Miracle-dz/UniFAR-FTUs huggingface dataset:
Range-Angle Estimation for FDA-MIMO System With Frequency Offset

Frequency diverse array multiple-input multiple-output (FDA-MIMO) radar differs from the traditional phased array (PA) radar, and can form range-angle-dependent beampattern and differentiate between closely spaced targets sharing the same angle but occupying distinct range cells. In the FDA-MIMO radar, target range estimation is achieved by employing a subtle frequency variation between adjacent array antennas, so the estimation performance is degraded severely in a practical scenario with frequency offset. In this paper, the range-angle estimation problem for FDA-MIMO radar is considered with frequency offsets in both transmitting and receiving arrays. First, we build a system model for the FDA-MIMO radar with transmitting and receiving frequency offsets. Then, the frequency offset is transferred into an equalized additional noise. The noise characteristics are analyzed in detail theoretically, together with the influence on the range-angle estimation. Moreover, since the effect of the transmitting frequency offset is similar to additional colored noise, denoising algorithms are introduced to mitigate the performance deterioration caused by the frequency offset. Finally, Cram\'{e}r-Rao lower bounds (CRLB) for the range-angle estimation are derived in the scenario with the frequency offsets. Simulation results show the analysis of frequency offset and the corresponding estimation performance using different algorithms.
Multi-agent Task-Driven Exploration via Intelligent Map Compression and Sharing

This paper investigates the task-driven exploration of unknown environments with mobile sensors communicating compressed measurements. The sensors explore the area and transmit their compressed data to another robot, assisting it in reaching a goal location. We propose a novel communication framework and a tractable multi-agent exploration algorithm to select the sensors' actions. The algorithm uses a task-driven measure of uncertainty, resulting from map compression, as a reward function. We validate the efficacy of our algorithm through numerical simulations conducted on a realistic map and compare it with two alternative approaches. The results indicate that the proposed algorithm effectively decreases the time required for the robot to reach its target without causing excessive load on the communication network.
LDetector: A Low Overhead Race Detector For GPU Programs

Data race detection becomes an important problem in GPU programming. The paper presents a novel solution mainly aimed at detecting data races happening in shared memory accesses with no use of atomic primitives. It makes use of compiler support to privatize shared data and then at run time parallelizes data race checking. It has two distinct features. First, unlike previous existing work, our work gets rid of per memory access monitoring by data privatization technique, which brings a very low performance overhead and also well scalability. Second, data race checking utilizes massively parallel resources on GPU. Preliminary results show two orders of magnitude performance improvement over an existing work.
A self-routing protocol for distributed consensus on logical information

In this paper, we address decision making problems, depending on a set of input events, with networks of dynamic agents that have partial visibility of such events. Previous work by the authors proposed so-called logical consensus approach, by which a network of agents, that can exchange binary values representing their local estimates of the events, is able to reach a unique and consistent decision. The approach therein proposed is based on the construction of an iterative map, whose computation is centralized and guaranteed under suitable conditions on the input visibility and graph connectivity. Under the same conditions, we extend the approach in this work by allowing the construction of a logical linear consensus system that is globally stable in a fully distributed way. The effectiveness of the proposed method is showed through the real implementation of a wireless sensor network as a framework for the surveillance of an urban area.
Automated Feature Selection for Inverse Reinforcement Learning

Inverse reinforcement learning (IRL) is an imitation learning approach to learning reward functions from expert demonstrations. Its use avoids the difficult and tedious procedure of manual reward specification while retaining the generalization power of reinforcement learning. In IRL, the reward is usually represented as a linear combination of features. In continuous state spaces, the state variables alone are not sufficiently rich to be used as features, but which features are good is not known in general. To address this issue, we propose a method that employs polynomial basis functions to form a candidate set of features, which are shown to allow the matching of statistical moments of state distributions. Feature selection is then performed for the candidates by leveraging the correlation between trajectory probabilities and feature expectations. We demonstrate the approach's effectiveness by recovering reward functions that capture expert policies across non-linear control tasks of increasing complexity. Code, data, and videos are available at https://sites.google. com/view/feature4irl.
Nonparametric 'regression' when errors are positioned at end-points

Increasing practical interest has been shown in regression problems where the errors, or disturbances, are centred in a way that reflects particular characteristics of the mechanism that generated the data. In economics this occurs in problems involving data on markets, productivity and auctions, where it can be natural to centre at an end-point of the error distribution rather than at the distribution's mean. Often these cases have an extreme-value character. and in that broader context, examples involving meteorological, record-value and production-frontier data have been discussed in the literature. We shall discuss nonparametric methods for estimating regression curves in these settings, showing that they have features that contrast so starkly with those in better understood problems that they lead to apparent contradictions. For example, merely by centring errors at their end-points rather than their means the problem can chance from one with a familiar nonparametric character, where the optimal convergence rate is slower than n(-1/2), to one in the super-efficient class, where the optimal rate is faster than n(-1/2). Moreover, when the errors are centred in a non-standard way there is greater intrinsic interest in estimating characteristics of the error distribution, as well as of the regression mean itself. The paper will also address this aspect of the problem.
Early and Long-term Effects of the Autologous Peripheral Stem Cell Implantation for Critical Limb Ischemia.

