Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of Low-Level Thermodynamic Structure on the Downdraft Properties of Simulated Supercells

AbstractIdealized simulations using the Weather Research and Forecasting Model (WRF) were performed to examine the role of capping inversions on the near-surface thermodynamic structure of outflow from simulated supercells. Two simulations were performed: one with the traditional noncapped Weisman and Klemp (WK) analytic sounding and the second with a WK sounding modified to contain a capping inversion. Both sounding environments favor splitting storms and a right-moving supercell by 90 min into the simulation. These two supercell simulations evolve in a qualitatively similar fashion, with both storms exhibiting large, quasi-steady updrafts, hook-shaped appendages in the precipitation mixing ratio field, and prominent localized downdrafts.Results show that the supercell simulated in the capped environment has a surface cold pool with larger values of pseudoequivalent potential temperature (θep) than the cold pool of the supercell produced in the noncapped simulation. Parcels in the surface cold pool of th.
Morphology and Distribution of Antennal Sensilla in an Egg Parasitoid Wasp, Anastatus disparis (Hymenoptera: Eupelmidae)

Abstract The wasp Anastatus disparis is an egg endoparasitoid of a number of Lepidopteran pest species. To better understand the A. disparis olfactory system, we observed the antennal sensilla of males and females under a scanning electron microscope and quantified their sizes and morphological characteristics. We identified the types of sensilla and counted the numbers and locations of the different types on the dorsal and ventral antennal surfaces. The antennae of A. disparis are geniculate, with flagella that comprise 11 subsegments in females and eight in males. The mean antenna length was 1324.10 ± 52.50 μm in females and 1323.93 ± 65.20 μm in males. Ten sensillum types were identified in both sexes: Böhm bristles (BBs), sensilla trichodea (ST, with subtypes STI and STII), sensilla chaetica (SCh), sensilla basiconica (SB, with subtypes SBI and SBII), sensilla placodea (SP), sensilla coeleoconica (SCo), sensilla grooved peg (SGP), sensilla auricillica (SAu), sensilla campaniformia (SCa), and glandular pores (GPs). The total numbers of BBs, STI, SBII, SCa, SCo, and GPs did not differ significantly between the sexes, whereas the total numbers of SCh, SBI, and SAu were significantly greater in females, and those of STII, SP, and SGP were significantly lower. The types, number, and density of antennal sensilla increased from the base to the end. The possible functions of these sensilla in host-detection behavior are discussed.
Input correlations impede suppression of chaos and learning in balanced firing-rate networks

Neural circuits exhibit complex activity patterns, both spontaneously and evoked by external stimuli. Information encoding and learning in neural circuits depend on how well time-varying stimuli can control spontaneous network activity. We show that in firing-rate networks in the balanced state, external control of recurrent dynamics, i.e., the suppression of internally-generated chaotic variability, strongly depends on correlations in the input. A distinctive feature of balanced networks is that, because common external input is dynamically canceled by recurrent feedback, it is far more difficult to suppress chaos with common input into each neuron than through independent input. To study this phenomenon, we develop a non-stationary dynamic mean-field theory for driven networks. The theory explains how the activity statistics and the largest Lyapunov exponent depend on the frequency and amplitude of the input, recurrent coupling strength, and network size, for both common and independent input. We further show that uncorrelated inputs facilitate learning in balanced networks.
Strength of Attentional Modulation on Cortical Auditory Evoked Responses Correlates with Speech-in-Noise Performance in Bimodal Cochlear Implant Users

