Miracle-dz/UniFAR-FTUs huggingface dataset:
Network-aware EV charging and discharging in unbalanced distribution grids: A distributed, robust approach against communication failures

This paper proposes a distributed optimization-based algorithm for electric vehicle (EV) charging and discharging, incorporating EV customer economics and distribution network constraints enforced on an unbalanced distribution grid. Building on a consensus-based alternating direction method of multipliers (ADMM), the algorithm is designed such that EVs coordinate by means of exchanging limited information with their neighboring EVs in a connected communication network. Specifically, an iterative routine is executed, whereby EVs cooperatively determine their charge-discharge profiles that maintain the distribution grid voltages and transformer core temperatures within safe operating limits. Importantly, the algorithm is robust against communication failures potentially arising in real-world implementations. Numerical simulations are conducted to verify the efficacy of the proposed EV charging algorithm in terms of network-aware operation and communication-failure tolerant operation.
Domain Generalization Using Large Pretrained Models with Mixture-of-Adapters

Learning a robust vision model despite large distribution shift is essential for model deployment in real-world settings. Especially, domain generalization (DG) algorithm aims to maintain the performance of a trained model on different distributions which were not seen during training. One of the most effective methods has been leveraging the already learned rich knowledge of large pretrained models. However, naively fine-tuning large models to DG tasks is often practically infeasible due to memory limitations, extensive time requirements for training, and the risk of learned knowledge deterioration. Recently, parameter-efficient fine-tuning (PEFT) methods have been proposed to reduce the high computational cost during training and efficiently adapt large models to downstream tasks. In this work, for the first time, we find that the use of adapters in PEFT methods not only reduce high computational cost during training but also serve as an effective regularizer for DG tasks. Surprisingly, a naive adapter implementation for large models achieve superior performance on common datasets. However, in situations of large distribution shifts, additional factors such as optimal amount of regularization due to the strength of distribution shifts should be considered for a sophisticated adapter implementation. To address this, we propose a mixture-of-expert based adapter fine-tuning method, dubbed as mixture-of-adapters (MoA). Specifically, we employ multiple adapters that have varying capacities, and by using learnable routers, we allocate each token to a proper adapter. By using both PEFT and MoA methods, we effectively alleviate the performance deterioration caused by distribution shifts and achieve state-of-the-art performance on diverse DG benchmarks.
Diagonalizing Bose Gases in the Gross-Pitaevskii Regime and Beyond

We present a novel approach to the Bogoliubov theory of dilute Bose gases, allowing for an elementary derivation of the celebrated Lee-Huang-Yang formula in the Gross-Pitaevskii regime. Furthermore, we identify the low lying excitation spectrum beyond the Gross-Pitaevskii scaling, extending a recent result [3] to significantly more singular scaling regimes. Finally, we provide an upper bound on the ground state energy in the Gross-Pitaevskii regime that captures the correct expected order of magnitude beyond the Lee-Huang-Yang formula.
On two possible ways to recover ordinary thermodynamics from extended thermodynamics of polyatomic gases

We consider two possible ways, i.e. the Maxwellian iteration (MI) and the Chapman–Enskog method (CEM), to recover relativistic ordinary thermodynamics from relativistic extended thermodynamics of Polyatomic gases with N moments. Both of these methods give the Eckart equations which are the relativistic version of the Navier–Stokes and Fourier laws as a first iteration. However, these methods do not lead to the same expressions of the heat conductivity χ, the shear viscosity µ, and the bulk viscosity ν which appear as coefficients in the Eckart equations. In particular, we prove that the expressions of χ, µ, and ν obtained via the CEM do not depend on N, while those obtained through the MI depend on N. Moreover, we also prove that these two methods lead to the same results in the nonrelativistic limit.
Regularization properties of adversarially-trained linear regression

