Miracle-dz/UniFAR-FTUs huggingface dataset:
Pilot Performance modeling via observer-based inverse reinforcement learning

The focus of this paper is behavior modeling for pilots of unmanned aerial vehicles. The pilot is assumed to make decisions that optimize an unknown cost functional, which is estimated from observed trajectories using a novel inverse reinforcement learning (IRL) framework. The resulting IRL problem often admits multiple solutions. In this paper, a recently developed novel IRL observer is adapted to the pilot modeling problem. The observer is shown to converge to one of the equivalent solutions of the IRL problem. The developed technique is implemented on a quadcopter where the pilot is a linear quadratic supervisory controller that generates velocity commands for the quadcopter to travel to and hover over a desired location. Experimental results demonstrate the robustness of the method and its ability to learn equivalent cost functionals.
An Intent Taxonomy of Legal Case Retrieval

Legal case retrieval is a special Information Retrieval (IR) task focusing on legal case documents. Depending on the downstream tasks of the retrieved case documents, users’ information needs in legal case retrieval could be significantly different from those in Web search and traditional ad hoc retrieval tasks. While there are several studies that retrieve legal cases based on text similarity, the underlying search intents of legal retrieval users, as shown in this article, are more complicated than that yet mostly unexplored. To this end, we present a novel hierarchical intent taxonomy of legal case retrieval. It consists of five intent types categorized by three criteria, i.e., search for Particular Case(s), Characterization, Penalty, Procedure, and Interest. The taxonomy was constructed transparently and evaluated extensively through interviews, editorial user studies, and query log analysis. Through a laboratory user study, we reveal significant differences in user behavior and satisfaction under different search intents in legal case retrieval. Furthermore, we apply the proposed taxonomy to various downstream legal retrieval tasks, e.g., result ranking and satisfaction prediction, and demonstrate its effectiveness. Our work provides important insights into the understanding of user intents in legal case retrieval and potentially leads to better retrieval techniques in the legal domain, such as intent-aware ranking strategies and evaluation methodologies.
New Adaptive Low-Dissipation Central-Upwind Schemes

We introduce new second-order adaptive low-dissipation central-upwind (LDCU) schemes for the one- and two-dimensional hyperbolic systems of conservation laws. The new adaptive LDCU schemes employ the LDCU numerical fluxes (recently proposed in [{\sc A. Kurganov and R. Xin}, J. Sci. Comput., 96 (2023), Paper No. 56]) computed using the point values reconstructed with the help of adaptively selected nonlinear limiters. To this end, we use a smoothness indicator to detect ``rough'' parts of the computed solution, where the piecewise linear reconstruction is performed using an overcompressive limiter, which leads to extremely sharp resolution of shock and contact waves. In the ``smooth'' areas, we use a more dissipative limiter to prevent the appearance of artificial kinks and staircase-like structures there. In order to avoid oscillations, we perform the reconstruction in the local characteristic variables obtained using the local characteristic decomposition. We test two different smoothness indicators and apply the developed schemes to the one- and two-dimensional Euler equations of gas dynamics. The obtained numerical results clearly demonstrate that the new adaptive LDCU schemes outperform the original ones.
Chandra X-Ray Observatory Observations of 13 Fermi LAT Sources

In the latest data release from the Fermi Gamma-ray Space Telescope (the 4th Fermi LAT 14 yr Catalog, or 4FGL), more than 50% of the Galactic sources are yet to be identified. We observed 13 unidentified Fermi LAT sources with the Chandra X-Ray Observatory to explore their nature. We report the results of the classification of X-ray sources in the fields of these γ-ray sources and discuss the implications for their nature. We use multiwavelength (MW) data for a machine-learning classification, accompanied by a more detailed spectral/variability analysis for brighter sources. Eight 4FGL sources have γ-ray pulsars within their position error ellipses. We consider three of these pulsars (PSR J1906+0722, PSR J1105–6037, and PSR J1358–6025) to be detected in X-rays, while PSR J1203–6242 shows a hint of X-ray emission. Within the positional uncertainties of three of the 4FGL sources, we detect X-ray sources that may be yet unknown pulsars, depending on the MW association. In addition to point sources, we discovered two extended sources, one of which is likely to be a bow-shock pulsar-wind nebula associated with PSR J1358–6025. Finally, we classify other X-ray sources detected in these observations and report the most interesting classifications.
Unbiased Weight Maximization

