Miracle-dz/UniFAR-FTUs huggingface dataset:
strap: an R package for plotting phylogenies against stratigraphy and assessing their stratigraphic congruence

strap (Stratigraphic Tree Analysis for Palaeontology) is a new package for the freely available statistical programming language R designed to perform three main tasks: (1) to time‐scale phylogenies of fossil taxa; (2) to plot those time‐scaled trees against stratigraphy; and (3) to assess congruence between phylogenies and stratigraphy. Time‐scaling is performed with the DatePhylo function, with three approaches offered. Plotting trees against a choice of five different geological time scaless is possible using the geoscalePhylo function. Finally, the function StratPhyloCongruence calculates stratigraphic congruence measures for one or more input phylogenies, with no taxon limit. All three major congruence measures are offered: Stratigraphic Consistency Index (SCI), Manhattan Stratigraphic Measure (MSM*) and the gap excess ratio (GER; including GERt and GER*), as well as the pseudocongruence measure, the Relative Completeness Index (RCI). Each measure has an accompanying significance test that works by comparing the input trees against a user‐defined number of randomly generated topologies with the same taxon set and age ranges. Additional options for generating these random topologies allow the user to fix the outgroup or retain the input tree shape to make fairer comparisons. A tutorial that assumes no prior knowledge of R showcases all three functions using two different example data sets.
New data about the Piedimonte Formation (Monte Soro Unit): insights and constraints to the geodynamic evolution of the Sicilian Maghrebids

A detailed study of the siliciclastic Piedimonte Fm (Sicilian Maghrebids), cropping out in north-eastern Sicily, between the Peloritani Mts. and the Etna Mt., has been carried out. The formation is tectonically covered by the Stilo-Capo d'Orlando Fm, which constitutes a siliciclastic succession unconformable above all the Internal Units of the Sicilian Maghrebids. The Piedimonte Fm resulted younger than the age previously considered in literature, being not older than late Oligocene (Late Chattian) at the base, and not older than early Miocene (Aquitanian) in the upper part. The Piedimonte Fm stratigraphically lies above the Variegated Clays Fm that, in turn, stratigraphically rests above the Monte Soro Fm. Therefore, the Piedimonte Fm constitutes the uppermost part of the Monte Soro Unit, which is re-defined in the present paper and which, from a paleogeographic point of view, is to ascribe to the innermost part (Mauretanian sub-domain) of the Maghrebian Flysch Domain. According to these results, the main phase of deformation of the most internal sector of the Sicilian Maghrebian Flysch Domain occurred not before than Aquitanian, in agreement with the Burdigalian age recognised in more external units.
Which mode is better for federated learning? Centralized or Decentralized

Both centralized and decentralized approaches have shown excellent performance and great application value in federated learning (FL). However, current studies do not provide sufficient evidence to show which one performs better. Although from the optimization perspective, decentralized methods can approach the comparable convergence of centralized methods with less communication, its test performance has always been inefficient in empirical studies. To comprehensively explore their behaviors in FL, we study their excess risks, including the joint analysis of both optimization and generalization. We prove that on smooth non-convex objectives, 1) centralized FL (CFL) always generalizes better than decentralized FL (DFL); 2) from perspectives of the excess risk and test error in CFL, adopting partial participation is superior to full participation; and, 3) there is a necessary requirement for the topology in DFL to avoid performance collapse as the training scale increases. Based on some simple hardware metrics, we could evaluate which framework is better in practice. Extensive experiments are conducted on common setups in FL to validate that our theoretical analysis is contextually valid in practical scenarios.
Hunting Gamma-Ray-emitting FR0 Radio Galaxies in Wide-field Sky Surveys

