Miracle-dz/UniFAR-FTUs huggingface dataset:
NUMERICAL SIMULATION OF SOLAR MICROFLARES IN A CANOPY-TYPE MAGNETIC CONFIGURATION

Microflares are small activities in the solar low atmosphere; some are in the low corona while others are in the chromosphere. Observations show that some of the microflares are triggered by magnetic reconnection between the emerging flux and a pre-existing background magnetic field. We perform 2.5-dimensional, compressible, resistive magnetohydrodynamic simulations of the magnetic reconnection with gravity considered. The background magnetic field is a canopy-type configuration that is rooted at the boundary of the solar supergranule. By changing the bottom boundary conditions in the simulation, a new magnetic flux emerges at the center of the supergranule and reconnects with the canopy-type magnetic field. We successfully simulate the coronal and chromospheric microflares whose current sheets are located at the corona and the chromosphere, respectively. The microflare with a coronal origin has a larger size and a higher temperature enhancement than the microflare with a chromospheric origin. In the microflares with coronal origins, we also found a hot jet (∼1.8 × 106 K), which is probably related to the observational extreme ultraviolet or soft X-ray jets, and a cold jet (∼104 K), which is similar to the observational Hα/Ca surges. However, there is only a Hα/Ca bright point in the microflares that have chromospheric origins. The study of parameter dependence shows that the size and strength of the emerging magnetic flux are the key parameters that determine the height of the reconnection location, and they further determine the different observational features of the microflares.
FedCross: Towards Accurate Federated Learning via Multi-Model Cross Aggregation

Due to the remarkable performance in preserving data privacy for decentralized data scenarios, Federated Learning (FL) has been considered as a promising distributed machine learning paradigm to deal with data silos problems. Typically, conventional FL approaches adopts a one-to-multi training scheme, where the cloud server keeps only one single global model for all the involved clients for the purpose of model aggregation. However, this scheme suffers from inferior classification performance, since only one global model cannot always accommodate all the incompatible convergence directions of local models, resulting in a low convergence rate and classification accuracy. To address this issue, this paper presents an efficient FL framework named FedCross, which adopts a novel multi-to-multi FL training scheme based on our proposed similarity-based multi-model cross aggregation method. Unlike traditional FL methods, in each round of FL training, FedCross uses a small set of distinct intermediate models to conduct weighted fusion under the guidance of model similarities. In this way, the intermediate models used by FedCross can sufficiently respect the convergence characteristics of clients, thus leading to much fewer conflicts in tuning the convergence directions of clients. Finally, in the deployment stage, FedCross forms a global model for all the clients by performing the federated averaging on the trained immediate models.
Learning Object Affordance with Contact and Grasp Generation

Understanding object affordance can help in designing better and more robust robotic grasping. Existing work in the computer vision community formulates the object affordance understanding as a grasping pose generation problem, which treats the problem as a black box by learning a mapping between objects and the distributions of possible grasping poses for the objects. On the other hand, in the robotics community, estimating object affordance represented by contact maps is of the most importance as localizing the positions of the possible affordance can help the planning of grasping actions. In this paper, we propose to formulate the object affordance understanding as both contacts and grasp poses generation. we factorize the learning task into two sequential stages, rather than the black-box strategy: (1) we ﬁrst reason the contact maps by allowing multi-modal contact generation; (2) assuming that grasping poses are fully constrained given contact maps, we learn a one-to-one mapping from the contact maps to the grasping poses. Further, we propose a penetration-aware partial optimization from the intermediate contacts. It combines local and global optimization for the reﬁnement of the partial poses of the generated grasps exhibiting penetration. Extensive validations on two public datasets show our method outperforms state-of-the-art methods regarding grasp generation on various metrics.
Video in 10 Bits: Few-Bit VideoQA for Efficiency and Privacy

In Video Question Answering (VideoQA), answering general questions about a video requires its visual information. Yet, video often contains redundant information irrelevant to the VideoQA task. For example, if the task is only to answer questions similar to"Is someone laughing in the video? ", then all other information can be discarded. This paper investigates how many bits are really needed from the video in order to do VideoQA by introducing a novel Few-Bit VideoQA problem, where the goal is to accomplish VideoQA with few bits of video information (e.g., 10 bits). We propose a simple yet effective task-specific feature compression approach to solve this problem. Specifically, we insert a lightweight Feature Compression Module (FeatComp) into a VideoQA model which learns to extract task-specific tiny features as little as 10 bits, which are optimal for answering certain types of questions. We demonstrate more than 100,000-fold storage efficiency over MPEG4-encoded videos and 1,000-fold over regular floating point features, with just 2.0-6.6% absolute loss in accuracy, which is a surprising and novel finding. Finally, we analyze what the learned tiny features capture and demonstrate that they have eliminated most of the non-task-specific information, and introduce a Bit Activation Map to visualize what information is being stored. This decreases the privacy risk of data by providing k-anonymity and robustness to feature-inversion techniques, which can influence the machine learning community, allowing us to store data with privacy guarantees while still performing the task effectively.
Role of complementarity in superdense coding