OBJECTIVE Therapeutic angiogenesis by peripheral blood mononuclear cells (PB-MNCs) implantation has been shown to be a safe and effective treating for critical limb ischemia (CLI). We herein report our investigation of the long-term efficacy of implantation of granulocyte-colony stimulating factor (G-CSF)-induced PB-MNCs to treat patients with CLI for which surgical bypass and/or percutaneous transluminal angioplasty are not possible. Methods and Methods : Eleven cases were enrolled in this study. Following an injection of G-CSF (250 ug/day) for 3 days, PB-MNCs (1.1 ± 0.5 × 10(10) including 1.5 ± 0.2 × 10(7) CD34-positive cells) were harvested by apheresis and then injected into 13 ischemic limbs. RESULTS Resting pain either diminished or improved in 10 cases (91%) at 4 weeks, and ulcer formation was cured in 6 out of 10 limbs (60%) after treatment. The time required to enhance the arteries at the level of foot-joint by angiography which was performed in the abdominal aorta was shortened by 1 month (10 ± 4 seconds) and 6 months (12 ± 1) compared with the pretreatment time (15 ± 5). Three patients died after treatment, and the actuarial survival rate at 3 years was 73%. Freedom from major amputation at 3 years was 92%. CONCLUSION The local injection of G-CSF-induced PB-MNCs showed striking early and long-term effects.
Piecewise-Linear Manifolds for Deep Metric Learning

Unsupervised deep metric learning (UDML) focuses on learning a semantic representation space using only unlabeled data. This challenging problem requires accurately estimating the similarity between data points, which is used to supervise a deep network. For this purpose, we propose to model the high-dimensional data manifold using a piecewise-linear approximation, with each low-dimensional linear piece approximating the data manifold in a small neighborhood of a point. These neighborhoods are used to estimate similarity between data points. We empirically show that this similarity estimate correlates better with the ground truth than the similarity estimates of current state-of-the-art techniques. We also show that proxies, commonly used in supervised metric learning, can be used to model the piecewise-linear manifold in an unsupervised setting, helping improve performance. Our method outperforms existing unsupervised metric learning approaches on standard zero-shot image retrieval benchmarks.
Testing for Fault Diversity in Reinforcement Learning

Reinforcement Learning is the premier technique to approach sequential decision problems, including complex tasks such as driving cars and landing spacecraft. Among the software validation and verification practices, testing for functional fault detection is a convenient way to build trustworthiness in the learned decision model. While recent works seek to maximise the number of detected faults, none consider fault characterisation during the search for more diversity. We argue that policy testing should not find as many failures as possible (e.g., inputs that trigger similar car crashes) but rather aim at revealing as informative and diverse faults as possible in the model. In this paper, we explore the use of quality diversity optimisation to solve the problem of fault diversity in policy testing. Quality diversity (QD) optimisation is a type of evolutionary algorithm to solve hard combinatorial optimisation problems where high-quality diverse solutions are sought. We define and address the underlying challenges of adapting QD optimisation to the test of action policies. Furthermore, we compare classical QD optimisers to state-of-the-art frameworks dedicated to policy testing, both in terms of search efficiency and fault diversity. We show that QD optimisation, while being conceptually simple and generally applicable, finds effectively more diverse faults in the decision model, and conclude that QD-based policy testing is a promising approach.
Frequency-dependent covariance reveals critical spatio-temporal patterns of synchronized activity in the human brain

Recent analyses combining advanced theoretical techniques and high-quality data from thousands of simultaneously recorded neurons provide strong support for the hypothesis that neural dynamics operate near the edge of instability across regions in the brain. However, these analyses, as well as related studies, often fail to capture the intricate temporal structure of brain activity as they primarily rely on time-integrated measurements across neurons. In this study, we present a novel framework designed to explore signatures of criticality across diverse frequency bands and construct a much more comprehensive description of brain activity. Additionally, we introduce a method for projecting brain activity onto a basis of spatio-temporal patterns, facilitating time-dependent dimensionality reduction. Applying this framework to a magnetoencephalography dataset, we observe significant differences in both criticality signatures and spatio-temporal activity patterns between healthy subjects and individuals with Parkinson's disease.
Allspark: Workload Orchestration for Visual Transformers on Processing In-Memory Systems