The latest entry in the jetted active galactic nuclei (AGN) family is the Fanaroff–Riley type 0 (FR0) radio galaxies. They share several observational characteristics, e.g., nuclear emission and host galaxy morphology, with FR I sources; however, they lack extended, kiloparsec-scale radio structures, which are the defining features of canonical FR I and II sources. Here we report the identification of seven γ-ray-emitting AGN as FR0 radio sources by utilizing the high-quality observations delivered by ongoing multiwavelength wide-field sky surveys, e.g., Very Large Array Sky Survey. The broadband observational properties of these objects are found to be similar to their γ-ray undetected counterparts. In the γ-ray band, FR0 radio galaxies exhibit spectral features similar to more common FR I and II radio galaxies, indicating a common γ-ray production mechanism and the presence of misaligned jets. Although the parsec-scale radio structure of FR0s generally exhibits a wide range, with about half having emission on opposite sides of the core, the γ-ray-detected FR0s tend to have dominant cores with core-jet structures. We conclude that dedicated, high-resolution observations are needed to unravel the origin of relativistic jets in this enigmatic class of faint yet numerous population of compact radio sources.
IoTScent: Enhancing Forensic Capabilities in Internet of Things Gateways

The widespread deployment of Consumer Internet of Things devices in proximity to human activities makes them digital observers of our daily actions. This has led to a new field of digital forensics, known as IoT Forensics, where digital traces generated by IoT devices can serve as key evidence for forensic investigations. Thus, there is a need to develop tools that can efficiently acquire and store network traces from IoT ecosystems. This paper presents IoTScent, an open-source IoT forensic tool that enables IoT gateways and Home Automation platforms to perform IoT traffic capture and analysis. Unlike other works focusing on IP-based protocols, IoTScent is specifically designed to operate over IEEE 802.15. 4-based traffic, which is the basis for many IoT-specific protocols such as Zigbee, 6LoWPAN and Thread. IoTScent offers live traffic capture and feature extraction capabilities, providing a framework for forensic data collection that simplifies the task of setting up a data collection pipeline, automating the data collection process, and providing ready-made features that can be used for forensic evidence extraction. This work provides a comprehensive description of the IoTScent tool, including a practical use case that demonstrates the use of the tool to perform device identification from Zigbee traffic. The study presented here significantly contributes to the ongoing research in IoT Forensics by addressing the challenges faced in the field and publicly releasing the IoTScent tool.
Disentangling the Effects of Structure and Lone-Pair Electrons in the Lattice Dynamics of Halide Perovskites

Metal halide perovskites have shown great performance as solar energy materials, but their outstanding optoelectronic properties are paired with unusually strong anharmonic effects. It has been proposed that this intriguing combination of properties derives from the"lone pair"6$s^2$ electron configuration of the Pb$^{2+}$ cations, and associated weak pseudo-Jahn-Teller effect, but the precise impact of this chemical feature remains unclear. Here we show that in fact an $ns^2$ electron configuration is not a prerequisite for the strong anharmonicity and low-energy lattice dynamics encountered in this class of materials. We combine X-ray diffraction, infrared and Raman spectroscopies, and first-principles molecular dynamics calculations to directly contrast the lattice dynamics of CsSrBr$_3$ with those of CsPbBr$_3$, two compounds which bear close structural similarity but with the former lacking the propensity to form lone pairs on the 5$s^0$ octahedral cation. We exploit low-frequency diffusive Raman scattering, nominally symmetry-forbidden in the cubic phase, as a fingerprint to detect anharmonicity and reveal that low-frequency tilting occurs irrespective of octahedral cation electron configuration. This work highlights the key role of structure in perovskite lattice dynamics, providing important design rules for the emerging class of soft perovskite semiconductors for optoelectronic and light-harvesting devices.
Posterior Sampling Based on Gradient Flows of the MMD with Negative Distance Kernel

We propose conditional flows of the maximum mean discrepancy (MMD) with the negative distance kernel for posterior sampling and conditional generative modeling. This MMD, which is also known as energy distance, has several advantageous properties like efficient computation via slicing and sorting. We approximate the joint distribution of the ground truth and the observations using discrete Wasserstein gradient flows and establish an error bound for the posterior distributions. Further, we prove that our particle flow is indeed a Wasserstein gradient flow of an appropriate functional. The power of our method is demonstrated by numerical examples including conditional image generation and inverse problems like superresolution, inpainting and computed tomography in low-dose and limited-angle settings.
B-Coder: Value-Based Deep Reinforcement Learning for Program Synthesis

