Miracle-dz/UniFAR-FTUs huggingface dataset:
Distribution Locational Marginal Emission for Carbon Alleviation in Distribution Networks: Formulation, Calculation, and Implication

Regulating the proper carbon-aware intervention policy is one of the keys to emission alleviation in the distribution network, whose basis lies in effectively attributing the emission responsibility using emission factors. This paper establishes the distribution locational marginal emission (DLME) to calculate the marginal change of emission from the marginal change of both active and reactive load demand for incentivizing carbon alleviation. It first formulates the day-head distribution network scheduling model based on the second-order cone program (SOCP). The emission propagation and responsibility are analyzed from demand to supply to system emission. Considering the complex and implicit mapping of the SOCP-based scheduling model, the implicit theorem is leveraged to exploit the optimal condition of SOCP. The corresponding SOCP-based implicit derivation approach is proposed to calculate the DLMEs effectively in a model-based way. Comprehensive numerical studies are conducted to verify the superiority of the proposed method by comparing its calculation efficacy to the conventional marginal estimation approach, assessing its effectiveness in carbon alleviation with comparison to the average emission factors, and evaluating its carbon alleviation ability of reactive DLME.
Spin orbit resonance cascade via core shell model. Application to Mercury and Ganymede

We discuss a model describing the spin orbit resonance cascade. We assume that the primary has a two-layer (core-shell) structure: it is composed by a thin solid crust and an inner and heavier solid core that are interacting due to the presence of a fluid interface. We assume two sources of dissipation: a viscous one, depending on the relative angular velocity between core and crust and a tidal one, smaller than the first, due to the viscoelastic structure of the core. We show how these two sources of dissipation are needful for the capture in spin-orbit resonance. The crust and the core fall in resonance with different time scales if the viscous coupling between them is big enough. Finally, the tidal dissipation of the viscoelastic core, decreasing the eccentricity, brings the system out of the resonance in a third very long time scale. This mechanism of entry and exit from resonance ends in the $1:1$ stable state.
Make it more specific: A novel uncertainty based airway segmentation application on 3D U-Net and its variants

Each medical segmentation task should be considered with a specific AI algorithm based on its scenario so that the most accurate prediction model can be obtained. The most popular algorithms in medical segmentation, 3D U-Net and its variants, can directly implement the task of lung trachea segmentation, but its failure to consider the special tree-like structure of the trachea suggests that there is much room for improvement in its segmentation accuracy. Therefore, a research gap exists because a great amount of state-of-the-art DL algorithms are vanilla 3D U-Net structures, which do not introduce the various performance-enhancing modules that come with special natural image modality in lung airway segmentation. In this paper, we proposed two different network structures Branch-Level U-Net (B-UNet) and Branch-Level CE-UNet (B-CE-UNet) which are based on U-Net structure and compared the prediction results with the same dataset. Specially, both of the two networks add branch loss and central line loss to learn the feature of fine branch endings of the airways. Uncertainty estimation algorithms are also included to attain confident predictions and thereby, increase the overall trustworthiness of our whole model. In addition, predictions of the lung trachea based on the maximum connectivity rate were calculated and extracted during post-processing for segmentation refinement and pruning.
Empowering Federated Learning for Massive Models with NVIDIA FLARE

In the ever-evolving landscape of artificial intelligence (AI) and large language models (LLMs), handling and leveraging data effectively has become a critical challenge. Most state-of-the-art machine learning algorithms are data-centric. However, as the lifeblood of model performance, necessary data cannot always be centralized due to various factors such as privacy, regulation, geopolitics, copyright issues, and the sheer effort required to move vast datasets. In this paper, we explore how federated learning enabled by NVIDIA FLARE can address these challenges with easy and scalable integration capabilities, enabling parameter-efficient and full supervised fine-tuning of LLMs for natural language processing and biopharmaceutical applications to enhance their accuracy and robustness.
An elementary approach to mixing and dissipation enhancement by transport noise

We investigate the mixing properties of solutions to the stochastic transport equation $d u= \circ d W \cdot\nabla u$, where the driving noise $W(t,x)$ is white in time, colored and divergence-free in space. Furthermore, we prove the dissipation enhancement in the presence of a small viscous term. Applying our results, we also derive the mixing properties for a regularized stochastic 2D Euler equation.
Consistent eccentricities for gravitational wave astronomy: Resolving discrepancies between astrophysical simulations and waveform models

