Miracle-dz/UniFAR-FTUs huggingface dataset:
Forecaster: Towards Temporally Abstract Tree-Search Planning from Pixels

The ability to plan at many different levels of abstraction enables agents to envision the long-term repercussions of their decisions and thus enables sample-efficient learning. This becomes particularly beneficial in complex environments from high-dimensional state space such as pixels, where the goal is distant and the reward sparse. We introduce Forecaster, a deep hierarchical reinforcement learning approach which plans over high-level goals leveraging a temporally abstract world model. Forecaster learns an abstract model of its environment by modelling the transitions dynamics at an abstract level and training a world model on such transition. It then uses this world model to choose optimal high-level goals through a tree-search planning procedure. It additionally trains a low-level policy that learns to reach those goals. Our method not only captures building world models with longer horizons, but also, planning with such models in downstream tasks. We empirically demonstrate Forecaster's potential in both single-task learning and generalization to new tasks in the AntMaze domain.
Corrections to diffusion in interacting quantum systems

Transport and the approach to equilibrium in interacting classical and quantum systems is a challenging problem of both theoretical and experimental interest. One useful organizing principle characterizing equilibration is the dissipative universality class, the most prevalent one being diffusion. In this paper, we use the effective field theory (EFT) of diffusion to systematically obtain universal power-law corrections to diffusion. We then employ large-scale simulations of classical and quantum systems to explore their validity. In particular, we find universal scaling functions for the corrections to the dynamical structure factor $\langle n(x,t)n\rangle$, in the presence of a single $U(1)$ or $SU(2)$ charge in systems with and without particle-hole symmetry, and present the framework to generalize the calculation to multiple charges. Classical simulations show remarkable agreement with EFT predictions for subleading corrections, pushing precision tests of effective theories for thermalizing systems to an unprecedented level. Moving to quantum systems, we perform large-scale tensor-network simulations in unitary and noisy 1d Floquet systems with conserved magnetization. We find a qualitative agreement with EFT which becomes quantitative in the case of noisy systems. Additionally, we show how the knowledge of EFT corrections allows for fitting methods, which can improve the estimation of transport parameters at the intermediate times accessible by simulations and experiments. Finally, we explore non-linear response in quantum systems and find that EFT provides an accurate prediction for its behavior. Our results provide a basis for a better understanding of the non-linear phenomena present in thermalizing systems.
Passive Inference Attacks on Split Learning via Adversarial Regularization

Split Learning (SL) has emerged as a practical and efficient alternative to traditional federated learning. While previous attempts to attack SL have often relied on overly strong assumptions or targeted easily exploitable models, we seek to develop more practical attacks. We introduce SDAR, a novel attack framework against SL with an honest-but-curious server. SDAR leverages auxiliary data and adversarial regularization to learn a decodable simulator of the client's private model, which can effectively infer the client's private features under the vanilla SL, and both features and labels under the U-shaped SL. We perform extensive experiments in both configurations to validate the effectiveness of our proposed attacks. Notably, in challenging but practical scenarios where existing passive attacks struggle to reconstruct the client's private data effectively, SDAR consistently achieves attack performance comparable to active attacks. On CIFAR-10, at the deep split level of 7, SDAR achieves private feature reconstruction with less than 0.025 mean squared error in both the vanilla and the U-shaped SL, and attains a label inference accuracy of over 98% in the U-shaped setting, while existing attacks fail to produce non-trivial results.
Study of the scalar and pseudoscalar meson mass spectrum of QCD at finite temperature, above $T_c$

In this work, we analyze (using a chiral effective Lagrangian model) the scalar and pseudoscalar meson mass spectrum of QCD at finite temperature, above the chiral transition at $T_c$, in the realistic case with $N_f = 2 + 1$ light quark flavors (that is, with $m_{u,d} \to 0$ and $m_s \sim 100$ MeV), looking, in particular, for signatures of the breaking of the $U(1)$ axial symmetry above $T_c$. A critical comparison with the corresponding results obtained (in a previous study) in the two limit cases $N_f = 2$ and $N_f = 3$ is performed, together with a discussion on the limits of validity of a description in terms of a chiral effective Lagrangian model.
Can LSH (locality-sensitive hashing) be replaced by neural network?