Auditory selective attention is a crucial top-down cognitive mechanism for understanding speech in noise. Cochlear implant (CI) users display great variability in speech-in-noise performance that is not easily explained by peripheral auditory profile or demographic factors. Thus, it is imperative to understand if auditory cognitive processes such as selective attention explain such variability. The presented study directly addressed this question by quantifying attentional modulation of cortical auditory responses during an attention task and comparing its individual differences with speech-in-noise performance. In our attention experiment, participants with CI were given a pre-stimulus visual cue that directed their attention to either of two speech streams and were asked to select a deviant syllable in the target stream. The two speech streams consisted of the female voice saying “Up” five times every 800 ms and the male voice saying “Down” four times every 1 s. The onset of each syllable elicited distinct event-related potentials (ERPs). At each syllable onset, the difference in the amplitudes of ERPs between the two attentional conditions (attended - ignored) was computed. This ERP amplitude difference served as a proxy for attentional modulation strength. Our group-level analysis showed that the amplitude of ERPs was greater when the syllable was attended than ignored, exhibiting that attention modulated cortical auditory responses. Moreover, the strength of attentional modulation showed a significant correlation with speech-in-noise performance. These results suggest that the attentional modulation of cortical auditory responses may provide a neural marker for predicting CI users’ success in clinical tests of speech-in-noise listening.
Different trajectories of depression, anxiety and anhedonia symptoms in the first 12 months of the COVID-19 pandemic in a UK longitudinal sample

Abstract Background While studies from the start of the COVID-19 pandemic have described initial negative effects on mental health and exacerbating mental health inequalities, longer-term studies are only now emerging. Method In total, 34 465 individuals in the UK completed online questionnaires and were re-contacted over the first 12 months of the pandemic. We used growth mixture modelling to identify trajectories of depression, anxiety and anhedonia symptoms using the 12-month data. We identified sociodemographic predictors of trajectory class membership using multinomial regression models. Results Most participants had consistently low symptoms of depression or anxiety over the year of assessments (60%, 69% respectively), and a minority had consistently high symptoms (10%, 15%). We also identified participants who appeared to show improvements in symptoms as the pandemic progressed, and others who showed the opposite pattern, marked symptom worsening, until the second national lockdown. Unexpectedly, most participants showed stable low positive affect, indicating anhedonia, throughout the 12-month period. From regression analyses, younger age, reporting a previous mental health diagnosis, non-binary, or self-defined gender, and an unemployed or a student status were significantly associated with membership of the stable high symptom groups for depression and anxiety. Conclusions While most participants showed little change in their depression and anxiety symptoms across the first year of the pandemic, we highlight the divergent responses of subgroups of participants, who fared both better and worse around national lockdowns. We confirm that previously identified predictors of negative outcomes in the first months of the pandemic also predict negative outcomes over a 12-month period.
Pheromone Response and Mating Behavior in Fission Yeast

Most ascomycete fungi, including the fission yeast Schizosaccharomyces pombe, secrete two peptidyl mating pheromones: C-terminally modified and unmodified peptides. S. pombe has two mating types, plus and minus, which secrete two different pheromones, P-factor (unmodified) and M-factor (modified), respectively. These pheromones are specifically recognized by receptors on the cell surface of cells of opposite mating types, which trigger a pheromone response. SUMMARY Most ascomycete fungi, including the fission yeast Schizosaccharomyces pombe, secrete two peptidyl mating pheromones: C-terminally modified and unmodified peptides. S. pombe has two mating types, plus and minus, which secrete two different pheromones, P-factor (unmodified) and M-factor (modified), respectively. These pheromones are specifically recognized by receptors on the cell surface of cells of opposite mating types, which trigger a pheromone response. Recognition between pheromones and their corresponding receptors is important for mate discrimination; therefore, genetic changes in pheromone or receptor genes affect mate recognition and cause reproductive isolation that limits gene flow between populations. Such genetic variation in recognition via the pheromone/receptor system may drive speciation. Our recent studies reported that two pheromone receptors in S. pombe might have different stringencies in pheromone recognition. In this review, we focus on the molecular mechanism of pheromone response and mating behavior, emphasizing pheromone diversification and its impact on reproductive isolation in S. pombe and closely related fission yeast species. We speculate that the “asymmetric” system might allow flexible adaptation to pheromone mutational changes while maintaining stringent recognition of mating partners. The loss of pheromone activity results in the extinction of an organism’s lineage. Therefore, genetic changes in pheromones and their receptors may occur gradually and/or coincidently before speciation. Our findings suggest that the M-factor plays an important role in partner discrimination, whereas P-factor communication allows flexible adaptation to create variations in S. pombe. Our inferences provide new insights into the evolutionary mechanisms underlying pheromone diversification.
Differential alterations of amygdala nuclei volumes in acutely ill patients with anorexia nervosa and their associations with leptin levels