State-of-the-art machine learning models can be vulnerable to very small input perturbations that are adversarially constructed. Adversarial training is an effective approach to defend against it. Formulated as a min-max problem, it searches for the best solution when the training data were corrupted by the worst-case attacks. Linear models are among the simple models where vulnerabilities can be observed and are the focus of our study. In this case, adversarial training leads to a convex optimization problem which can be formulated as the minimization of a finite sum. We provide a comparative analysis between the solution of adversarial training in linear regression and other regularization methods. Our main findings are that: (A) Adversarial training yields the minimum-norm interpolating solution in the overparameterized regime (more parameters than data), as long as the maximum disturbance radius is smaller than a threshold. And, conversely, the minimum-norm interpolator is the solution to adversarial training with a given radius. (B) Adversarial training can be equivalent to parameter shrinking methods (ridge regression and Lasso). This happens in the underparametrized region, for an appropriate choice of adversarial radius and zero-mean symmetrically distributed covariates. (C) For $\ell_\infty$-adversarial training -- as in square-root Lasso -- the choice of adversarial radius for optimal bounds does not depend on the additive noise variance. We confirm our theoretical findings with numerical examples.
Faster Algorithms for Generalized Mean Densest Subgraph Problem

The densest subgraph of a large graph usually refers to some subgraph with the highest average degree, which has been extended to the family of $p$-means dense subgraph objectives by~\citet{veldt2021generalized}. The $p$-mean densest subgraph problem seeks a subgraph with the highest average $p$-th-power degree, whereas the standard densest subgraph problem seeks a subgraph with a simple highest average degree. It was shown that the standard peeling algorithm can perform arbitrarily poorly on generalized objective when $p>1$ but uncertain when $0<p<1$. In this paper, we are the first to show that a standard peeling algorithm can still yield $2^{1/p}$-approximation for the case $0<p<1$. (Veldt 2021) proposed a new generalized peeling algorithm (GENPEEL), which for $p \geq 1$ has an approximation guarantee ratio $(p+1)^{1/p}$, and time complexity $O(mn)$, where $m$ and $n$ denote the number of edges and nodes in graph respectively. In terms of algorithmic contributions, we propose a new and faster generalized peeling algorithm (called GENPEEL++ in this paper), which for $p \in [1, +\infty)$ has an approximation guarantee ratio $(2(p+1))^{1/p}$, and time complexity $O(m(\log n))$, where $m$ and $n$ denote the number of edges and nodes in graph, respectively. This approximation ratio converges to 1 as $p \rightarrow \infty$.
The 3-dimensional Fermi liquid description for the normal state of cuprate superconductors

The quasiparticles in the normal state of cuprate superconductors have been shown to behave universally as a 3-dimensional Fermi liquid. Because of interactions and the presence of the Fermi surfaces (or Fermi energies), the quasiparticle energy contains, as a function of the momentum $\boldmath{p}$, a term of the form $(p-p_0)^3 \ln ( | p-p_0 | / p_0 )$, where $p = | \boldmath{p} |$ and $p_0$ is the Fermi momentum. The electronic specific heat coefficient $\gamma(T)$, electrical resistivity, Hall coefficient and thermoelectric power divided by temperature $T$, follow the logarithmic formula $a - b T^2 \ln ( T/T^*) $, $a$, $b$, and $T^*$ being constant. Singularities in the Landau $f$-function produce the $T^2 \ln T$ dependence of the magnetic susceptibility $\chi (T)$, and Knight shift, which gives rise to the phenomenon of the susceptibility maximum. The logarithmic $T$-dependence of the transport properties arises exclusively from the impurity scattering in 3-dimensional (3D) systems, but does not from the electron-electron scattering in 2D systems. The above logarithmic formula has been shown to explain universally the experimental data for the normal state of all cuprate superconductors. The decrease of $\gamma(T)$ or $\chi(T)$ with decreasing $T$ is not due to the appearance of pseudogap or spin gap but due to its $T^2 \ln T$ variation.
Agent-Specific Effects