Program synthesis aims to create accurate, executable programs from problem specifications, specifically from natural language descriptions in our context. Recent studies have leveraged the power of reinforcement learning (RL) in conjunction with large language models (LLMs), significantly enhancing code generation capabilities. The application of RL focuses on directly optimizing for functional correctness, offering an advantage over conventional supervised methods. Despite policy-based RL methods dominating the literature on RL for program synthesis, the nature of program synthesis tasks hints at a natural alignment with value-based methods. This stems from the rich collection of off-policy programs, including those developed by human programmers and also historical samples, coupled with the straightforward verification of generated programs through automated unit testing, meaning rewards are easy to obtain. Diverging from the dominant use of policy-based algorithms, our work explores the feasibility of value-based approaches, leading to the development of our $\mathcal{B}$-Coder (pronounced Bellman coder). Yet, training value-based methods presents challenges due to the enormous search space inherent to program synthesis. To this end, we introduce an initialization protocol for RL agents utilizing pre-trained LMs and a conservative Bellman operator to reduce training complexities. Moreover, we demonstrate how to leverage the learned value functions as a dual strategy to post-process generated programs. Our empirical evaluations demonstrated $\mathcal{B}$-Coder's capability in achieving state-of-the-art performance when compared to policy-based methods. Remarkably, this achievement is reached with minimal reward engineering effort, highlighting the effectiveness of value-based RL, independent of reward designs.
Response of a shrub‐steppe ecosystem to fire: Soil water and vegetational change

Abstract A wildfire in August 1984 eliminated big sagebrush from a shrub‐steppe community in southeastern Washington. Bluebunch wheatgrass dominated the community in subsequent years. By comparing leaf area index and soil water storage on the burned site with an adjacent unburned site containing big sagebrush, we were able to test the hypotheses that plant community structure controls soil moisture dynamics and that wildfire can alter the water balance in these arid landscapes. Elimination of big sagebrush from the landscape greatly reduced the community leaf area index. Soil water storage computed to a depth of 125 cm was not affected by the change in community structure. However, storage computed to a depth of 275 cm was significantly greater on the burned site compared to the unburned site. This difference increased with time after the fire. Wildfire in shrub‐steppe communities dominated by big sagebrush effectively removes the deep rooted, woody component from these ecosystems. Consequently, the abili.
Polar cap F layer patches: Structure and dynamics

Coordinated measurements of F region plasma patches were conducted on February 3/4, 1984, from Thule and Sondrestrom, Greenland. Optical, ionosonde, amplitude scintillation, total electron content (TEC), and incoherent scatter radar measurements were combined to reveal several new aspects of the structure and transport of these localized regions of enhanced F region ionization. For the first time these patches were directly tracked flowing in the antisunward direction over distances of 3000 km from the center of the polar cap to the poleward edge of the auroral oval. Quantitative measurements of TEC show increases of 10--15 TEC units within the patches, above a background polar cap value of 5 TEC units. Amplitude scintillation measurements show the presence of ionospheric irregularities through the entire patch, with a weak indication of stronger scintillation on the trailing (or E x B unstable) edge.
On the remarkable Arctic winter in 2008/2009

It is well known that the interannual variability of the stratospheric winters over the Arctic is very large. On the basis of data for more than 60 winters, this variability has been studied with the aim of understanding and possibly forecasting the type of the coming winter, in the stratosphere and also in the troposphere. Today, there is general agreement that the variability of the stratospheric circulation during the Arctic winters is influenced by different forcing mechanisms: by the tropospheric planetary waves which penetrate into the stratosphere, by the Quasi‐Biennial Oscillation (QBO) and the Southern Oscillation (SO) in the tropics which influence the stratospheric polar vortex, and by the 11‐year sunspot cycle which interacts with the QBO and probably also with the SO. For the winter 2008/2009, all of the known signals pointed to a stable, cold stratospheric polar vortex throughout the winter, but in the real atmosphere a major midwinter warming developed in January and February with record‐breaking temperatures. The synoptics of this winter will be discussed in the context of all of the above‐mentioned forcing mechanisms.
Large Language Models for Software Engineering: Survey and Open Problems