Abstract Background The amygdala is a subcortical limbic structure consisting of histologically and functionally distinct subregions. New automated structural magnetic resonance imaging (MRI) segmentation tools facilitate the in vivo study of individual amygdala nuclei in clinical populations such as patients with anorexia nervosa (AN) who show symptoms indicative of limbic dysregulation. This study is the first to investigate amygdala nuclei volumes in AN, their relationships with leptin, a key indicator of AN-related neuroendocrine alterations, and further clinical measures. Methods T1-weighted MRI scans were subsegmented and multi-stage quality controlled using FreeSurfer. Left/right hemispheric amygdala nuclei volumes were cross-sectionally compared between females with AN (n = 168, 12–29 years) and age-matched healthy females (n = 168) applying general linear models. Associations with plasma leptin, body mass index (BMI), illness duration, and psychiatric symptoms were analyzed via robust linear regression. Results Globally, most amygdala nuclei volumes in both hemispheres were reduced in AN v. healthy control participants. Importantly, four specific nuclei (accessory basal, cortical, medial nuclei, corticoamygdaloid transition in the rostral-medial amygdala) showed greater volumetric reduction even relative to reductions of whole amygdala and total subcortical gray matter volumes, whereas basal, lateral, and paralaminar nuclei were less reduced. All rostral-medially clustered nuclei were positively associated with leptin in AN independent of BMI. Amygdala nuclei volumes were not associated with illness duration or psychiatric symptom severity in AN. Conclusions In AN, amygdala nuclei are altered to different degrees. Severe volume loss in rostral-medially clustered nuclei, collectively involved in olfactory/food-related reward processing, may represent a structural correlate of AN-related symptoms. Hypoleptinemia might be linked to rostral-medial amygdala alterations.
Vortex shedding from a submerged rectangular obstacle attacked by a solitary wave

This study investigates the two-dimensional flow of a solitary wave that passes over a submerged rectangular obstacle using a Lagrangian-type numerical method. The main purpose is to investigate vortex generation and evolution caused by the obstacle. The numerical method is based on the combination of vortex methods and boundary integral methods using the Helmholtz decomposition. The simulated flow pattern is compared with the experimental measurements in detail, and the overall agreement is reasonably good. A series of simulations were performed with various wave heights to study the effect of wave height on vortex generation and evolution. The relation between the vorticity field and the drag experienced by the obstacle is also discussed. In the presented cases, the effects of the generated vortices are preserved over a long period, and may cause local scouring of the foundation at the lee side of the obstacle. The deformation of the solitary wave is not much affected by the presence of the vortices, but the drag is significantly affected by the vorticity field. An almost linear relationship between the Reynolds number and the maximum magnitude of the drag (positive and negative) is observed.
SOME PROPERTIES OF LOCAL AND NONLOCAL SITE EXCHANGE DETERMINISTIC CELLULAR AUTOMATA

Stationary densities of nonzero sites of one-dimensional range-one deterministic cellular automata are shown to be functions of a parameter m characterizing the degree of mixing of site values. The mixing results from either a local or a nonlocal exchange of the site values. In particular, the asymptotic behavior of the stationary densities of nonzero sites for very small and very large values of the parameter m have been determined.
Rule Generation for Classification: Scalability, Interpretability, and Fairness