The advent of Transformers has revolutionized computer vision, offering a powerful alternative to convolutional neural networks (CNNs), especially with the local attention mechanism that excels at capturing local structures within the input and achieve state-of-the-art performance. Processing in-memory (PIM) architecture offers extensive parallelism, low data movement costs, and scalable memory bandwidth, making it a promising solution to accelerate Transformer with memory-intensive operations. However, the crucial challenge lies in efficiently deploying the entire model onto a resource-limited PIM system while parallelizing each transformer block with potentially many computational branches based on local attention mechanisms. We present Allspark, which focuses on workload orchestration for visual Transformers on PIM systems, aiming at minimizing inference latency. Firstly, to fully utilize the massive parallelism of PIM, Allspark empolys a finer-grained partitioning scheme for computational branches, and format a systematic layout and interleaved dataflow with maximized data locality and reduced data movement. Secondly, Allspark formulates the scheduling of the complete model on a resource-limited distributed PIM system as an integer linear programming (ILP) problem. Thirdly, as local-global data interactions exhibit complex yet regular dependencies, Allspark provides a greedy-based mapping method to allocate computational branches onto the PIM system and minimize NoC communication costs. Extensive experiments on 3D-stacked DRAM-based PIM systems show that Allspark brings 1.2x-24.0x inference speedup for various visual Transformers over baselines, and that Allspark-enriched PIM system yields average speedups of 2.3x and energy savings of 20x-55x over Nvidia V100 GPU.
Entropy and the City: Origins, trajectories and explorations of the concept in urban science

Entropy is arguably one of the most powerful concepts to understand the world, from the behavior of molecules to the expansion of the universe, from how life emerges to how hybrid complex systems like cities come into being and continue existing. Yet, despite its widespread application, it is also one of the most misunderstood concepts across the sciences. This chapter seeks to demystify entropy and its main interpretations, along with some of its explorations in the context of cities. It first establishes the foundations of the concept by describing its trajectory since its inception in thermodynamics and statistical mechanics in the 19th century, its different incarnations from Boltzmanns pioneering formulation and Shannons information theory to its absorption in biology and the social sciences, until it reaches a nascent urban science in the late 1960s. The chapter then identifies some of the main domains in which entropy has been explored to understand cities as complex systems, from entropy-maximizing models of spatial interaction and applications as a measure of urban form, diversity, and complexity to a tool for understanding conditions of self-organization and urban sustainability.
Simulating Audiences: Automating Analysis of Values, Attitudes, and Sentiment

Current events such as the Park51 Project in downtown Manhattan create "critical discourse moments," explosions of discourse around a topic that can be exploited for data gathering. Policymakers have a need to understand the dynamics of public discussion in real time. Human values, which are cognitively related to attitudes and serve as reference points in moral argument, are important indicators of what's at stake in a public controversy. This work shows that it is possible to link values data with reader behavior to infer values implicit in a topical corpus, and that it is possible to automate this process using machine learning.
Entanglement Hamiltonian for inhomogeneous free fermions

We study the entanglement Hamiltonian for the ground state of one-dimensional free fermions in the presence of an inhomogeneous chemical potential. In particular, we consider a lattice with a linear, as well as a continuum system with a quadratic potential. It is shown that, for both models, conformal field theory predicts a Bisognano-Wichmann form for the entangement Hamiltonian of a half-infinite system. Furthermore, despite being nonrelativistic, this result is inherited by our models in the form of operators that commute exactly with the entanglement Hamiltonian. After appropriate rescaling, they also yield an excellent approximation of the entanglement spectra, which becomes asymptotically exact in the bulk of the trapped Fermi gas. For the gradient chain, however, the conformal result is recovered only after taking a proper continuum limit.
Remote physical device fingerprinting

We introduce the area of remote physical device fingerprinting, or fingerprinting a physical device, as opposed to an operating system or class of devices, remotely, and without the fingerprinted device's known cooperation. We accomplish this goal by exploiting small, microscopic deviations in device hardware: clock skews. Our techniques do not require any modification to the fingerprinted devices. Our techniques report consistent measurements when the measurer is thousands of miles, multiple hops, and tens of milliseconds away from the fingerprinted device, and when the fingerprinted device is connected to the Internet from different locations and via different access technologies. Further one can apply our passive and semi-passive techniques when the fingerprinted device is behind a NAT or firewall, and also when the device's system time is maintained via NTP or SNTP. One can use our techniques to obtain information about whether two devices an the Internet, possibly shifted in time or IP addresses, are actually the same physical device. Example applications include: computer forensics; tracking, with some probability, a physical device as it connects to the Internet from different public access points; counting the number of devices behind a NAT even when the devices use constant or random IP ID; remotely probing a block of addresses to determine if the addresses correspond to virtual hosts, e.g., as part of a virtual honeynet; and unanonymizing anonymized network traces.
The new intermediate polar RX J1238-38: a system below the period gap?