A biologically plausible method for training an Artificial Neural Network (ANN) involves treating each unit as a stochastic Reinforcement Learning (RL) agent, thereby considering the network as a team of agents. Consequently, all units can learn via REINFORCE, a local learning rule modulated by a global reward signal, which aligns more closely with biologically observed forms of synaptic plasticity. Nevertheless, this learning method is often slow and scales poorly with network size due to inefficient structural credit assignment, since a single reward signal is broadcast to all units without considering individual contributions. Weight Maximization, a proposed solution, replaces a unit's reward signal with the norm of its outgoing weight, thereby allowing each hidden unit to maximize the norm of the outgoing weight instead of the global reward signal. In this research report, we analyze the theoretical properties of Weight Maximization and propose a variant, Unbiased Weight Maximization. This new approach provides an unbiased learning rule that increases learning speed and improves asymptotic performance. Notably, to our knowledge, this is the first learning rule for a network of Bernoulli-logistic units that is unbiased and scales well with the number of network's units in terms of learning speed.
Wedge product theorem in compensated compactness theory with critical exponents on Riemannian manifolds

We formulate and prove compensated compactness theorems concerning the limiting behaviour of wedge products of weakly convergent differential forms on closed Riemannian manifolds. The case of critical regularity exponents is considered, which goes beyond the regularity regime entailed by H\"{o}lder's inequality. Implications on the weak continuity of $L^p$-extrinsic geometry of isometric immersions of Riemannian manifolds are discussed.
Federated Split Learning with Only Positive Labels for resource-constrained IoT environment

Distributed collaborative machine learning (DCML) is a promising method in the Internet of Things (IoT) domain for training deep learning models, as data is distributed across multiple devices. A key advantage of this approach is that it improves data privacy by removing the necessity for the centralized aggregation of raw data but also empowers IoT devices with low computational power. Among various techniques in a DCML framework, federated split learning, known as splitfed learning (SFL), is the most suitable for efficient training and testing when devices have limited computational capabilities. Nevertheless, when resource-constrained IoT devices have only positive labeled data, multiclass classification deep learning models in SFL fail to converge or provide suboptimal results. To overcome these challenges, we propose splitfed learning with positive labels (SFPL). SFPL applies a random shuffling function to the smashed data received from clients before supplying it to the server for model training. Additionally, SFPL incorporates the local batch normalization for the client-side model portion during the inference phase. Our results demonstrate that SFPL outperforms SFL: (i) by factors of 51.54 and 32.57 for ResNet-56 and ResNet-32, respectively, with the CIFAR-100 dataset, and (ii) by factors of 9.23 and 8.52 for ResNet-32 and ResNet-8, respectively, with CIFAR-10 dataset. Overall, this investigation underscores the efficacy of the proposed SFPL framework in DCML.
A Snoring Sound Dataset for Body Position Recognition: Collection, Annotation, and Analysis

Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) is a chronic breathing disorder caused by a blockage in the upper airways. Snoring is a prominent symptom of OSAHS, and previous studies have attempted to identify the obstruction site of the upper airways by snoring sounds. Despite some progress, the classification of the obstruction site remains challenging in real-world clinical settings due to the influence of sleep body position on upper airways. To address this challenge, this paper proposes a snore-based sleep body position recognition dataset (SSBPR) consisting of 7570 snoring recordings, which comprises six distinct labels for sleep body position: supine, supine but left lateral head, supine but right lateral head, left-side lying, right-side lying and prone. Experimental results show that snoring sounds exhibit certain acoustic features that enable their effective utilization for identifying body posture during sleep in real-world scenarios.
Holistic Exploration on Universal Decompositional Semantic Parsing: Architecture, Data Augmentation, and LLM Paradigm

In this paper, we conduct a holistic exploration of the Universal Decompositional Semantic (UDS) Parsing. We first introduce a cascade model for UDS parsing that decomposes the complex parsing task into semantically appropriate subtasks. Our approach outperforms the prior models, while significantly reducing inference time. We also incorporate syntactic information and further optimized the architecture. Besides, different ways for data augmentation are explored, which further improve the UDS Parsing. Lastly, we conduct experiments to investigate the efficacy of ChatGPT in handling the UDS task, revealing that it excels in attribute parsing but struggles in relation parsing, and using ChatGPT for data augmentation yields suboptimal results. Our code is available at https://github.com/hexuandeng/HExp4UDS.
Cloud Render Farm Services Discovery Using NLP And Ontology Based Knowledge Graph

Cloud render farm services are the animation domain specific cloud services Platform-as-a-Service (PaaS) type of cloud services that provides a complete platform to render the animation files. However, identifying the render farm services that is cost effective and also matches the functional requirements that changes for almost every project like the animation software, plug-ins required etc., is a challenge. This research work proposes an ontology-based service discovery engine named RenderSelect for the cloud render farm services. The cloud render farm ontology semantically defines the relationship among the cloud render farm services. The knowledge-based reasoning algorithms namely, the Concept similarity reasoning, Equivalent reasoning and the Numerical similarity reasoning have been applied to determine the similarity among the cloud services. The service discovery engine was evaluated for finding the services under three different scenarios namely a) with help of the ontology, b) without the help of the ontology and c) using a common search engine on the internet. The results show that the proposed service discovery engine which is specifically designed for the cloud render farm services using the ontology performs significantly better than the other two.
HasTEE: Programming Trusted Execution Environments with Haskell

