Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint and Robust Beamforming Framework for Integrated Sensing and Communication Systems

Integrated sensing and communication (ISAC) is widely recognized as a fundamental enabler for future wireless communications. In this paper, we present a joint communication and radar beamforming framework for maximizing a sum spectral efficiency (SE) while guaranteeing desired radar performance with imperfect channel state information (CSI) in multi-user and multi-target ISAC systems. To this end, we adopt either a radar transmit beam mean square error (MSE) or receive signal-to-clutter-plus-noise ratio (SCNR) as a radar performance constraint of a sum SE maximization problem. To resolve inherent challenges such as non-convexity and imperfect CSI, we reformulate the problems and identify first-order optimality conditions for the joint radar and communication beamformer. Turning the condition to a nonlinear eigenvalue problem with eigenvector dependency (NEPv), we develop an alternating method which finds the joint beamformer through power iteration and a Lagrangian multiplier through binary search. The proposed framework encompasses both the radar metrics and is robust to channel estimation error with low complexity. Simulations validate the proposed methods. In particular, we observe that the MSE and SCNR constraints exhibit complementary performance depending on the operating environment, which manifests the importance of the proposed comprehensive and robust optimization framework.
Fast Pulsars, Neutron Stars, and Astrophysical Strange Quark Matter Objects

This book chapter explores key aspects of neutron stars, pulsar glitches, tidal deformability, fast pulsars, the equation of state, and strange quark matter stars. Challenges in directly measuring neutron star radius have led to reliance on spectroscopic and timing techniques, with uncertainties addressed through careful source selection and theoretical modeling. Pulsar glitches reveal insights into the equation of state through angular momentum transfer within the neutron star. Tidal deformability is crucial in gravitational-wave astronomy, exemplified by the GW170817 event. Fast pulsars, instrumental in astrophysical testing, are classified into ordinary pulsars, millisecond pulsars, and magnetars. The EOS is vital for understanding neutron star internal structure, explored through various models. The chapter delves into the theoretical framework for rotating neutron stars, addressing uniform and differential rotation scenarios and their impacts on mass and radius. Additionally, the intriguing concept of quark stars and strange dwarfs is investigated. The various topics discussed in this book chapter contribute to a broader understanding of dense matter physics, astrophysical phenomena, and the potential for transformative discoveries through advanced observational techniques and technologies like gravitational wave detectors, radio telescopes, and X-ray telescopes.
Mean-Field Analysis for Learning Subspace-Sparse Polynomials with Gaussian Input

In this work, we study the mean-field flow for learning subspace-sparse polynomials using stochastic gradient descent and two-layer neural networks, where the input distribution is standard Gaussian and the output only depends on the projection of the input onto a low-dimensional subspace. We propose a basis-free generalization of the merged-staircase property in Abbe et al. (2022) and establish a necessary condition for the SGD-learnability. In addition, we prove that the condition is almost sufficient, in the sense that a condition slightly stronger than the necessary condition can guarantee the exponential decay of the loss functional to zero.
Teamwork Makes TEE Work: Open and Resilient Remote Attestation on Decentralized Trust

Remote Attestation (RA) enables the integrity and authenticity of applications in Trusted Execution Environment (TEE) to be verified. Existing TEE RA designs employ a centralized trust model where they rely on a single provisioned secret key and a centralized verifier to establish trust for remote parties. This model is however brittle and can be untrusted under advanced attacks nowadays. Besides, most designs only provide fixed functionalities once deployed, making them hard to adapt to different needs on availability, Quality of Service (QoS), etc. Therefore, we propose JANUS, an open and resilient TEE RA scheme. To decentralize trust, we, on one hand, introduce Physically Unclonable Function (PUF) as an intrinsic root of trust (RoT) in TEE to provide additional measurements and cryptographic enhancements. On the other hand, we use blockchain and smart contract to realize decentralized verification and result audit. Furthermore, we design an automated turnout mechanism that allows JANUS to remain resilient and offer flexible RA services under various situations. We provide a UC-based security proof and demonstrate the scalability and generality of JANUS by implementing an open-sourced prototype.
If Turing played piano with an artificial partner