We present optical observations of the recently discovered ROSAT source RX J1238 ¹ 38, which is a new member of the intermediate polar class of asynchronous magnetic cataclysmic variables (CVs). Optical photometry reveals two coherent periodicities at 1860 and 2147 s respectively, with similar amplitudes of (cid:44) 8 per cent. Infrared ( J -band) intensity variations are detected only at the 1860-s period, at an amplitude of (cid:44) 15 per cent. The initial hypothesis, that these two periods were the spin and synodic (i.e., beat) period respectively, appears not to be supported by the spectroscopic data. The emission lines vary on the longer photometric period, and radial velocity variations are detected at this period and at a longer period of (cid:44) 5300 s, which we identify as the spin and orbital periods respectively. The most likely explanation for the 1860-s period is that it is the ﬁrst harmonic of the (cid:113) ¹ (cid:81) sideband, leading to an improved determination of the orbital period as 5077 s ( ¼ 84 min). If this interpretation is correct, RX J1238 ¹ 38 joins EX Hya as the only other intermediate polar below the 2–3 h period gap, and with an orbital period close to the minimum for CVs with non-degenerate secondaries. The spin-modulated emission-line radial velocities and widths appear to be anticorrelated, with maximum width occurring at maximum blueshift. Such an anticorrelation is expected for aspect changes of accretion curtains. Polarimetric observations of RX J1238 ¹ 38 were inconclusive, although we can put a limit of 0.4 per cent on any variability on the circular polarization, and certainly there is no indication of variations at the photometric or spectroscopic periods.
Topological analysis and experimental control of transformations of domain walls in magnetic cylindrical nanowires

Topology is a powerful tool for categorizing magnetization textures, highlighting specific features in both 2D systems, such as thin films or curved surfaces, and in 3D bulk systems. In the emerging field of 3D nanomagnetism within confined geometries, the contributions from both volume and surface must be considered, requiring appropriate topological analysis to obtain a complete view of the system. Here, we consider domain walls in cylindrical magnetic nanowires to illustrate the use of topological invariants. We begin with micromagnetic simulations of domain wall transformation under the stimulus of an \OErsted field, tracking bulk and surface topological signatures, and analyzing the interplay between multiple micromagnetic objects. For instance, the extensive analysis allowed us to highlight mechanisms of domain wall type conversion from topologically non-trivial to trivial states, a phenomenon disregarded in previous studies. Additionally, we provide experimental evidence of the transient states predicted to occur during the dynamical process.
Can large language models explore in-context?

We investigate the extent to which contemporary Large Language Models (LLMs) can engage in exploration, a core capability in reinforcement learning and decision making. We focus on native performance of existing LLMs, without training interventions. We deploy LLMs as agents in simple multi-armed bandit environments, specifying the environment description and interaction history entirely in-context, i.e., within the LLM prompt. We experiment with GPT-3.5, GPT-4, and Llama2, using a variety of prompt designs, and find that the models do not robustly engage in exploration without substantial interventions: i) Across all of our experiments, only one configuration resulted in satisfactory exploratory behavior: GPT-4 with chain-of-thought reasoning and an externally summarized interaction history, presented as sufficient statistics; ii) All other configurations did not result in robust exploratory behavior, including those with chain-of-thought reasoning but unsummarized history. Although these findings can be interpreted positively, they suggest that external summarization -- which may not be possible in more complex settings -- is important for obtaining desirable behavior from LLM agents. We conclude that non-trivial algorithmic interventions, such as fine-tuning or dataset curation, may be required to empower LLM-based decision making agents in complex settings.
DP-Dueling: Learning from Preference Feedback without Compromising User Privacy

We consider the well-studied dueling bandit problem, where a learner aims to identify near-optimal actions using pairwise comparisons, under the constraint of differential privacy. We consider a general class of utility-based preference matrices for large (potentially unbounded) decision spaces and give the first differentially private dueling bandit algorithm for active learning with user preferences. Our proposed algorithms are computationally efficient with near-optimal performance, both in terms of the private and non-private regret bound. More precisely, we show that when the decision space is of finite size $K$, our proposed algorithm yields order optimal $O\Big(\sum_{i = 2}^K\log\frac{KT}{\Delta_i} + \frac{K}{\epsilon}\Big)$ regret bound for pure $\epsilon$-DP, where $\Delta_i$ denotes the suboptimality gap of the $i$-th arm. We also present a matching lower bound analysis which proves the optimality of our algorithms. Finally, we extend our results to any general decision space in $d$-dimensions with potentially infinite arms and design an $\epsilon$-DP algorithm with regret $\tilde{O} \left( \frac{d^6}{\kappa \epsilon } + \frac{ d\sqrt{T }}{\kappa} \right)$, providing privacy for free when $T \gg d$.