Establishing causal relationships between actions and outcomes is fundamental for accountable multi-agent decision-making. However, interpreting and quantifying agents' contributions to such relationships pose significant challenges. These challenges are particularly prominent in the context of multi-agent sequential decision-making, where the causal effect of an agent's action on the outcome depends on how other agents respond to that action. In this paper, our objective is to present a systematic approach for attributing the causal effects of agents' actions to the influence they exert on other agents. Focusing on multi-agent Markov decision processes, we introduce agent-specific effects (ASE), a novel causal quantity that measures the effect of an agent's action on the outcome that propagates through other agents. We then turn to the counterfactual counterpart of ASE (cf-ASE), provide a sufficient set of conditions for identifying cf-ASE, and propose a practical sampling-based algorithm for estimating it. Finally, we experimentally evaluate the utility of cf-ASE through a simulation-based testbed, which includes a sepsis management environment.
Video Super-Resolution Using a Grouped Residual in Residual Network

Super-resolution (SR) is the technique of increasing the nominal resolution of image / video content accompanied with quality improvement. Video super-resolution (VSR) can be considered as the generalization of single image super-resolution (SISR). This generalization should be such that more detail is created in the output using adjacent input frames. In this paper, we propose a grouped residual in residual network (GRRN) for VSR. By adjusting the hyperparameters of the proposed structure, we train three networks with different numbers of parameters and compare their quantitative and qualitative results with the existing methods. Although based on some quantitative criteria, GRRN does not provide better results than the existing methods, in terms of the quality of the output image it has acceptable performance.
ALMA Gas-dynamical Mass Measurement of the Supermassive Black Hole in the Red Nugget Relic Galaxy PGC 11179

We present 0. ″22-resolution Atacama Large Millimeter/submillimeter Array (ALMA) observations of CO(2−1) emission from the circumnuclear gas disk in the red nugget relic galaxy PGC 11179. The disk shows regular rotation, with projected velocities near the center of 400 km s−1. We assume the CO emission originates from a dynamically cold, thin disk and fit gas-dynamical models directly to the ALMA data. In addition, we explore systematic uncertainties by testing the impacts of various model assumptions on our results. The supermassive black hole (BH) mass (M BH) is measured to be M BH = (1.91 ± 0.04 [1σ statistical] −0.51+0.11 [systematic]) × 109 M ⊙, and the H-band stellar mass-to-light ratio M/L H = 1.620 ± 0.004 [1σ statistical] −0.107+0.211 [systematic] M ⊙/L ⊙. This M BH is consistent with the BH mass−stellar velocity dispersion relation but over-massive compared to the BH mass−bulge luminosity relation by a factor of 3.7. PGC 11179 is part of a sample of local compact early-type galaxies that are plausible relics of z ∼ 2 red nuggets, and its behavior relative to the scaling relations echoes that of three relic galaxy BHs previously measured with stellar dynamics. These over-massive BHs could suggest that BHs gain most of their mass before their host galaxies do. However, our results could also be explained by greater intrinsic scatter at the high-mass end of the scaling relations, or by systematic differences in gas- and stellar-dynamical methods. Additional M BH measurements in the sample, including independent cross-checks between molecular gas- and stellar-dynamical methods, will advance our understanding of the co-evolution of BHs and their host galaxies.
PELA: Learning Parameter-Efficient Models with Low-Rank Approximation

Applying a pre-trained large model to downstream tasks is prohibitive under resource-constrained conditions. Recent dominant approaches for addressing efficiency issues involve adding a few learnable parameters to the fixed backbone model. This strategy, however, leads to more challenges in loading large models for downstream fine-tuning with limited resources. In this paper, we propose a novel method for increasing the parameter efficiency of pre-trained models by introducing an intermediate pre-training stage. To this end, we first employ low-rank approximation to compress the original large model and then devise a feature distillation module and a weight perturbation regularization module. These modules are specifically designed to enhance the low-rank model. In particular, we update only the low-rank model while freezing the backbone parameters during pre-training. This allows for direct and efficient utilization of the low-rank model for downstream fine-tuning tasks. The proposed method achieves both efficiencies in terms of required parameters and computation time while maintaining comparable results with minimal modifications to the backbone architecture. Specifically, when applied to three vision-only and one vision-language Transformer models, our approach often demonstrates a merely $\sim$0.6 point decrease in performance while reducing the original parameter size by 1/3 to 2/3.
Suspended sediment yields of rivers in Turkey