Music is an inherently social activity that allows people to share experiences and feel connected with one another. There has been little progress in designing artificial partners exhibiting a similar social experience as playing with another person. Neural network architectures that implement generative models, such as large language models, are suited for producing musical scores. Playing music socially, however, involves more than playing a score; it must complement the other musicians' ideas and keep time correctly. We addressed the question of whether a convincing social experience is made possible by a generative model trained to produce musical scores, not necessarily optimized for synchronization and continuation. The network, a variational autoencoder trained on a large corpus of digital scores, was adapted for a timed call-and-response task with a human partner. Participants played piano with a human or artificial partner-in various configurations-and rated the performance quality and first-person experience of self-other integration. Overall, the artificial partners held promise but were rated lower than human partners. The artificial partner with simplest design and highest similarity parameter was not rated differently from the human partners on some measures, suggesting that interactive rather than generative sophistication is important in enabling social AI.
Single-Reset Divide & Conquer Imitation Learning

Demonstrations are commonly used to speed up the learning process of Deep Reinforcement Learning algorithms. To cope with the difficulty of accessing multiple demonstrations, some algorithms have been developed to learn from a single demonstration. In particular, the Divide&Conquer Imitation Learning algorithms leverage a sequential bias to learn a control policy for complex robotic tasks using a single state-based demonstration. The latest version, DCIL-II demonstrates remarkable sample efficiency. This novel method operates within an extended Goal-Conditioned Reinforcement Learning framework, ensuring compatibility between intermediate and subsequent goals extracted from the demonstration. However, a fundamental limitation arises from the assumption that the system can be reset to specific states along the demonstrated trajectory, confining the application to simulated systems. In response, we introduce an extension called Single-Reset DCIL (SR-DCIL), designed to overcome this constraint by relying on a single initial state reset rather than sequential resets. To address this more challenging setting, we integrate two mechanisms inspired by the Learning from Demonstrations literature, including a Demo-Buffer and Value Cloning, to guide the agent toward compatible success states. In addition, we introduce Approximate Goal Switching to facilitate training to reach goals distant from the reset state. Our paper makes several contributions, highlighting the importance of the reset assumption in DCIL-II, presenting the mechanisms of SR-DCIL variants and evaluating their performance in challenging robotic tasks compared to DCIL-II. In summary, this work offers insights into the significance of reset assumptions in the framework of DCIL and proposes SR-DCIL, a first step toward a versatile algorithm capable of learning control policies under a weaker reset assumption.
DUEL: Duplicate Elimination on Active Memory for Self-Supervised Class-Imbalanced Learning

Recent machine learning algorithms have been developed using well-curated datasets, which often require substantial cost and resources. On the other hand, the direct use of raw data often leads to overfitting towards frequently occurring class information. To address class imbalances cost-efficiently, we propose an active data filtering process during self-supervised pre-training in our novel framework, Duplicate Elimination (DUEL). This framework integrates an active memory inspired by human working memory and introduces distinctiveness information, which measures the diversity of the data in the memory, to optimize both the feature extractor and the memory. The DUEL policy, which replaces the most duplicated data with new samples, aims to enhance the distinctiveness information in the memory and thereby mitigate class imbalances. We validate the effectiveness of the DUEL framework in class-imbalanced environments, demonstrating its robustness and providing reliable results in downstream tasks. We also analyze the role of the DUEL policy in the training process through various metrics and visualizations.
IMUOptimize: A Data-Driven Approach to Optimal IMU Placement for Human Pose Estimation with Transformer Architecture