Detecting imprints of orbital eccentricity in gravitational wave signals promises to shed light on the formation mechanisms of binary black holes. To constrain formation mechanisms, distributions of eccentricity derived from numerical simulations of astrophysical formation channels are compared to the estimates of eccentricity inferred from GW signals. We report that the definition of eccentricity typically used in astrophysical simulations is inconsistent with the one used while modeling GW signals, with the differences mainly arising due to the choice of reference frequency used in both cases. We also posit a prescription for calculating eccentricity from astrophysical simulations by evolving ordinary differential equations obtained from post-Newtonian theory, and using the dominant ($\ell = m =2$) mode's frequency as the reference frequency; this ensures consistency in the definitions. On comparing the existing eccentricities of binaries present in the Cluster Monte Carlo catalog of globular cluster simulations with the eccentricities calculated using the prescription presented here, we find a significant discrepancy at $e \gtrsim 0.2$; this discrepancy becomes worse with increasing eccentricity. We note the implications this discrepancy has for existing studies, and recommend that care be taken when comparing data-driven constraints on eccentricity to expectations from astrophysical formation channels.
Global well-posedness and asymptotic behavior for the Euler-alignment system with pressure

We study the Cauchy problem of the compressible Euler system with strongly singular velocity alignment. We establish a global well-posedness theory for the system with small smooth initial data. Additionally, we derive asymptotic emergent behaviors for the system, providing time decay estimates with optimal decay rates. Notably, the optimal decay rate we obtain does not align with the corresponding fractional heat equation within our considered range, where the parameter $\alpha\in(0,1)$. This highlights the distinct feature of the alignment operator.
MAFIA: Multi-Adapter Fused Inclusive Language Models

Pretrained Language Models (PLMs) are widely used in NLP for various tasks. Recent studies have identified various biases that such models exhibit and have proposed methods to correct these biases. However, most of the works address a limited set of bias dimensions independently such as gender, race, or religion. Moreover, the methods typically involve finetuning the full model in order to maintain the performance on the downstream task. In this work, we aim to modularly debias a pre-trained language model across multiple dimensions. Previous works extensively explored debiasing PLMs by using limited US-centric counterfactual data augmentation (CDA). We use structured knowledge and a large generative model to build a diverse CDA across multiple bias dimensions in a semi-automated way. We highlight how existing debiasing methods do not consider interactions between multiple societal biases and propose a debiasing model that exploits the synergy amongst various societal biases and enables multi-bias debiasing simultaneously. An extensive evaluation on multiple tasks and languages demonstrates the efficacy of the approach.
On co-dimension 2 defect anomalies in N=4 SYM and (2,0) theory via brane probes in AdS/CFT

We consider a $\frac{1}{2}$-BPS solution for a D3 brane probe in AdS$_5 \times S^5$ that has world-volume geometry of AdS$_3 \times S^1$. It intersects the boundary over a surface that represents a dimension 2 defect in the boundary N=4 SYM theory. The effective action of the probe brane is proportional to the logarithmically divergent volume of AdS$_3$ and may thus be interpreted as computing conformal anomaly of the supersymmetric $S^2$ defect. The classical action scales as $N$. We compute the 1-loop correction to it due to quantum fluctuations of the D3 brane world-volume fields and compare the result to an earlier suggested expression for the defect anomaly. We also perform a similar analysis of a $\frac{1}{2}$-BPS M5 brane probe solution in AdS$_7 \times S^4$ with the world-volume geometry of AdS$_5 \times S^1$ that represents a dimension 4 defect in the boundary (2,0) 6d theory. Here the classical M5 brane action computes the leading order $N^2$ term in $a$-anomaly of the supersymmetric $S^4$ defect. We perform a detailed computation of the 1-loop correction to the M5 brane effective action and thus provide a prediction for the subleading constant in the $S^4$ defect $a$-anomaly coefficient.
Fermi arcs mediated transport in inversion symmetry-broken Weyl semimetal nanowire and its hybrid junctions

The emergence of gapless surface states, known as Fermi arcs (FAs), is one of the unique properties of the novel topological Weyl semimetal (WSM). However, extracting the signatures of FAs from the bulk states has always been a challenge as both of them are gapless in nature and connected to each other. We capture the signatures of FAs via transport in an inversion symmetry (IS)-broken WSM. We study the band structure and the properties of FAs like shape, spin polarization considering slab and nanowire (NW) geometry, and then compute the two-terminal conductance in WSM NW in terms of the scattering coefficients within the Landauer formalism. We find the FA-mediated conductance to be quantized in units of $2e^2/h$. We extend our study to the transport in WSM/Weyl superconductor (WSC) NW hybrid junction using the Blonder-Tinkham-Klapwijk (BTK) formalism. We show that due to the intricate spin textures, the signatures of the FAs can be captured via Andreev reflection process. We also show that our results of conductance are robust against delta-correlated quenched disorder and thus enhancing the experimental feasibility.
The Chukchi Sea Planning Area, Alaska: Structural Development, Basin Analysis and Hydrocarbon Potential