We introduce a new rule-based optimization method for classification with constraints. The proposed method leverages column generation for linear programming, and hence, is scalable to large datasets. The resulting pricing subproblem is shown to be NP-Hard. We recourse to a decision tree-based heuristic and solve a proxy pricing subproblem for acceleration. The method returns a set of rules along with their optimal weights indicating the importance of each rule for learning. We address interpretability and fairness by assigning cost coefficients to the rules and introducing additional constraints. In particular, we focus on local interpretability and generalize separation criterion in fairness to multiple sensitive attributes and classes. We test the performance of the proposed methodology on a collection of datasets and present a case study to elaborate on its different aspects. The proposed rule-based learning method exhibits a good compromise between local interpretability and fairness on the one side, and accuracy on the other side.
D EMYSTIFYING A PPROXIMATE RL WITH (cid:15) - GREEDY E XPLORATION : A D IFFERENTIAL I NCLUSION V IEW

Q-learning and SARSA(0) with -greedy exploration are leading reinforcement learning methods, and their tabular forms converge to the optimal Q-function under reasonable conditions. However, with function approximation, these methods exhibit strange behaviors, e.g., policy oscillation and chattering, convergence to different attractors (possibly even the worst policy) on different runs, etc., apart from the usual instability. Accordingly, a theory to explain these phenomena has been a long-standing open problem, even for basic linear function approximation (Sutton, 1999). Our work uses differential inclusion theory to provide the first framework for resolving this problem. We further illustrate via numerical examples how this framework helps explain these algorithms’ asymptotic behaviors.
FIRe: Fast Inverse Rendering using Directional and Signed Distance Functions

Neural 3D implicit representations learn priors that are useful for diverse applications, such as single- or multiple-view 3D reconstruction. A major downside of existing approaches while rendering an image is that they require evaluating the network multiple times per camera ray so that the high computational time forms a bottleneck for downstream applications. We address this problem by introducing a novel neural scene representation that we call the directional distance function (DDF). To this end, we learn a signed distance function (SDF) along with our DDF model to represent a class of shapes. Specifically, our DDF is defined on the unit sphere and predicts the distance to the surface along any given direction. Therefore, our DDF allows rendering images with just a single network evaluation per camera ray. Based on our DDF, we present a novel fast algorithm (FIRe) to reconstruct 3D shapes given a posed depth map. We evaluate our proposed method on 3D reconstruction from single-view depth images, where we empirically show that our algorithm reconstructs 3D shapes more accurately and it is more than 15 times faster (per iteration) than competing methods.
Resilient Edge Service Placement Under Demand and Node Failure Uncertainties

Resiliency plays a critical role in designing future communication networks. How to make edge computing systems resilient against unpredictable failures and fluctuating demand is an important and challenging problem. To this end, this paper investigates a resilient service placement and workload allocation problem for a service provider (SP) who can procure resources from numerous edge nodes to serve its users, considering both resource demand and node failure uncertainties. We introduce a novel two-stage adaptive robust model to capture this problem. The service placement and resource procurement decisions are optimized in the first stage, while the workload allocation decision is determined in the second stage after the uncertainty realization. By exploiting the special structure of the uncertainty set, we develop an efficient iterative algorithm that can converge to an exact optimal solution within a finite number of iterations. However, the running time of this iterative algorithm heavily depends on the uncertainty set. Therefore, we further present an affine decisions rule approximation approach, which exhibits greater insensitivity to the uncertainty set, to tackle the underlying problem. Extensive numerical results demonstrate the advantages of the proposed model and approaches, which can help the SP make proactive decisions to mitigate the impacts of the uncertainties.
Downlink Transmission With Heterogeneous URLLC Services: Discrete Signaling With Single-User Decoding