This paper provides a survey of the emerging area of Large Language Models (LLMs) for Software Engineering (SE). It also sets out open research challenges for the application of LLMs to technical problems faced by software engineers. LLMs' emergent properties bring novelty and creativity with applications right across the spectrum of Software Engineering activities including coding, design, requirements, repair, refactoring, performance improvement, documentation and analytics. However, these very same emergent properties also pose significant technical challenges; we need techniques that can reliably weed out incorrect solutions, such as hallucinations. Our survey reveals the pivotal role that hybrid techniques (traditional SE plus LLMs) have to play in the development and deployment of reliable, efficient and effective LLM-based SE.
Kinetic Modeling Analysis of Ar Addition to Atmospheric Pressure N2-H2 Plasma for Plasma-Assisted Catalytic Synthesis of NH3.

Zero-dimensional kinetic modeling of atmospheric pressure Ar-N2-H2 nonthermal plasma was carried out to gain mechanistic insights into plasma-assisted catalytic synthesis of ammonia. Ar dilution is a common technique for tailoring plasma discharge properties and has been shown to enhance NH3 formation when added to N2-H2 plasma. The kinetic model was developed for a coaxial dielectric barrier discharge quartz wool-packed bed reactor operating at near room temperature using a kHz-frequency plasma source. With 30% Ar mixed in a 1:1 N2-H2 plasma at 760 Torr, we find that NH3 production is dominated by Eley-Rideal (E-R) surface reactions, which heavily involve surface NHx species derived from N and H radicals in the gas phase, while the influence of excited N2 molecules is negligible. This is contrary to the commonly proposed mechanism that excited N2 molecules created by Penning excitation of N2 by Ar(4s) and Ar(4p) play a significant role in assisting NH3 formation. Our model shows that the enhanced NH3 formation upon Ar dilution is unlikely due to the interactions between Ar and H species, as excited Ar atoms have a weak effect on H radical formation through H2 dissociation compared to electrons. We find that excited Ar atoms contribute to 28% of the N radical production in the gas phase via N2 dissociation, while the rest are dominated by electron-impact dissociation. Furthermore, Ar species play a negligible role in the product NH3 dissociation. N2 conversion sensitivity analyses were carried out for electron number density (ne) and reduced electric field (E/N), and contributions from Ar to gas-phase N radical production were quantified. The model can provide guidance on potential reasons for observing enhanced NH3 formation upon Ar dilution in N2-H2 plasma beyond changes in the discharge characteristics.
On the geometry of rate independent droplet evolution

We consider a toy model of rate independent droplet motion on a surface with contact angle hysteresis based on the one-phase Bernoulli free boundary problem. Taking advantage of two notions of weak solutions, energy-based and comparison-principle-based, we study the dynamic contact angle of moving contact lines and the geometry of de-pinning. We show that these two notions of solutions coincide in a star-shaped setting, where we show (almost) optimal regularity of the contact line and the convergence of a minimizing movements scheme. In a general setting, the notions differ essentially in how they handle jumps, but both are shown to satisfy a weak motion law.
Effects of sea surface temperature and greenhouse gas changes on the transport between the stratosphere and troposphere