With the rapid development of GPU (graphics processing unit) technologies and neural networks, we can explore more appropriate data structures and algorithms. Recent progress shows that neural networks can partly replace traditional data structures. In this paper, we proposed a novel DNN (deep neural network)-based learned locality-sensitive hashing, called LLSH, to efficiently and flexibly map high-dimensional data to low-dimensional space. LLSH replaces the traditional LSH (locality-sensitive hashing) function families with parallel multi-layer neural networks, which reduces the time and memory consumption and guarantees query accuracy simultaneously. The proposed LLSH demonstrate the feasibility of replacing the hash index with learning-based neural networks and open a new door for developers to design and configure data organization more accurately to improve information-searching performance. Extensive experiments on different types of data sets show the superiority of the proposed method in query accuracy, time consumption, and memory usage.
Towards Semantic Communication Protocols for 6G: From Protocol Learning to Language-Oriented Approaches

The forthcoming 6G systems are expected to address a wide range of non-stationary tasks. This poses challenges to traditional medium access control (MAC) protocols that are static and predefined. In response, data-driven MAC protocols have recently emerged, offering ability to tailor their signaling messages for specific tasks. This article presents a novel categorization of these data-driven MAC protocols into three levels: Level 1 MAC. task-oriented neural protocols constructed using multi-agent deep reinforcement learning (MADRL); Level 2 MAC. neural network-oriented symbolic protocols developed by converting Level 1 MAC outputs into explicit symbols; and Level 3 MAC. language-oriented semantic protocols harnessing large language models (LLMs) and generative models. With this categorization, we aim to explore the opportunities and challenges of each level by delving into their foundational techniques. Drawing from information theory and associated principles as well as selected case studies, this study provides insights into the trajectory of data-driven MAC protocols and sheds light on future research directions.
Generation and detection of spin current in iridate/manganite heterostructures

The present study focuses on experimental investigations of spin current across the interface of an iridate/manganite heterostructure (SrIrO3/La0.7Sr0.3MnO3) consisting of oxide epitaxial films with nanometer thickness. Pure spin current is induced by microwave irradiation in the GHz frequency band, specifically under conditions of ferromagnetic resonance. The detection of spin current is achieved through the inverse spin-Hall effect, which measures the charge current arising on the electrically conductive SrIrO3 film with strong spin-orbit interaction. To quantify the efficiency of spin current conversion to charge current, the angular dependences of spin magnetoresistance of the iridate/manganite interface are measured, thereby determining the spin- Hall angle.
A model‐based approach for reconstructing a terrain surface from airborne LIDAR data

A lidar filtering technique is used to differentiate on‐terrain points and off‐terrain points from a cloud of 3D point data collected by a lidar system. A major issue of concern in this low‐level filter is to design a methodology to ensure a continual adaptation to variations of terrain slopes and object scales. In this paper, a new lidar filtering technique which hierarchically fragments lidar data into piecewise planar terrain models is introduced. Once a number of hypothetical planar terrain models are generated to fit the terrain surface of the underlying area, the optimal terrain model to produce the minimum labelling errors is determined based on minimum description length (MDL) principles. This hypothesis‐verification optimisation is achieved in a coarse‐to‐fine strategy by which the entire terrain surface is incrementally reconstructed by increasing the number of planar terrain models fitted. The proposed technique was successfully applied to a digital surface model provided within an OEEPE lidar trial, showing 0·94% of Type I errors and 6·75% of Type II errors compared to manually classified reference data.
Submarine navigation based on gravity gradient-terrain matching

Based on the geophysics technology, a gravity gradient-terrain matching submarine navigation approach is proposed in this paper. The submarine's current position obtained by matching the measured gravity gradient data to the prepared gravity gradient reference map is used to correct the inertial navigation system's accumulated error. Although the precision gradiometer is in use, there is no world-wide gravity gradient map. The ocean's gravity gradient data is even scarce. Therefore, a gravity gradient matching navigation system directly utilizing the gravity gradient reference map can't be realized. With the sensitivity of gravity gradient to terrain, the gravity gradient reference map can be computed from the local terrain elevation data and the preparing approach of the gravity gradient map is proposed in detail in the paper. Since the seabed terrain elevation map, especially highly accurate offing terrain elevation map has been pre-surveyed, the location can be actualized through matching the real-time measured gravity gradient data to the prepared gravity gradient reference map. Simulations show that the submarine navigation approach on gravity gradient-terrain matching is feasible and can be put into practice.
The challenge of active optical sensing from extreme orbits

A review of the history and current state of atmospheric sensing lidar from Earth orbit was conducted and it was found that space based earth remote sensing is still in its infancy with only one limited success extended duration autonomous mission to date. An analysis of the basic requirements for some candidate geo-stationary lidar concepts was completed and it was concluded that significant basic work is required in all areas of lidar development.
The role of precipitation losses in producing the rapid early recovery phase of the Great Magnetic Storm of February 1986