The problem of designing downlink transmission schemes for supporting heterogeneous ultra-reliable low-latency communications (URLLC) and/or with other types of services is investigated. We consider the broadcast channel, where the base station sends superimposed signals to multiple users. Under heterogeneous blocklength constraints, strong users who are URLLC users cannot wait to receive the entire transmission frame and perform successive interference cancellation (SIC) due to stringent latency requirements, in contrast to the conventional infinite blocklength cases. Even if SIC is feasible, SIC may be imperfect under finite blocklength constraints. To cope with the heterogeneity in latency and reliability requirements, we propose a practical downlink transmission scheme with discrete signaling and single-user decoding (SUD), i.e., without SIC. We carefully design the discrete input distributions to enable efficient SUD by exploiting the structural interference. Furthermore, we derive the second-order achievable rate under heterogenous blocklength and error probability constraints and use it to guide the design of channel coding and modulations. It is shown that in terms of achievable rate under short blocklength, the proposed scheme with regular quadrature amplitude modulations and SUD can operate extremely close to the benchmark schemes that assume perfect SIC with Gaussian signaling.
Policy Learning with the polle package

The R package polle is a unifying framework for learning and evaluating finite stage policies based on observational data. The package implements a collection of existing and novel methods for causal policy learning including doubly robust restricted Q-learning, policy tree learning, and outcome weighted learning. The package deals with (near) positivity violations by only considering realistic policies. Highly flexible machine learning methods can be used to estimate the nuisance components and valid inference for the policy value is ensured via cross-fitting. The library is built up around a simple syntax with four main functions policy_data(), policy_def(), policy_learn(), and policy_eval() used to specify the data structure, define user-specified policies, specify policy learning methods and evaluate (learned) policies. The functionality of the package is illustrated via extensive reproducible examples.
Fusion reactivities with drift bi-Maxwellian ion velocity distributions

The calculation of fusion reactivity involves a complex six-dimensional integral that takes into account the fusion cross section and velocity distributions of two reactants. However, a more simplified one-dimensional integral form can be useful in certain cases, such as for studying fusion yield or diagnosing ion energy spectra. This simpler form has been derived in a few special cases, such as for a combination of two Maxwellian distributions, a beam-Maxwellian combination, and a beam-target combination, and can greatly reduce computational costs. In this study, it is shown that the reactivity for two drift bi-Maxwellian reactants with different drift velocities, temperatures, and anisotropies can also be reduced to a one-dimensional form, unifying existing derivations into a single expression. This result is used to investigate the potential enhancement of fusion reactivity due to the combination of beam and temperature anisotropies. For relevant parameters in fusion energy, the enhancement factor can be larger than 20%, which is particularly significant for proton-boron (p–B11) fusion, as this factor can have a significant impact on the Lawson fusion gain criteria.
Proximity effect in PbTe-Pb hybrid nanowire Josephson junctions

Semiconductor-superconductor hybrid nanowires are a leading material platform for the realization of Majorana zero modes. The semiconductors in previous studies are dominantly InAs or InSb. In this work, we show the induced superconductivity in a PbTe nanowire epitaxially coupled to a superconductor Pb. The Josephson junction device based on this hybrid reveals a gate-tunable supercurrent in the open regime and a hard superconducting gap in the tunneling regime. By demonstrating the superconducting proximity effect, our result can enable Majorana searches and other applications like gate-tunable qubits in a new semiconductor system.
CrossSplit: Mitigating Label Noise Memorization through Data Splitting

We approach the problem of improving robustness of deep learning algorithms in the presence of label noise. Building upon existing label correction and co-teaching methods, we propose a novel training procedure to mitigate the memorization of noisy labels, called CrossSplit, which uses a pair of neural networks trained on two disjoint parts of the labelled dataset. CrossSplit combines two main ingredients: (i) Cross-split label correction. The idea is that, since the model trained on one part of the data cannot memorize example-label pairs from the other part, the training labels presented to each network can be smoothly adjusted by using the predictions of its peer network; (ii) Cross-split semi-supervised training. A network trained on one part of the data also uses the unlabeled inputs of the other part. Extensive experiments on CIFAR-10, CIFAR-100, Tiny-ImageNet and mini-WebVision datasets demonstrate that our method can outperform the current state-of-the-art in a wide range of noise ratios.