This paper presents a novel approach for predicting human poses using IMU data, diverging from previous studies such as DIP-IMU, IMUPoser, and TransPose, which use up to 6 IMUs in conjunction with bidirectional RNNs. We introduce two main innovations: a data-driven strategy for optimal IMU placement and a transformer-based model architecture for time series analysis. Our findings indicate that our approach not only outperforms traditional 6 IMU-based biRNN models but also that the transformer architecture significantly enhances pose reconstruction from data obtained from 24 IMU locations, with equivalent performance to biRNNs when using only 6 IMUs. The enhanced accuracy provided by our optimally chosen locations, when coupled with the parallelizability and performance of transformers, provides significant improvements to the field of IMU-based pose estimation.
Infinite-horizon optimal scheduling for feedback control

Emerging cyber-physical systems impel the development of communication protocols to efficiently utilize resources. This paper investigates the optimal co-design of control and scheduling in networked control systems. The objective is to co-design the control law and the scheduling mechanism that jointly optimize the tradeoff between regulation performance and communication resource consumption in the long run. The concept of the value of information (VoI) is employed to evaluate the importance of data being transmitted. The optimal solution includes a certainty equivalent control law and a stationary scheduling policy based on the VoI function. The closed-loop system under the designed scheduling policy is shown to be stochastically stable. By analyzing the property of the VoI function, we show that the optimal scheduling policy is symmetric and is a monotone function when the system matrix is diagonal. Moreover, by the diagonal system matrix assumption, the optimal scheduling policy is shown to be of threshold type. Then we provide a simplified yet equivalent form of the threshold-based optimal scheduling policy. The threshold value searching region is also given. Finally, the numerical simulation illustrates the theoretical result of the VoI-based scheduling.
Fusing Individualized Treatment Rules Using Secondary Outcomes

An individualized treatment rule (ITR) is a decision rule that recommends treatments for patients based on their individual feature variables. In many practices, the ideal ITR for the primary outcome is also expected to cause minimal harm to other secondary outcomes. Therefore, our objective is to learn an ITR that not only maximizes the value function for the primary outcome, but also approximates the optimal rule for the secondary outcomes as closely as possible. To achieve this goal, we introduce a fusion penalty to encourage the ITRs based on different outcomes to yield similar recommendations. Two algorithms are proposed to estimate the ITR using surrogate loss functions. We prove that the agreement rate between the estimated ITR of the primary outcome and the optimal ITRs of the secondary outcomes converges to the true agreement rate faster than if the secondary outcomes are not taken into consideration. Furthermore, we derive the non-asymptotic properties of the value function and misclassification rate for the proposed method. Finally, simulation studies and a real data example are used to demonstrate the finite-sample performance of the proposed method.
RB5 Low-Cost Explorer: Implementing Autonomous Long-Term Exploration on Low-Cost Robotic Hardware

This systems paper presents the implementation and design of RB5, a wheeled robot for autonomous long-term exploration with fewer and cheaper sensors. Requiring just an RGB-D camera and low-power computing hardware, the system consists of an experimental platform with rocker-bogie suspension. It operates in unknown and GPS-denied environments and on indoor and outdoor terrains. The exploration consists of a methodology that extends frontier- and sampling-based exploration with a path-following vector field and a state-of-the-art SLAM algorithm. The methodology allows the robot to explore its surroundings at lower update frequencies, enabling the use of lower-performing and lower-cost hardware while still retaining good autonomous performance. The approach further consists of a methodology to interact with a remotely located human operator based on an inexpensive long-range and low-power communication technology from the internet-of-things domain (i.e., LoRa) and a customized communication protocol. The results and the feasibility analysis show the possible applications and limitations of the approach.
Coexistence of Superconductivity and Antiferromagnetism in Topological Magnet MnBi2Te4 Films