[1] The effects of sea surface temperature (SST) and greenhouse gas (GHG) changes on the mean age-of-air and water vapor are investigated using a state-of-the-art general circulation model (GCM), and general characteristics of tracer transport between the stratosphere and troposphere are analyzed. Downward tracer transport in the northern midlatitude stratosphere is found to be faster than at southern midlatitudes. The global mean downward transport to the troposphere from stratosphere mainly occurs during northern winter and the downward cross-tropopause transport is weakest from August to October. The maximum troposphere mean (TM) age-of-air, derived from an age tracer released near the stratopause (around 1 hPa), can reach 13 years and is much larger than the maximum stratosphere mean (SM) age-of-air derived from an analogous age tracer released in the troposphere, with the SM age-of-air in the Northern Hemisphere being younger than in the Southern Hemisphere. Increased SSTs tend to accelerate upward transport through the stratosphere and slow downward transport in midlatitudes and the tropical stratosphere. In the context of effects of GHG increases and the associated SST increases on the stratosphere mean age-of-air, the GHG effects dominate, i.e., changes in the stratospheric mean age-of-air caused by SST increases only are smaller than those caused by combined changes in SSTs and GHGs. An increase in SSTs enhances the upward Eliasen-Palm (EP) flux in the extratropics. A 7.7% enhancement of tropical upwelling can be caused by a uniform 1.5 K SST increase. When both SST and GHG values are increased to the 2100 conditions, the meridional heat flux decreases in both winter hemispheres (and statistically significantly in the Southern Hemisphere). Meanwhile, the EP flux in the Northern Hemisphere increases significantly and the tropical upwelling is enhanced by 15% compared to the present-day conditions.
Plug-and-Play Posterior Sampling under Mismatched Measurement and Prior Models

Posterior sampling has been shown to be a powerful Bayesian approach for solving imaging inverse problems. The recent plug-and-play unadjusted Langevin algorithm (PnP-ULA) has emerged as a promising method for Monte Carlo sampling and minimum mean squared error (MMSE) estimation by combining physical measurement models with deep-learning priors specified using image denoisers. However, the intricate relationship between the sampling distribution of PnP-ULA and the mismatched data-fidelity and denoiser has not been theoretically analyzed. We address this gap by proposing a posterior-L2 pseudometric and using it to quantify an explicit error bound for PnP-ULA under mismatched posterior distribution. We numerically validate our theory on several inverse problems such as sampling from Gaussian mixture models and image deblurring. Our results suggest that the sensitivity of the sampling distribution of PnP-ULA to a mismatch in the measurement model and the denoiser can be precisely characterized.
A High-Performance Design, Implementation, Deployment, and Evaluation of The Slim Fly Network

Novel low-diameter network topologies such as Slim Fly (SF) offer significant cost and power advantages over the established Fat Tree, Clos, or Dragonfly. To spearhead the adoption of low-diameter networks, we design, implement, deploy, and evaluate the first real-world SF installation. We focus on deployment, management, and operational aspects of our test cluster with 200 servers and carefully analyze performance. We demonstrate techniques for simple cabling and cabling validation as well as a novel high-performance routing architecture for InfiniBand-based low-diameter topologies. Our real-world benchmarks show SF's strong performance for many modern workloads such as deep neural network training, graph analytics, or linear algebra kernels. SF outperforms non-blocking Fat Trees in scalability while offering comparable or better performance and lower cost for large network sizes. Our work can facilitate deploying SF while the associated (open-source) routing architecture is fully portable and applicable to accelerate any low-diameter interconnect.
Certifiably Robust Graph Contrastive Learning

Graph Contrastive Learning (GCL) has emerged as a popular unsupervised graph representation learning method. However, it has been shown that GCL is vulnerable to adversarial attacks on both the graph structure and node attributes. Although empirical approaches have been proposed to enhance the robustness of GCL, the certifiable robustness of GCL is still remain unexplored. In this paper, we develop the first certifiably robust framework in GCL. Specifically, we first propose a unified criteria to evaluate and certify the robustness of GCL. We then introduce a novel technique, RES (Randomized Edgedrop Smoothing), to ensure certifiable robustness for any GCL model, and this certified robustness can be provably preserved in downstream tasks. Furthermore, an effective training method is proposed for robust GCL. Extensive experiments on real-world datasets demonstrate the effectiveness of our proposed method in providing effective certifiable robustness and enhancing the robustness of any GCL model. The source code of RES is available at https://github.com/ventr1c/RES-GCL.