Trusted Execution Environments (TEEs) are hardware enforced memory isolation units, emerging as a pivotal security solution for security-critical applications. TEEs, like Intel SGX and ARM TrustZone, allow the isolation of confidential code and data within an untrusted host environment, such as the cloud and IoT. Despite strong security guarantees, TEE adoption has been hindered by an awkward programming model. This model requires manual application partitioning and the use of error-prone, memory-unsafe, and potentially information-leaking low-level C/C++ libraries. We address the above with HasTEE, a domain-specific language (DSL) embedded in Haskell for programming TEE applications. HasTEE includes a port of the GHC runtime for the Intel-SGX TEE.HasTEE uses Haskell’s type system to automatically partition an application and to enforce Information Flow Control on confidential data. The DSL, being embedded in Haskell, allows for the usage of higher-order functions, monads, and a restricted set of I/O operations to write any standard Haskell application. Contrary to previous work, HasTEE is lightweight, simple, and is provided as a simple security library; thus avoiding any GHC modifications. We show the applicability of HasTEE by implementing case studies on federated learning, an encrypted password wallet, and a differentially-private data clean room.
Accurate displacement-measuring interferometer with wide range using an I2 frequency-stabilized laser diode based on sinusoidal frequency modulation

We propose the use of the sinusoidal frequency modulation technique to improve both the frequency stability of an external cavity laser diode (ECLD) and the measurement accuracy and range of a displacement-measuring interferometer. The frequency of the ECLD was modulated at 300 kHz by modulating the injection current, and it was locked to the b21 hyperfine component of the transition 6-3, P(33), 127I2 (633 nm) by the null method. A relative frequency stability of 6.5 × 10−11 was achieved at 100 s sampling time. The stabilized ECLD was then utilized as a light source for an unbalanced Michelson interferometer. In the interferometer, the displacement and direction of the target mirror can be determined using a Lissajous diagram based on two consecutive and quadrant-phase harmonics of the interference signal. Generally, the measurement range of the interferometer by the proposed method is limited by the modulation index and the signal-to-noise ratio of the harmonics. To overcome this drawback, suitable consecutive harmonic pairs were selected for the specific measurement ranges to measure the displacement. The displacements determined in the specific ranges by the proposed method were compared with those observed by a commercial capacitive sensor. From the comparison, the proposed method has high precision to determine the displacement. The measurement range was also extended up to 10 m by selecting a suitable modulation index and suitable consecutive pairs of harmonics.
Parametric Subtyping for Structural Parametric Polymorphism

We study the interaction of structural subtyping with parametric polymorphism and recursively defined type constructors. Although structural subtyping is undecidable in this setting, we describe a notion of parametricity for type constructors and then exploit it to define parametric subtyping, a conceptually simple, decidable, and expressive fragment of structural subtyping that strictly generalizes rigid subtyping. We present and prove correct an effective saturation-based decision procedure for parametric subtyping, demonstrating its applicability using a variety of examples. We also provide an implementation of this decision procedure as an artifact.
Competing phases and intertwined orders in coupled wires near the self-dual point

The interplay between different quantum phases plays an important role in strongly correlated systems, such as high-$T_c$ cuprates, quantum spin systems, and ultracold atoms. In particular, the application of effective field theory model and renormalization group analysis suggested that the coexistence of density wave (DW) and superfluid (SF) orders can lead to a supersolid phase of ultracold bosons. Here we revisit the problem by considering weakly coupled wires, where we treat the intra-wire interactions exactly via bosonization and inter-wire couplings using a mean-field theory which becomes asymptotically exact in the limit of high dimensionality. We obtain and solve the mean-field equations for the system near the self-dual point, where each wire has the Luttinger parameter $K=1$ and the inter-wire DW and SF coupling strengths are identical. This allows us to find explicit solutions for the possible supersolid order. An energy comparison between different possible solutions shows that the supersolid order is energetically unfavorable at zero temperature. This suggests that the density wave and superfluid phases are connected by a first order transition near the self-dual point. We also discuss the relation between our work and the intertwining of charge density wave and superconducting orders in cuprates.
Do algorithms and barriers for sparse principal component analysis extend to other structured settings?