The aim of this paper is to quantify the spatial distribution of suspended sediment yields of rivers in Turkey, and to quantify the suspended sediment flux to the sea, to lakes and reservoirs, and out of the country. Sediment yields in Turkey vary in accordance with the erosion potential of the river basins and values range from 12 to 609 t km"2 year1. The total mass of suspended sediment flowing to the sea, lakes, reservoirs, and out of the country is 175 000 000 t year1. The sediment yields of selected basins are compared with average values for the world. INTRODUCTION Man has faced problems caused by erosion, transportation and deposition of sediment ever since the civilisations that developed in the fertile valleys of the Nile, Tigris, Euphrates, Indus and the Yellow River. As early as 4000 BC people dug canals, built storage dams, diverted water and constructed flood protection works. With the develop­ ment of rivers for multi-purpose use, such as irrigation, power generation, flood control, navigation, water supply for industry and drinking, and recreation, man has interfered with the equilibrium of alluvial streams and created several problems. Erosion of soil from river basins involves the processes of detachment of soil from the soil surface and its transport by runoff and wind. Some sediment is also eroded from the bed and banks of small and large channels. Part of the sediment eroded by the action of rainfall and surface flow is deposited in the lower parts of the basin, in streams and on flood plains, and the remainder is transported to the sea unless it is deposited in reservoirs. The study of erosion and sediment yield from river basins is important for many reasons. Deposition of sediment in a reservoir reduces its capacity, «"hereby adversely affecting water supply for irrigation, domestic and industrial use and for power generation. Depletion of reservoir capacity is a serious problem, since after some years reservoirs may become ineffective in holding large quantities of water. When sediment eroded from river basins is deposited on stream beds and banks it can cause braiding of river reaches, inundation of the flood plain during floods and reduction of clearance below bridges. The removal of fertile topsoil from a river basin will also adversely affect the agricultural production.
The physics and metaphysics of the conceptuality interpretation of quantum mechanics

Quantum mechanics has maintained over the years the reputation of being"the most obscure theory. "It works perfectly well, but nobody seems to know why. It has been argued that the difficulty in understanding quantum theory is our failed attempt to force onto it a wrong conceptual scheme, wanting at all costs to think about the objects of the theory as, precisely, objects, i.e., entities having continuously actual spatiotemporal properties. This too restrictive spatiotemporal scheme is most probably at the heart of the problem, as also underlined by the Einsteinian revolution, but then what could be an alternative? Many thinkers have suggested that we must surrender to the fact that our physical world is one of immanent powers and potencies. Aristotle did so ante quantum litteram, followed by scholars like Heisenberg, Primas, Shimony, Piron, Kastner, Kauffman, de Ronde, just to name a few, including the authors, who were both students of Piron in Geneva. However, if on the one hand a potentiality ontology puts the accent on the processes of change, responsible for the incessant shifts between actual and potential properties, on the other hand it does not tell what these changes are all about. In other words, the metaphysical question remains of identifying the nature of the bearer of these potencies, or potentialities, and of the entities that can actualize them. It is the purpose of the present article to emphasize that the above question has found a possible answer in the recent Conceptuality Interpretation of Quantum Mechanics, which we believe offers the missing ontology and metaphysics that can make the theory fully intelligible, and even intuitive.
On General Capacity-Distortion Formulas of Integrated Sensing and Communication