The interface of two materials can harbor unexpected emergent phenomena. One example is interface-induced superconductivity. In this work, we employ molecular beam epitaxy to grow a series of heterostructures formed by stacking together two non-superconducting antiferromagnetic materials, an intrinsic antiferromagnetic topological insulator MnBi2Te4 and an antiferromagnetic iron chalcogenide FeTe. Our electrical transport measurements reveal interface-induced superconductivity in these heterostructures. By performing scanning tunneling microscopy and spectroscopy measurements, we observe a proximity-induced superconducting gap on the top surface of the MnBi2Te4 layer, confirming the interaction between superconductivity and antiferromagnetism in the MnBi2Te4 layer. Our findings will advance the fundamental inquiries into the topological superconducting phase in hybrid devices and provide a promising platform for the exploration of chiral Majorana physics in MnBi2Te4-based heterostructures.
TikTokActions: A TikTok-Derived Video Dataset for Human Action Recognition

The increasing variety and quantity of tagged multimedia content on platforms such as TikTok provides an opportunity to advance computer vision modeling. We have curated a distinctive dataset of 283,582 unique video clips categorized under 386 hashtags relating to modern human actions. We release this dataset as a valuable resource for building domain-specific foundation models for human movement modeling tasks such as action recognition. To validate this dataset, which we name TikTokActions, we perform two sets of experiments. First, we pretrain the state-of-the-art VideoMAEv2 with a ViT-base backbone on TikTokActions subset, and then fine-tune and evaluate on popular datasets such as UCF101 and the HMDB51. We find that the performance of the model pre-trained using our Tik-Tok dataset is comparable to models trained on larger action recognition datasets (95.3% on UCF101 and 53.24% on HMDB51). Furthermore, our investigation into the relationship between pre-training dataset size and fine-tuning performance reveals that beyond a certain threshold, the incremental benefit of larger training sets diminishes. This work introduces a useful TikTok video dataset that is available for public use and provides insights into the marginal benefit of increasing pre-training dataset sizes for video-based foundation models.
On groups and fields interpretable in differentially closed valued fields and in various $\mathrm{NTP_2}$ fields

This paper aims at developing model-theoretic tools to study interpretable fields and definably amenable groups, mainly in $\mathrm{NIP}$ or $\mathrm{NTP_2}$ settings. An abstract theorem constructing definable group homomorphisms from generic data is proved. It relies heavily on a stabilizer theorem of Montenegro, Onshuus and Simon. The main application is a structure theorem for definably amenable groups that are interpretable in algebraically bounded perfect $\mathrm{NTP_2}$ fields with bounded Galois group (under some mild assumption on the imaginaries involved), or in algebraically bounded theories of (differential) NIP fields. These imply a classification of the fields interpretable in differentially closed valued fields, and structure theorems for fields interpretable in finitely ramified henselian valued fields of characteristic $0$, or in NIP algebraically bounded differential fields.
Problem-Parameter-Free Decentralized Nonconvex Stochastic Optimization

Existing decentralized algorithms usually require knowledge of problem parameters for updating local iterates. For example, the hyperparameters (such as learning rate) usually require the knowledge of Lipschitz constant of the global gradient or topological information of the communication networks, which are usually not accessible in practice. In this paper, we propose D-NASA, the first algorithm for decentralized nonconvex stochastic optimization that requires no prior knowledge of any problem parameters. We show that D-NASA has the optimal rate of convergence for nonconvex objectives under very mild conditions and enjoys the linear-speedup effect, i.e. the computation becomes faster as the number of nodes in the system increases. Extensive numerical experiments are conducted to support our findings.
Sudoku Number of Corona of Graphs

Let $G = (V,E)$ be a graph of order $n$ with chromatic number $\chi(G) = k$, let $S \subset V$ and let $C_0$ be a $k$-coloring of the induced subgraph $G[S]$. The coloring $C_0$ is called an extendable coloring, if $C_0$ can be extended to a $k$-coloring of $G$ and it is a Sudoku coloring of $G$ if the extension is unique. The smallest order of such an induced subgraph $G[S]$ of $G$ which admits a Sudoku coloring is called the Sudoku number of $G$ and is denoted by $sn(G)$. In this paper, we first introduce the notion of uniquely color extendable vertex and then we obtain the lower and upper bounds for the Sudoku number of $G \circ K_1$. Some families of graphs which attain these bounds are also obtained. The exact value of the Sudoku number of corona of $C_n$, $W_n$ and $K_n$ with $K_1$ and $C_n \circ P_m$ are also obtained.
Structure and magnetic properties of a La$_{0.75}$Sr$_{0.25}$Cr$_{0.90}$O$_{3-\delta}$ single crystal