Seismic stratigraphic and seismic fault evidence both show that the Central Chukchi basin is a north-trending, fault-controlled basin, which developed at the beginning of early Mississippian time. About 20 km thickness of sediments have been deposited in the basin. Using a simple flexural model a series of 3-dimensional basement motion patterns with time was produced. From these characteristic patterns two stages of basement evolution can be recognized: the first is basement subsidence controlled by regional extensional fault systems during the period from early Mississippian to later Jurassic: the second stage is basement evolution as two nearly separate blocks from the beginning of Early Cretaceous. The west block development is related to reactivation of the old fault system, while the subsidence of the east block is caused by tectonism and associated sediment loading due to progressive thrusting northward of the Brooks Range. Quantitative basin modeling techniques show that many uncertainties occur when applying mathematical and computer models to simulate a sedimentary basin, especially in a frontier area where only limited data are available. In this study, quantitative basin modeling techniques applied to the Chukchi Sea Planning Area, Alaska, demonstrate how uncertainty can be handled. The Chukchi Sea Planning Area encompasses about 127,000 square kilometers of the northwestern Alaska continental shelf and lies offshore of the Natural Petroleum Reserve in Alaska. Only seismic data and a few remote onshore wells are available in the Planning Area. Compared with the North Slope of Alaska, the Planning Area has had a significantly different structural history. In addition, most of the strata in the Area are currently thermally mature to overmature and in the stage of generating dry gas. The prospective areas for oil are the margins of the central Chukchi Basin, such as the Chukchi platform, Herald Arch and northeast part of the basin.
SpectralGPT: Spectral Remote Sensing Foundation Model

The foundation model has recently garnered significant attention due to its potential to revolutionize the field of visual representation learning in a self-supervised manner. While most foundation models are tailored to effectively process RGB images for various visual tasks, there is a noticeable gap in research focused on spectral data, which offers valuable information for scene understanding, especially in remote sensing (RS) applications. To fill this gap, we created for the first time a universal RS foundation model, named SpectralGPT, which is purpose-built to handle spectral RS images using a novel 3D generative pretrained transformer (GPT). Compared to existing foundation models, SpectralGPT 1) accommodates input images with varying sizes, resolutions, time series, and regions in a progressive training fashion, enabling full utilization of extensive RS big data; 2) leverages 3D token generation for spatial-spectral coupling; 3) captures spectrally sequential patterns via multi-target reconstruction; 4) trains on one million spectral RS images, yielding models with over 600 million parameters. Our evaluation highlights significant performance improvements with pretrained SpectralGPT models, signifying substantial potential in advancing spectral RS big data applications within the field of geoscience across four downstream tasks: single/multi-label scene classification, semantic segmentation, and change detection.
Blurring boundaries: A fuzzy framework for shaping sustainable tourism experiences

There is currently a growing interest in the study of sustainable tourism experiences, and recent advances in their conceptualization and empirical operationalization represent an important evolutionary leap for the development of innovative tourism products. Although the main dimensions of tourism experiences are known, the enhancement of attractions as a key component in sustainable tourism design has seen little research. This gap stems from two primary challenges: firstly, the complexity of each destination, which hinders the broad application of evaluative methods; and secondly, the subjective nature of tourism experiences that poses significant challenges to measurement and assessment. To overcome these drawbacks, a new fuzzy number-based data standardization technique is proposed. This technique quantifies the tourism value of attractions for sustainable experience design and was validated in Santiago de Cuba's tourism destination. This technique not only facilitates the quantitative assessment of experiential tourism value but also guides the strategic prioritization of attractions, thereby fostering the creation of more sustainable and tailored tourism experiences.
Deep Learning Methods for Device Identiﬁcation Using Symbols Trace Plot

—Devices authentication is one crucial aspect of any communication system. Recently, the physical layer approach radio frequency (RF) ﬁngerprinting has gained increased interest as it provides an extra layer of security without requiring additional components. In this work, we propose an RF ﬁn-gerprinting based transmitter authentication approach density trace plot (DTP) to exploit device-identiﬁable ﬁngerprints. By considering IQ imbalance solely as the feature source, DTP can efﬁciently extract device-identiﬁable ﬁngerprints from symbol transition trajectories and density center drifts. In total, three DTP modalities based on constellation, eye and phase traces are respectively generated and tested against three deep learning classiﬁers: the 2D-CNN, 2D-CNN+biLSTM and 3D-CNN. The feasibility of these DTP and classiﬁer pairs is veriﬁed using a practical dataset collected from the ADALM-PLUTO software-deﬁned radios (SDRs).
Parvalbumin Interneuron Impairment Leads to Synaptic Transmission Deficits and Seizures in SCN8A Epileptic Encephalopathy

