Miracle-dz/UniFAR-FTUs huggingface dataset:
Galactic Cosmic Rays at Mars and Venus: Temporal Variations from Hours to Decades Measured as the Background Signal of Onboard Microchannel Plates

A microchannel plate (MCP) is a component widely used for counting particles in space. Using the background counts from MCPs on the Mars Express and Venus Express orbiters—operating over 17 yr and 8 yr, respectively—we investigated the galactic cosmic ray (GCR) characteristics of the inner solar system. The MCP background counts at Mars and Venus, on a solar cycle timescale, exhibited clear anticorrelation with the sunspot number. We concluded that the measured MCP background counts contained GCR information. The GCR characteristics measured using the MCP background counts at Mars showed features consistent with measurements on Earth in Solar Cycle 24. The time lag between the sunspot number and the MCP background counts was found to be ∼9 months at Mars. The shorter-term background data recorded along the orbits (with a timescale of several hours) also showed evident depletion of the background counts, due to absorption of the GCR particles by the planets. Thanks to the visible planetary size change along an orbit, we developed a model to separate the GCR contribution to the MCP background counts from the internal contribution caused by the β-decay of radioactive elements in the MCP glass. Our statistical analysis of the GCR absorption signatures at Mars implies that the effective absorption radius of Mars for the GCR particles is >100 km larger than the radius of the planet. However, the cause remains an open question.
Neutrino electromagnetic properties in a Leptoquark model

The neutrino magnetic dipole moment is studied in a simple extension of the SM augmented with a scalar Leptoquark with quantum numbers (¯ 3 , 1 , 1 / 3). In order to reach a sizeable neutrino magnetic moment, we include in the framework the contribution of right-handed neutrinos and analytical result for the one-loop contribution of a scalar LQ is presented. The allowed parameter space is found through recent restrictions on the muon ( g − 2), the τ → µγ and the B c → τ − ¯ ν decay. It is shown that exist a region of the parameter space where the recent anomaly reported by the XENON collaboration can be accommodated.
Virtual Relational Knowledge Graphs for Recommendation

Incorporating knowledge graph as side information has become a new trend in recommendation systems. Recent studies regard items as entities of a knowledge graph and leverage graph neural networks to assist item encoding, yet by considering each relation type individually. However, relation types are often too many and sometimes one relation type involves too few entities. We argue that it is not efficient nor effective to use every relation type for item encoding. In this paper, we propose a VRKG4Rec model (Virtual Relational Knowledge Graphs for Recommendation), which explicitly distinguish the influence of different relations for item representation learning. We first construct virtual relational graphs (VRKGs) by an unsupervised learning scheme. We also design a local weighted smoothing (LWS) mechanism for encoding nodes, which iteratively updates a node embedding only depending on its own embedding and its neighbors’, but involve no additional training parameters. We also employ the LWS mechanism on a user-item bipartite graph for user representation learning, which utilizes items’ encodings with relational knowledge to help train users’ representations. Experiment results on two public datasets validate that our VRKG4Rec model outperforms the state-of-the-art methods.
Time-domain Ad-hoc Array Speech Enhancement Using a Triple-path Network

Deep neural networks (DNNs) are very effective for multichannel speech enhancement with fixed array geometries. However, it is not trivial to use DNNs for ad-hoc arrays with unknown order and placement of microphones. We propose a novel triple-path network for ad-hoc array processing in the time domain. The key idea in the network design is to divide the overall processing into spatial processing and temporal processing and use self-attention for spatial processing. Using self-attention for spatial processing makes the network invariant to the order and the number of microphones. The temporal processing is done independently for all channels using a recently proposed dual-path attentive recurrent network. The proposed network is a multiple-input multiple-output architecture that can simultaneously enhance signals at all microphones. Experimental results demonstrate the excellent performance of the proposed approach. Further, we present analysis to demonstrate the effectiveness of the proposed network in utilizing multichannel information even from microphones at far locations.
Distributed Anomaly Detection and Estimation over Sensor Networks: Observational-Equivalence and $Q$-Redundant Observer Design

In this paper, we study stateless and stateful physics-based anomaly detection scenarios via distributed estimation over sensor networks. In the stateful case, the detector keeps track of the sensor residuals (i.e., the difference of estimated and true outputs) and reports an alarm if certain statistics of the recorded residuals deviate over a predefined threshold, e.g., $\chi^{2}$ (Chi-square) detector. Instead, only instantaneous deviation of the residuals raises the alarm in the stateless case without considering the history of the sensor outputs and estimation data. Given (approximate) false-alarm rate for both cases, we propose a probabilistic threshold design based on the noise statistics. We show by simulation that increasing the window length in the stateful case may not necessarily reduce the false-alarm rate. On the other hand, it adds unwanted delay to raise the alarm. The distributed aspect of the proposed detection algorithm enables local isolation of the faulty sensors with possible recovery solutions by adding redundant observationally-equivalent sensors. We, then, offer a mechanism to design $Q$-redundant distributed observers, robust to failure (or removal) of up to $Q$ sensors over the network.
Find Another me Across the World - Large-Scale Semantic Trajectory Analysis Using Spark