The possible role of precipitation losses in eroding stormtime ring current is subject to debate. To explore this controversy, the recovery phase of the February 6–10, 1986, great magnetic storm is examined, when intense ion precipitation was observed at midlatitudes by NOAA-6 and DMSP satellites. This storm period is particularly interesting because the ring current exhibits distinctive two-phase decay as seen in the Dst index, the early rapid timescale decay corresponding to the intense ion precipitation period described above. Hamilton et al. [1988] concluded, from close agreement between the observed timescale for ring current decay and the theoretical timescale for O+ charge exchange loss, that rapid early recovery phase of this storm resulted from the charge exchange loss of high energy O+; the second and longer decay phase was equated with H+ charge exchange loss. A model of the ring current evolution during this great magnetic storm [Fok et al., 1995] failed to reproduce the observed ring current decay rates, a puzzling result because charge exchange losses were well represented in the ring current model and initial and boundary conditions were taken from the same data set used in the Hamilton et al. [1988] study. A simple energy balance calculation for the global ring current is carried out using (1) either an energy input predicted from upstream solar wind parameters or one calculated from the drift-loss model output, (2) collisional loss timescales extracted from the drift-loss model, and (3) precipitation losses estimated from NOAA-6 and DMSP observations. The energy balance model replicates the evolution of the ring current energy content derived from Active Magnetospheric Particle Tracer Explorers/Charge Composition Explorer (AMPTE/CCE) observations when ion precipitation losses are included and model energy input function is reduced to agree with predictions based upon upstream solar wind parameters. The O+ charge exchange losses and observed global precipitation losses were of equal magnitude in early recovery of the ring current during this great magnetic storm. Later longer decay timescales in the model resulted from a combination of O+ and H+ charge exchange losses; O+ charge exchange losses remained important throughout the model time interval. The present model produces agreement with the AMPTE/CCE estimates of ring current kinetic energy content versus time. Disagreement between the Dst* inferred from the AMPTE/CCE particle measurements and the observed Dst* is an interesting issue needing further explanation.
A Delphi Study

Concerning the needs for mental health intervention within an increasingly diverse younger population, there is a need for play therapists to ensure cultural inclusivity in their clinical practice. Apart from application of treatment modalities and skills with specific populations, play therapy that fosters multicultural inclusivity also involves the process of constructing the therapeutic environment – the playroom. We are particularly concerned with structuring a playroom that is inviting to children of varying identities and backgrounds. For this chapter, we sought to explore the potential of enhancing the multicultural inclusivity of the playroom for play therapy through research and discussion of outcomes related to a research project designed specifically for the purpose of building knowledge regarding play therapy rooms. In this chapter, we will present the methods and results of a recent research study conducted with play therapy experts on increasing the inclusivity of the playroom through attention to the attitudes, structure, and materials that comprise a playroom. In the release of the 2020 census results, the U.S. Bureau of the Census introduced a new concept labeled as the Diversity Index (DI), which indicates the probability that two people chosen at random will be from different race or ethnicity groups. It was shown that in 25 out of 51 states, the DI is above 50%; and among these states, eight have a high DI ranging from 65.8% to 76% (U.S. Census Bureau, 2021), indicating the nation is diversifying rapidly. Taking a closer look at the population trend in the United States over the last two decades, the percentage of the population under age 16 who identified as a racial or ethnic minority grew from 39.2% to 50%. This group is comprised of U.S. residents identifying as Latino or Hispanic, Black, Asian American, American Indian Alaska Native, and “2+ and other races” (Frey, 2020). Race/ethnicity is only one aspect of cultural diversity. In play therapy, we are attuned to cultural aspects of gender, sexual identity, socioeconomic status, religion, and ability status, among many other diverse identities. Growing diversity in the U.S. child population also means that cultural sensitivity and inclusivity in play therapy is needed now more than ever. Child-centered play therapy (CCPT; Landreth, 2012) is a developmentally responsive and evidence-based child therapy intervention for
Measuring Cohesion and Coupling of Object-Oriented Systems Derivation and Mutual Study of Cohesion and Coupling

Cohesion and coupling are considered amongst the most important properties to evaluate the quality of a design. In the context of OO software development, cohesion means relatedness of the public functionality of a class whereas coupling stands for the degree of dependence of a class on other classes in OO system. In this thesis, a new metric has been proposed that measures the class cohesion on the basis of relative relatedness of the public methods to the overall public functionality of a class. The proposed metric for class cohesion uses a new concept of subset tree to determine relative relatedness of the public methods to the overall public functionality of a class. A set of metrics has been proposed for measuring class coupling based on three types of UML relationships, namely association, inheritance and dependency. The reasonable metrics to measure cohesion and coupling are supposed to share the same set of input data. Sharing of input data by the metrics encourages the idea for the existence of mutual relationships between them. Based on potential relationships research questions have been formed. An attempt is made to find answers of these questions with the help of an experiment on OO system FileZilla. Mutual relationships between class cohesion and class coupling have been analyzed statistically while considering OO metrics for size and reuse. Relationships among the pairs of metrics have been discussed and results are drawn in accordance with observed correlation coefficients. A study on Software evolution with the help of class cohesion and class coupling metrics has also been performed and observed trends have been analyzed.
Experiences from Simulating TSP Clusters in the Simics Full System Simulator