SCN8A epileptic encephalopathy (EE) is a severe epilepsy syndrome resulting from de novo mutations in the voltage-gated sodium channel Nav1.6, encoded by the gene SCN8A. Nav1.6 is expressed in both excitatory and inhibitory neurons, yet previous studies have primarily focused on the impact SCN8A mutations have on excitatory neuron function, with limited studies on the importance of inhibitory interneurons to seizure onset and progression. Inhibitory interneurons are critical in balancing network excitability and are known to contribute to the pathophysiology of other epilepsies. Parvalbumin (PV) interneurons are the most prominent inhibitory neuron subtype in the brain, making up about 40% of inhibitory interneurons. Notably, PV interneurons express high levels of Nav1.6. To assess the role of PV interneurons within SCN8A EE, we used two mouse models harboring patient-derived SCN8A gain-of-function mutations, Scn8aD/+, where the SCN8A mutation N1768D is expressed globally, and Scn8aW/+-PV, where the SCN8A mutation R1872W is selectively expressed in PV interneurons. Expression of the R1872W SCN8A mutation selectively in PV interneurons led to the development of spontaneous seizures in Scn8aW/+-PV mice and seizure-induced death, decreasing survival compared to wild-type. Electrophysiology studies showed that PV interneurons in Scn8aD/+ and Scn8aW/+-PV mice were susceptible to depolarization block, a state of action potential failure. Scn8aD/+ and Scn8aW/+-PV interneurons also exhibited increased persistent sodium current, a hallmark of SCN8A gain-of-function mutations that contributes to depolarization block. Evaluation of synaptic connections between PV interneurons and pyramidal cells showed an increase in synaptic transmission failure at high frequencies (80-120Hz) as well as an increase in synaptic latency in Scn8aD/+ and Scn8aW/+-PV interneurons. These data indicate a distinct impairment of synaptic transmission in SCN8A EE, potentially decreasing overall cortical network inhibition. Together, our novel findings indicate that failure of PV interneuron spiking via depolarization block along with frequency-dependent inhibitory synaptic impairment likely elicits an overall reduction in the inhibitory drive in SCN8A EE, leading to unchecked excitation and ultimately resulting in seizures and seizure-induced death.
Functional genomic screening in Komagataella phaffii enabled by high-activity CRISPR-Cas9 library

CRISPR-based high-throughput genome-wide loss-of-function screens are a valuable approach to functional genetics and strain engineering. The yeast Komagataella phaffii is a host of particular interest in the biopharmaceutical industry and as a metabolic engineering host for proteins and metabolites. Here, we design and validate a highly active 6-fold coverage genome-wide sgRNA library for this biotechnologically important yeast containing 30,848 active sgRNAs targeting over 99% of its coding sequences. Conducting fitness screens in the absence of functional non-homologous end joining (NHEJ), the dominant DNA repair mechanism in K. phaffii, provides a quantitative means to assess the activity of each sgRNA in the library. This approach allows for the experimental validation of each guide’s targeting activity, leading to more precise screening outcomes. We used this approach to conduct growth screens with glucose as the sole carbon source and identify essential genes. Comparative analysis of the called gene sets identified a core set of K. phaffii essential genes, many of which relate to protein production, secretion, and glycosylation. The high activity, genome-wide CRISPR library developed here enables functional genomic screening in K. phaffii, applied here to gene essentiality classification, and promises to enable other genetic screens. Highlights Designed and validated a high activity genome-wide CRISPR-Cas9 library for K. phaffii Disabling NHEJ DNA repair enables the generation of genome-wide guide activity profiles Activity-corrected fitness screens identify a high confidence set of essential genes in K. phaffii Protein production, secretion, and glycosylation pathways are essential in K. phaffii but not in other yeasts
Rules of departure: Antiphony and personalized vocal spaces in wild male elephant group coordination

In the intricate realm of animal communication, vocal signals play a vital role in maintaining bonds and coordinating group activities. Through detailed analysis of low-frequency rumble vocalizations of wild male African elephants, we demonstrate how these calls are employed in a coordinated manner. Our findings reveal a distinct pattern of call-and-response, usually initiated by a dominant male, with subsequent turn-taking responses from other members. This antiphonal calling is instrumental in facilitating collective decision-making and group departures, highlighting a sophisticated level of communication previously unrecognized in male elephant groups. Furthermore, the study delves into the frequency content of these rumbles, finding significant individual variations in vocal characteristics. Analysis of the first two formant frequencies of these calls uncovers a distinct vocal space for each elephant, variable over time and across different social contexts. These individualized vocal spaces suggest a mechanism for personal identification within and across elephant groups, and may also relate to the information content of their vocal exchanges. The vocal spaces and structured antiphony presented in this study underline the complexity of elephant vocal communication and invite further targeted research into the vocal communication system of elephants, as well as inquiry into the cognitive and social aspects of wildlife communication in general.