The complementarity of two observables is often captured in uncertainty relations, which quantify an inevitable tradeoff in knowledge. Here we study complementarity in the context of an information processing task: we link the complementarity of two observables to their usefulness for superdense coding (SDC). In SDC, Alice sends two classical dits of information to Bob by sending a single qudit. However, we show that encoding with commuting unitaries prevents Alice from sending more than one dit per qudit, implying that complementarity is necessary for SDC to be advantagous over a classical strategy for information transmission. When Alice encodes with products of Pauli operators for the $X$ and $Z$ bases, we quantify the complementarity of these encodings in terms of the overlap of the $X$ and $Z$ basis elements. Our main result explicitly solves for the SDC capacity as a function of the complementarity, showing that the entropy of the overlap matrix gives the capacity, when the preshared state is maximally entangled. We generalise this equation to resources with symmetric noise such as a preshared Werner state. In the most general case of arbitrary noisy resources, we obtain an analogous lower bound on the SDC capacity. Our results shed light on the role of complementarity in determining the quantum advantage in SDC and also seem fundamentally interesting since they bear a striking resemblance to uncertainty relations.
AI Governance and Ethics Framework for Sustainable AI and Sustainability

AI is transforming the existing technology landscape at a rapid phase enabling data-informed decision making and autonomous decision making. Unlike any other technology, because of the decision-making ability of AI, ethics and governance became a key concern. There are many emerging AI risks for humanity, such as autonomous weapons, automation-spurred job loss, socio-economic inequality, bias caused by data and algorithms, privacy violations and deepfakes. Social diversity, equity and inclusion are considered key success factors of AI to mitigate risks, create values and drive social justice. Sustainability became a broad and complex topic entangled with AI. Many organizations (government, corporate, not-for-profits, charities and NGOs) have diversified strategies driving AI for business optimization and social-and-environmental justice. Partnerships and collaborations become important more than ever for equity and inclusion of diversified and distributed people, data and capabilities. Therefore, in our journey towards an AI-enabled sustainable future, we need to address AI ethics and governance as a priority. These AI ethics and governance should be underpinned by human ethics.
RPoA: Redefined Proof of Activity

The consensus protocol is the core of a blockchain system which guarantees its secure and stable operation. Proof of Activity (PoA) is a consensus protocol that tries to address some of the issues pertinent to the most widely used protocols, such as Proof of Stake (PoS) and Proof of Work (PoW). However, it still needs to solve the issues regarding high energy consumption, significant resources required, high mining latency, and the need for private blockchains. In this paper, we propose Redefined Proof of Activity (RPoA), a new consensus protocol that builds on top of some of the best features of the existing protocols, such as PoW, PoS, and PoA, and values active service provided by users on the network. Our approach tries to address the issues above and falls in the service-based protocols category that gives mining credit to users as they serve on the network.
The quantum orthogonal mystery

This is (hopefully) a Latexable version of a talk given at the XXX Winter School in Theoretical Physics at Karpacz in February 1994. It discusses the use of non-commutative differential calculus to construct a Lie algebra of a quantum group. Usually the result has a different dimension from the classical Lie algebra. This is illustrated by menas of the orthogonal quantum group, and various other possible ways of constructing an orthogonal Lie algebra are described.
D2SLAM: Semantic visual SLAM based on the influence of Depth for Dynamic environments

—Taking into account the dynamics of the scene is the most effective solution to obtain an accurate perception of unknown environments within the framework of a real autonomous robotic application. Many works have attempted to address the non-rigid scene assumption by taking advantage of deep learning advancements. Most new methods combine geometric and semantic approaches to determine dynamic elements that lack generalization and scene awareness. We propose a novel approach that overcomes the limitations of these methods by using scene depth information that reﬁnes the accuracy of estimates from geometric and semantic modules. In addition, the depth information is used to determine an area of inﬂuence of dynamic objects through our Objects Interaction module that estimates the state of both non-matched keypoints and out of segmented region keypoints. The obtained results demonstrate the efﬁcacy of the proposed method in providing accurate localization and mapping in dynamic environments.
Regularized Data Programming with Automated Bayesian Prior Selection