TSP (or Telecommunication Server Platform) is a scalable, high availability cluster operating system developed by Ericsson for use in the telecommunications industry. This thesis describes an attempt to simulate a TSP cluster in the full system simulator Simics, and talks about some of the possibilities offered by such a setup and full system simulation in general. This attempt to simulate TSP was unsuccessful in completely booting the cluster in Simics, but some of the experiences and problems encountered are described. A proposed development environment for working with TSP in Simics is also presented, along with scripts that were created during this thesis to alleviate the working process.
Evaluación o Inculpación

The improvement of education on a large scale seems an unattainable goal and a difficult promise to fulfill. With this objective, education policies today dominant on the world scene, promote a device in which the evaluation and self-evaluation are the key piece. Inspired by the prospect of effective schools and quality management models, its virtuality does not consist of both actually improve education significantly, but it exempts the State from this responsibility and endorses it centers and teachers. Thus, in view of the poor results the evaluation mechanism has achieved in years, instead of evaluation could be talking of indictment.
Depth–age and temperature prediction at Dome Fuji station, East Antarctica

Abstract The geophysical metronome (Milankovitch components of the past surface temperature variations) and the isotope–temperature transfer function deduced from the borehole temperature profile at Vostok station, Antarctica, are applied to date the 2500 mdeep ice core from Dome Fuji station, Antarctica, and to reconstruct paleoclimatic conditions at the drilling site on the basis of the local δ18O isotope record. Special attention is paid to consistency of this depth–age relation with the mass-balance reconstruction and predictions of ice-flow modeling. the present-day ice mass-balance rate at Dome Fuji is estimated as 3.2 cm a–1. the ice age at the borehole bottom (590m above the bedrock) is around 335 ± 4.5 kyr and may reach 2000 kyr at about 3000 mdepth.The difference in the ice-sheet surface temperatures between Holocene optimum and Last Glacial Maximum is found to be 17.8˚C at the temporal isotope/temperature slope, about 30% lower than the modern geographical estimates. A good agreement between modeled and measured (preliminary data) borehole temperatures is obtained at the geothermal flux 0.059 Wm–2 and ice-fusion temperature (–2˚C) at the ice–rock interface with minimum (zero) melt rates.
A Multifactor, Nonlinear, Continuous-time Model of Interest Rate Volatility a Multifactor, Nonlinear, Continuous-time Model of Interest Rate Volatility

This paper presents a general, nonlinear version of existing multifactor models, such as Longstaff and Schwartz (1992). The novel aspect of our approach is that rather than choosing the model parameterization out of " thin air " , our processes are generated from the data using approximation methods for multifactor continuous-time Markov processes. In applying this technique to the short-and long-end of the term structure for a general two-factor diffusion process for interest rates, a major finding is that the volatility of interest rates is increasing in the level of interest rates mostly for sharply upward sloping term structures. In fact, the slope of the term structure plays a larger role in determining the magnitude of the diffusion coefficient. As an application, we show how this method can be used to value fixed-income contingent claims.
Analysis of regional drought severity over central dry zone of Karnataka by using SPI

Drought is one of the most complex and least understood natural disasters that badly affect growing or living conditions Rainfall is to be believed as a normal, wet and dry condition of the climate. It is one of the most important water associated hazards. It has great impact on agricultural, hydrological, economic, environmental and social systems. Understanding these impacts is crucial for drought planning, mitigation, and response. It also helps decision makers identify and reduce vulnerability to drought. Drought monitoring is a key component of drought preparedness and the standardized precipitation index (SPI) has several characteristics that are an upgrading over other indices, with its simplicity and flexibility. The objective of this study was to analyze drought severity using SPI method for observing and describing drought based on thirty (30) year precipitation data of central dry zone of karnataka (1985 - 2014). Positive SPI values point to normal condition to wet condition and negative values indicate normal condition to dry condition. SPI method resulted in showing extreme drought events in the year 1985, 1989, 1990 and 1995, whereas the major extreme drought event occurred in 1985 was really caused by the low rainfall. The results show that the annual drought classification system proved to be efficient in the identification of events. The results of this study will be useful in understanding the historic patterns and build future scenarios of drought for risk management and climate change adaptation planning. Further research should include relating dryness patterns with pastoral systems, cropping pattern and crop production.