inside and outside bikeshare program’s service area separately to compare crash trends at different biking activity levels within the same local context. Results show that before bikeshare, crash trends inside and outside service areas had similar patterns in Philadelphia and Portland while moving in opposite directions in Pittsburgh. The post-bikeshare crash trends differed across cities for both inside and outside of the service areas. The finding that bicycle crash trends changed in different patterns across the three cities indicates that there may be no single safety in numbers effect on bicycle crashes. The inconsistent patterns of bicycle crash trends within and across cities suggest confounding factors underlying the change in bicycle crashes.
AlGaN Channel Field Effect Transistors with Graded Heterostructure Ohmic Contacts

We report on ultra-wide bandgap (UWBG) Al0.75Ga0. 25N channel metal-insulator-semiconductor field-effect transistors (MISFET) with heterostructure engineered low-resistance ohmic contacts. The low intrinsic electron affinity of AlN (0.6 eV) leads to large Schottky barriers at metal-AlGaN interface, resulting in highly resistive ohmic contacts. In this work, we use reverse compositional graded n++ AlGaN contact layer to achieve upward electron affinity grading, leading to a low specific contact resistance of 1.9x10-6 ohm.cm2 to n-Al0.75Ga0. 25N channels (bandgap ~ 5.3 eV) with non-alloyed contacts. We also demonstrate UWBG Al0.75Ga0. 25N channel MISFET device operation employing compositional graded n++ ohmic contact layer and 20 nm atomic layer deposited Al2O3 as the gate-dielectric.
Warm Dirac-Born-Infeld inflation

We propose a warm inflationary model in the context of relativistic D-brane inflation in a warped throat, which has Dirac-Born-Infeld (DBI) kinetic term and is coupled to radiation through a dissipation term. The perturbation freezes at the sound horizon and the power spectrum is determined by a combination of the dissipative parameter and the sound speed parameter. The thermal dissipation ameliorates the eta problem and softens theoretical constraints from the extra-dimensional volume and from observational bounds on the tensor-to-scalar ratio. The warm DBI model can lead to appreciable non-Gaussianity of the equilateral type. As a phenomenological model, ignoring compactification constraints, we show that large-field warm inflation models do not necessarily yield a large tensor-to-scalar ratio.
Defining the unknowns for cell therapies in Parkinson's disease

ABSTRACT First-in-human clinical trials have commenced to test the safety and efficacy of cell therapies for people with Parkinson's disease (PD). Proof of concept that this neural repair strategy is efficacious is based on decades of preclinical studies and clinical trials using primary foetal cells, as well as a significant literature exploring more novel stem cell-derived products. Although several measures of efficacy have been explored, including the successful in vitro differentiation of stem cells to dopamine neurons and consistent alleviation of motor dysfunction in rodent models, many unknowns still remain regarding the long-term clinical implications of this treatment strategy. Here, we consider some of these outstanding questions, including our understanding of the interaction between anti-Parkinsonian medication and the neural transplant, the impact of the cell therapy on cognitive or neuropsychiatric symptoms of PD, the role of neuroinflammation in the therapeutic process and the development of graft-induced dyskinesias. We identify questions that are currently pertinent to the field that require further exploration, and pave the way for a more holistic understanding of this neural repair strategy for treatment of PD.
On the role of empirical boundary conditions in space weather prediction results

Prediction of the conditions in the near-Earth space environment (space weather) is an urgent scientific and practical task, and there are several scientific teams in the world which have been deeply involved in this research using various model assumptions. One of the most important problems in such calculations is the reliability of the initial data – synoptic maps of the solar magnetic fields. The most famous Space Weather Prediction Center (SWPC) uses observations of the Global Oscillations Network Group (GONG). However, there are other sources of measurements of the full-disk solar magnetic fields (WSO, SDO/HMI and SOLIS in the USA, IRmag at Mitaka in Japan, SMAT in China, STOPs in Russia), and it is of interest to use them to calculate the parameters of the solar wind. In this paper this is done on the example of Carrington Rotation (CR) 2164 using observations from Wilcox Solar Observatory (WSO), GONG, Solar Telescope for Operative Prediction (STOP) at the Sayan Solar Observatory (SSO). The calculations are based on the Wang-Sheeley-Arge (WSA) model and include the determination of the parameters of the coronal magnetic field in the Potential Field Source Surface (PFSS) approximation. The propagation of the solar wind to Earth’s orbit is calculated using the HUX (Heliospheric Upwind eXtrapolation) model. It is shown that the differences in solar wind speeds for different data sets can reach 200 km/s or even more. The results of model simulations are compared with the experimental ACE satellite data.
Dynamic Best Send Time Prediction for Marketing Email Campaigns

Email has been among the most popular mediums for sending out digital marketing campaigns since it provides an ease of engaging and keeping track of customers and prospects at an individual level. Being a cost effective medium, most marketing teams send out campaigns in bulk irrespective of the receiver's preference. This leads to a lower level of engagement and at times customers opting out from email subscriptions. Hence personalization that understands the receiver preferences is key to designing successful campaigns. One of the personalization is that of identification of the best time to send an email, where the objective is to identify the best time for email delivery where the likelihood of the email being visible and opened is high. Most of the existing solutions rely extensively on large receiver level behavioral information, leading to a cold start in most likely scenarios. Also, these solutions being static makes the prediction go stale over time. This work takes inspiration from studies which suggest that multiple attributes at senders and receivers level lead to differences in open behavior and propose a two-stage solution for the same. At first contacts/receivers are assigned to segments with similar attributes using a proprietary Decision Tree Algorithm, and further use Reinforcement Learning when predicting the best send time using the segment and receiver level behavioral information. Reinforcement Learning based prediction makes it possible for the predictions to be dynamic, whereupon receiving the feedback the predictions can be adjusted to a time that is closer to true behavior at each receiver level. The main novelty of the approach is that of being able to provide predictions for contacts with very little to no historical events, and a feedback mechanism to further improve upon the predictions in a live system. The system by design is simple and hence making it much more interpretable when trying to make sense of the predictions being made.