The integrated sensing and communication (ISAC) problem with general state and channel distributions is investigated. General formulas of the capacity-distortion tradeoff for the ISAC problem under maximal and average distortion constraints are provided. The results cover some existing communication models such as the general point-to-point channel and Gel'fand-Pinsker channel. More details including memoryless states and channels, mixed states and channels, and rate-limited CSI at one side are considered. Numerical results focus on writing-on-dirty paper model and channel with ergodic/non-ergodic fading.
ENSO Amplitude Modulation Associated with the Mean SST Changes in the Tropical Central Pacific Induced by Atlantic Multidecadal Oscillation

AbstractThe mechanism associated with the modulation of the El Nino–Southern Oscillation (ENSO) amplitude caused by the Atlantic multidecadal oscillation (AMO) is investigated by using long-term historical observational data and various types of models. The observational data for the period 1900–2013 show that the ENSO variability weakened during the positive phase of the AMO and strengthened in the negative phase. Such a relationship between the AMO and ENSO amplitude has been reported by a number of previous studies. In the present study the authors demonstrate that the weakening of the ENSO amplitude during the positive phase of the AMO is related to changes of the SST cooling in the eastern and central Pacific accompanied by the easterly wind stress anomalies in the equatorial central Pacific, which were reproduced reasonably well by coupled general circulation model (CGCM) simulations performed with the Atlantic Ocean SST nudged perpetually with the observed SST representing the positive phase of the.
Thermodynamic properties of MgSiO3 majorite and phase transitions near 660 km depth in MgSiO3 and Mg2SiO4: A first principles study

[1] Thermodynamic properties of MgSiO3 tetragonal majorite have been calculated at high pressures and temperatures within the quasi-harmonic approximation based on density functional theory using the local density approximation (LDA) and the generalized gradient approximation (GGA). The LDA results compare exceptionally well with measured thermodynamic properties. A classical Monte Carlo simulation based on results from a cluster expansion method demonstrates that disorder between magnesium and silicon in the octahedral sites in MgSiO3 majorite does not occur below 3600 K at transition zone pressures. The ensuing calculations on phase boundaries of MgSiO3 between majorite, perovskite, and ilmenite show that a much better agreement with experiment can be obtained by using GGA rather than LDA, for LDA underestimates the transition pressures by as much as 11 GPa. The Clapeyron slopes predicted by GGA and LDA are close to each other: 0.9–1.7 MPa/K for majorite-perovskite transition, 6.9–7.9 MPa/K for majorite-ilmenite transition, and −7–−3 MPa/K for ilmenite-perovskite transition. The triple point predicted by GGA is located at 21.8 ± 1 GPa and 1840 ± 200 K which is ∼400 K lower in temperature than most experimental estimates. This result suggests that ilmenite is restricted to lower temperatures and that the majorite to ilmenite transition may occur in cold subducting slabs in the transition zone. Our calculation also reveals that wadsleyite decomposes to an assemblage of majorite plus periclase above 2280 K with a large negative Clapeyron slope (−22–−12 MPa/K) and that ringwoodite decomposes to ilmenite plus periclase below 1400 K (1.2 MPa/K). These two decomposition transitions may influence hot plumes and cold slabs near 660 km depth, respectively. Further calculations show that discontinuities in density, bulk modulus, and bulk sound velocity associated with the majorite to perovskite transition in MgSiO3 are much larger than those from the postspinel transition in Mg2SiO4 at conditions close to 660 km depth. This suggests that the large density discontinuity at 660 km depth as proposed by PREM (9.3%) might be accounted by a piclogite compositional model or marginally accounted by a pyrolite compositional model with, for example, 50 vol % ringwoodite, 45 vol % majorite, and 5 vol % other phases (such as calcium perovskite) at the bottom of the transition zone, provided that the density contrast between majorite and perovskite will not be greatly altered by the presence of other elements such as Fe, Al, Ca, and H. On the other hand, the smaller density discontinuity at 660 km depth as derived from impedance studies (4–6%) disfavors sharp contributions to seismic discontinuities from the majorite to perovskite transition.