We have successfully grown large and good-quality single crystals of the La$_{0.75}$Sr$_{0.25}$Cr$_{0.90}$O$_{3-\delta}$ compound using the floating-zone method with laser diodes. We investigated the crystal quality, crystallography, chemical composition, magnetic properties and the oxidation state of Cr in the grown single crystals by employing a combination of techniques, including X-ray Laue and powder diffraction, scanning electron microscopy, magnetization measurements, X-ray photoelectron spectroscopy and light absorption. The La$_{0.75}$Sr$_{0.25}$Cr$_{0.90}$O$_{3-\delta}$ single crystal exhibits a single-phase composition, crystallizing in a trigonal structure with the space group $R\bar{3}c$ at room temperature. The chemical composition was determined as La$_{0.75}$Sr$_{0.25}$Cr$_{0.90}$O$_{3-\delta}$, indicating a significant chromium deficiency. Upon warming, we observed five distinctive characteristic temperatures, namely $T_1 =$ 21.50(1) K, $T_2 =$ 34.98(1) K, $T_3 =$ 117.94(1) K, $T_4 =$ 155.01(1) K, and $T_{\textrm{N}} =$ 271.80(1) K, revealing five distinct magnetic anomalies. Our magnetization study allows us to explore the nature of these anomalies. Remarkably, the oxidation state of chromium in the single-crystal La$_{0.75}$Sr$_{0.25}$Cr$_{0.90}$O$_{3-\delta}$, characterized by a band gap of 1.630(8) eV, is exclusively attributed to Cr$^{3+}$ ions, making a departure from the findings of previous studies on polycrystalline materials.
Variable stars in galactic globular clusters. I. The population of RR Lyrae stars

We present a comprehensive catalog of $2824$ RR Lyrae stars (RRLs) residing in $115$ Galactic globular clusters (GCs). Our catalog includes 1594 fundamental-mode (RRab), 824 first-overtone (RRc), and 28 double-mode (RRd) RRLs, as well as 378 RRLs of an unknown pulsation mode. We cross-matched 481\,349 RRLs reported in the third data release (DR3) of the ESA mission and the literature to 170 known GCs. Membership probabilities were computed as the products of a position and shape-dependent prior and a likelihood was computed using parallaxes, proper motions, and, where available, radial velocities from Membership likelihoods of RRLs were computed by comparing cluster average parameters based on known member stars and the cross-matched RRLs. We determined empirical RRL instability strip (IS) boundaries based on our catalog and detected three new cluster RRLs inside this region via their excess $G-$band photometric uncertainties. We find that $77$ of RRLs in GCs are included in the DR3 Specific Object Study, and $82$ were classified as RRLs by the DR3 classifier, with the majority of the missing sources being located at the crowded GC centers. Surprisingly, we find that $25$ of cluster member stars located within the empirical IS are not RRLs and appear to be non-variable. Additionally, we find that $80$ of RRab, $84$ of RRc, and $100$ of the RRd stars are located within theoretical IS boundaries predicted using MESA models with $Z=0.0003$, $M=0.7$ \(M_ and $Y=0.290$. Unexpectedly, a higher $Y=0.357$ is required to fully match the location of RRc stars, and lower $Y=0.220$ is needed to match the location of RRab stars. Lastly, our catalog does not exhibit an Oosterhoff dichotomy, with at least 22 GCs located inside the Oosterhoff "gap," which is close to the mode of the distribution of mean RRL periods in GCs.