The cost of manual data labeling can be a significant obstacle in supervised learning. Data programming (DP) offers a weakly supervised solution for training dataset creation, wherein the outputs of user-defined programmatic labeling functions (LFs) are reconciled through unsupervised learning. However, DP can fail to outperform an unweighted majority vote in some scenarios, including low-data contexts. This work introduces a Bayesian extension of classical DP that mitigates failures of unsupervised learning by augmenting the DP objective with regularization terms. Regularized learning is achieved through maximum a posteriori estimation with informative priors. Majority vote is proposed as a proxy signal for automated prior parameter selection. Results suggest that regularized DP improves performance relative to maximum likelihood and majority voting, confers greater interpretability, and bolsters performance in low-data regimes.
Approximating Continuous Convolutions for Deep Network Compression

We present ApproxConv, a novel method for compressing the layers of a convolutional neural network. Reframing conventional discrete convolution as continuous convolution of parametrised functions over space, we use functional approximations to capture the essential structures of CNN filters with fewer parameters than conventional operations. Our method is able to reduce the size of trained CNN layers requiring only a small amount of fine-tuning. We show that our method is able to compress existing deep network models by half whilst losing only 1.86% accuracy. Further, we demonstrate that our method is compatible with other compression methods like quantisation allowing for further reductions in model size.
SocialMapf: Optimal and Efficient Multi-Agent Path Finding With Strategic Agents for Social Navigation

We propose an extension to the MAPF formulation, called SocialMapf, to account for private incentives of agents in constrained environments such as doorways, narrow hallways, and corridor intersections. SocialMapf is able to, for instance, accurately reason about the urgent incentive of an agent rushing to the hospital over another agent's less urgent incentive of going to a grocery store; MAPF ignores such agent-specific incentives. Our proposed formulation addresses the open problem of optimal and efficient path planning for agents with private incentives. To solve SocialMapf, we propose a new class of algorithms that use mechanism design during conflict resolution to simultaneously optimize agents' private local utilities and the global system objective. We perform an extensive array of experiments that show that optimal search-based MAPF techniques lead to collisions and increased time-to-goal in SocialMapf compared to our proposed method using mechanism design. Furthermore, we empirically demonstrate that mechanism design results in models that maximizes agent utility and minimizes the overall time-to-goal of the entire system. We further showcase the capabilities of mechanism design-based planning by successfully deploying it in environments with static obstacles. To conclude, we briefly list several research directions using the SocialMapf formulation, such as exploring motion planning in the continuous domain for agents with private incentives.
TRANSPORT PHENOMENA IN A COMPLETELY IONIZED TWO-TEMPERATURE PLASMA

A system of transport equations has been obtained for a plasma consisting of electrons and one kind of positive ions placed in an electric and magnetic field. The system includes the continuity equations, equations of motion, and the equation of heat transport for electrons and ions. The electron and ion temperatures are considered to be different. The case of arbitrary ratio of the particle collision frequency to the Larmor frequency is considered. The derivation of the transport equations from the kinetic equations is similar to that of Chapman and Cowling. (auth)
Constraining the Equation of State of Hybrid Stars Using Recent Information from Multidisciplinary Physics

At the ultrahigh densities existing in the core of neutron stars (NSs), it is expected that a phase transition from baryonic to deconfined quark matter may occur. Such a phase transition would affect the underlying equation of state (EoS) as well as the observable astrophysical properties of NSs. Comparison of EoS model predictions with astronomical data from multimessenger signals then provides us an opportunity to probe the behavior of dense matter. In this work, we restrict the allowed parameter space of EoS models in NSs for both nucleonic (relativistic mean field model) and quark matter (MIT bag model) sectors by imposing state-of-the-art constraints from nuclear calculations, multimessenger astrophysical data, and perturbative quantum chromodynamics (pQCD). We systematically investigate the effect of each constraint on the parameter space of uncertainties using a cutoff filter scheme, as well as the correlations among the parameters and with NS astrophysical observables. Using the constraints, we obtain limits for maximum NS mass, maximum central density, as well as for NS radii and tidal deformability. Although pQCD constraints are only effective at very high densities, they significantly reduce the parameter space of the quark model. We also conclude that astrophysical data supports high values of the bag parameter B and disfavors the existence of a pure quark matter core in hybrid stars.
Indoor Smartphone SLAM with Learned Echoic Location Features