We study a principal component analysis problem under the spiked Wishart model in which the structure in the signal is captured by a class of union-of-subspace models. This general class includes vanilla sparse PCA as well as its variants with graph sparsity. With the goal of studying these problems under a unified statistical and computational lens, we establish fundamental limits that depend on the geometry of the problem instance, and show that a natural projected power method exhibits local convergence to the statistically near-optimal neighborhood of the solution. We complement these results with end-to-end analyses of two important special cases given by path and tree sparsity in a general basis, showing initialization methods and matching evidence of computational hardness. Overall, our results indicate that several of the phenomena observed for vanilla sparse PCA extend in a natural fashion to its structured counterparts.
Smartpick: Workload Prediction for Serverless-enabled Scalable Data Analytics Systems

Many data analytic systems have adopted a newly emerging compute resource, serverless (SL), to handle data analytics queries in a timely and cost-efficient manner, i.e., serverless data analytics. While these systems can start processing queries quickly thanks to the agility and scalability of SL, they may encounter performance-and cost-bottlenecks based on workloads due to SL's worse performance and more expensive cost than traditional compute resources, e.g., virtual machine (VM). In this paper, we introduce Smartpick, a SL-enabled scalable data analytics system that exploits SL and VM together to realize composite benefits, i.e., agility from SL and better performance with reduced cost from VM. Smartpick uses a machine learning prediction scheme, decision-tree based Random Forest with Bayesian Optimizer, to determine SL and VM configurations, i.e., how many SL and VM instances for queries, that meet cost-performance goals. Smartpick offers a knob for applications to allow them to explore a richer cost-performance tradeoff space opened by exploiting SL and VM together. To maximize the benefits of SL, Smartpick supports a simple but strong mechanism, called relay-instances. Smartpick also supports event-driven prediction model retraining to deal with workload dynamics. A Smartpick prototype was implemented on Spark and deployed on live testbeds, Amazon AWS and Google Cloud Platform. Evaluation results indicate 97.05% and 83.49% prediction accuracies respectively with up to 50% cost reduction as opposed to the baselines. The results also confirm that Smartpick allows data analytics applications to navigate the richer cost-performance tradeoff space efficiently and to handle workload dynamics effectively and automatically.
General-Purpose Multi-Modal OOD Detection Framework

Out-of-distribution (OOD) detection identifies test samples that differ from the training data, which is critical to ensuring the safety and reliability of machine learning (ML) systems. While a plethora of methods have been developed to detect uni-modal OOD samples, only a few have focused on multi-modal OOD detection. Current contrastive learning-based methods primarily study multi-modal OOD detection in a scenario where both a given image and its corresponding textual description come from a new domain. However, real-world deployments of ML systems may face more anomaly scenarios caused by multiple factors like sensor faults, bad weather, and environmental changes. Hence, the goal of this work is to simultaneously detect from multiple different OOD scenarios in a fine-grained manner. To reach this goal, we propose a general-purpose weakly-supervised OOD detection framework, called WOOD, that combines a binary classifier and a contrastive learning component to reap the benefits of both. In order to better distinguish the latent representations of in-distribution (ID) and OOD samples, we adopt the Hinge loss to constrain their similarity. Furthermore, we develop a new scoring metric to integrate the prediction results from both the binary classifier and contrastive learning for identifying OOD samples. We evaluate the proposed WOOD model on multiple real-world datasets, and the experimental results demonstrate that the WOOD model outperforms the state-of-the-art methods for multi-modal OOD detection. Importantly, our approach is able to achieve high accuracy in OOD detection in three different OOD scenarios simultaneously. The source code will be made publicly available upon publication.
Revisiting the implications of Liouville's theorem to the anisotropy of cosmic rays

We present a solution to Liouville's equation for an ensemble of charged particles propagating in magnetic fields. The solution is presented using an expansion in spherical harmonics of the phase space density, allowing a direct interpretation of the distribution of arrival directions of cosmic rays. The results are found for chosen conditions of variability and source distributions. We show there are two conditions for an initially isotropic flux of particles to remain isotropic while traveling through a magnetic field: isotropy and homogeneity of the sources. In case isotropically-distributed sources inject particles continuously in time, a transient magnetic induced dipole will appear. This dipole will vanish if the system reaches a steady-state. The formalism is used to analyze the data measured by the Pierre Auger Observatory, contributing to the understanding of the dependence of the dipole amplitude with energy and predicting the energy in which the quadrupole signal should be measured.
The Method of Types for the AWGN Channel

For the discrete-time AWGN channel with a power constraint, we give an alternative derivation of Shannon's sphere-packing upper bound on the optimal block error exponent and prove for the first time an analogous lower bound on the optimal correct-decoding exponent. The derivations use the method of types with finite alphabets of sizes depending on the block length n and with the number of types sub-exponential in n.