Indoor self-localization is a highly demanded system function for smartphones. The current solutions based on inertial, radio frequency, and geomagnetic sensing may have degraded performance when their limiting factors take effect. In this paper, we present a new indoor simultaneous localization and mapping (SLAM) system that utilizes the smartphone's built-in audio hardware and inertial measurement unit (IMU). Our system uses a smartphone's loud-speaker to emit near-inaudible chirps and then the microphone to record the acoustic echoes from the indoor environment. Our profiling measurements show that the echoes carry location information with sub-meter granularity. To enable SLAM, we apply contrastive learning to construct an echoic location feature (ELF) extractor, such that the loop closures on the smartphone's trajectory can be accurately detected from the associated ELF trace. The detection results effectively regulate the IMU-based trajectory reconstruction. Extensive experiments show that our ELF-based SLAM achieves median localization errors of 0.1 m, 0.53 m, and 0.4m on the reconstructed trajectories in a living room, an office, and a shopping mall, and outperforms the Wi-Fi and geomagnetic SLAM systems.
Duality in optimal consumption--investment problems with alternative data

This study investigates an optimal consumption--investment problem in which the unobserved stock trend is modulated by a hidden Markov chain that represents different economic regimes. In the classical approach, the hidden state is estimated from historical asset prices, but recent advancements in technology enable investors to consider alternative data in their decision-making. These include social media commentary, expert opinions, COVID-19 pandemic data, and GPS data, which originate outside of the standard sources of market data but are considered useful for predicting stock trends. We develop a novel duality theory for this problem and consider a jump-diffusion process for the alternative data series. This theory helps investors in identifying ``useful'' alternative data for dynamic decision-making by offering conditions to the filter equation that permit the use of a control approach based on the dynamic programming principle. We demonstrate an application for proving a unique smooth solution for a constant relative risk-averse agent once the distributions of the signals generated from alternative data satisfy a bounded likelihood ratio condition. In doing so, we obtain an explicit consumption--investment strategy that takes advantage of different types of alternative data that have not been addressed in the literature.
Towards More Efficient Shared Autonomous Mobility: A Learning-Based Fleet Repositioning Approach

Shared-use autonomous mobility services (SAMS) present new opportunities for improving accessible and demand-responsive mobility. A fundamental challenge that SAMS face is appropriate positioning of idle fleet vehicles to meet future demand - a problem that strongly impacts service quality and efficiency. This paper formulates SAMS fleet repositioning as a Markov Decision Process and presents a reinforcement learning-based repositioning (RLR) approach called integrated system-agent repositioning (ISR). The ISR learns a scalable fleet repositioning strategy in an integrated manner: learning to respond to evolving demand patterns without explicit demand forecasting and to cooperate with optimization-based passenger-to-vehicle assignment. Numerical experiments are conducted using New York City taxi data and an agent-based simulation tool. The ISR is compared to an alternative RLR approach named externally guided repositioning (EGR) and a benchmark joint optimization (JO) for passenger-to-vehicle assignment and repositioning. The results demonstrate the RLR approaches' substantial reductions in passenger wait times, over 50%, relative to the JO approach. The ISR's ability to bypass demand forecasting is also demonstrated as it maintains comparable performance to EGR in terms of average metrics. The results also demonstrate the model's transferability to evolving conditions, including unseen demand patterns, extended operational periods, and changes in the assignment strategy.
Towards a theory of surface orbital magnetization

The theory of bulk orbital magnetization has been formulated both in reciprocal space based on Berry curvature and related quantities, and in real space in terms of the spatial average of a quantum mechanical local marker. Here we consider a three-dimensional antiferromagnetic material having a vanishing bulk but a nonzero surface orbital magnetization. We ask whether the surface-normal component of the surface magnetization is well defined, and if so, how to compute it. As the physical observable corresponding to this quantity, we identify the macroscopic current running along a hinge shared by two facets. However, the hinge current only constrains the difference of the surface magnetizations on the adjoined facets, leaving a potential ambiguity. By performing a symmetry analysis, we find that only crystals exhibiting a pseudoscalar symmetry admit well-defined magnetizations at their surfaces at the classical level. We then explore the possibility of computing surface magnetization via a coarse-graining procedure applied to a quantum local marker. We show that multiple expressions for the local marker exist, and apply constraints to filter out potentially meaningful candidates. Using several tight-binding models as our theoretical test bed and several potential markers, we compute surface magnetizations for slab geometries and compare their predictions with explicit calculations of the macroscopic hinge currents of rod geometries. We find that only a particular form of the marker consistently predicts the correct hinge currents.