In today's society, location-based services are widely used which collect a huge amount of human trajectories. Analyzing semantic meanings of these trajectories can benefit numerous real-world applications, such as product advertisement, friend recommendation, and social behavior analysis. However, existing works on semantic trajectories are mostly centralized approaches that are not able to keep up with the rapidly growing trajectory collections. In this paper, we propose a novel large-scale semantic trajectory analysis algorithm in Apache Spark. We design a new hash function along with efficient distributed algorithms that can quickly compute semantic trajectory similarities and identify communities of people with similar behavior across the world. The experimental results show that our approach is more than 30 times faster than centralized approaches without sacrificing any accuracy like other parallel approaches.
Co-VQA : Answering by Interactive Sub Question Sequence

Most existing approaches to Visual Question Answering (VQA) answer questions directly, however, people usually decompose a complex question into a sequence of simple sub questions and finally obtain the answer to the original question after answering the sub question sequence(SQS). By simulating the process, this paper proposes a conversation-based VQA (Co-VQA) framework, which consists of three components: Questioner, Oracle, and Answerer. Questioner raises the sub questions using an extending HRED model, and Oracle answers them one-by-one. An Adaptive Chain Visual Reasoning Model (ACVRM) for Answerer is also proposed, where the question-answer pair is used to update the visual representation sequentially. To perform supervised learning for each model, we introduce a well-designed method to build a SQS for each question on VQA 2.0 and VQA-CP v2 datasets. Experimental results show that our method achieves state-of-the-art on VQA-CP v2. Further analyses show that SQSs help build direct semantic connections between questions and images, provide question-adaptive variable-length reasoning chains, and with explicit interpretability as well as error traceability.
R(Det)2: Randomized Decision Routing for Object Detection

In the paradigm of object detection, the decision head is an important part, which affects detection performance significantly. Yet how to design a high-performance decision head remains to be an open issue. In this paper, we propose a novel approach to combine decision trees and deep neural networks in an end-to-end learning manner for object detection. First, we disentangle the decision choices and prediction values by plugging soft decision trees into neural networks. To facilitate effective learning, we propose randomized decision routing with node selective and associative losses, which can boost the feature representative learning and network decision simultaneously. Second, we develop the decision head for object detection with narrow branches to generate the routing probabilities and masks, for the purpose of obtaining divergent decisions from different nodes. We name this approach as the randomized decision routing for object detection, abbreviated as R(Det)2. Experiments on MS-COCO dataset demonstrate that R(Det)2 is effective to improve the detection performance. Equipped with existing detectors, it achieves 1.4 ~ 3.6% AP improvement.
Near-infrared Spectroscopy of the Nucleus of Low-activity Comet P/2016 BA14 during Its 2016 Close Approach

The near-Earth comet P/2016 BA14 (PanSTARRS) is a slow-rotating, nearly dormant object, a likely dynamical twin of 252P/LINEAR, and was recently shown to have a mid-infrared spectrum very dissimilar to other comets. Comet BA14 was also recently selected as one of the backup targets for the ESA’s Comet Interceptor, so a clearer understanding of BA14's modern properties would not just improve our understanding of how comets go dormant but could also aid in planning for a potential spacecraft visit. We present observations of BA14 during its 2016 Earth close approach taken with the NASA Infrared Telescope Facility on two dates, both of which are consistent with direct observations of its nucleus. The reflectance spectrum of BA14 is similar to 67P/Churyumov–Gerasimenko, albeit highly phase-reddened. Thermal emission contaminates the reflectance spectrum at longer wavelengths, which we correct with a new Markov Chain Monte Carlo thermal modeling code. The models suggest that BA14's visible geometric albedo is p V = 0.01–0.03, consistent with radar observations; its beaming parameter is typical for NEOs observed in its geometry; and its reflectance spectrum is red and linear throughout the H and K bands. It appears very much like a “normal” comet nucleus despite its mid-infrared oddities. A slow loss of fine grains as the object’s activity diminished might help to reconcile some of the lines of evidence, and we discuss other possibilities. A spacecraft flyby past BA14 could get closer to the nucleus than with a more active target, and we highlight some science questions that could be addressed with a visit to a (nearly) dormant comet.
Path Development Network with Finite-dimensional Lie Group Representation

The path signature, a mathematically principled and universal feature of sequential data, leads to a performance boost of deep learning-based models in various sequential data tasks as a complimentary feature. However, it suffers from the curse of dimensionality when the path dimension is high. To tackle this problem, we propose a novel, trainable path development layer, which exploits representations of sequential data with the help of finite-dimensional matrix Lie groups. We also design the backpropagation algorithm of the development layer via an optimisation method on manifolds known as trivialisation. Numerical experiments demonstrate that the path development consistently and significantly outperforms, in terms of accuracy and dimensionality, signature features on several empirical datasets. Moreover, stacking the LSTM with the development layer with a suitable matrix Lie group is empirically proven to alleviate the gradient issues of LSTMs and the resulting hybrid model achieves the state-of-the-art performance.
Computing second-order points under equality constraints: revisiting Fletcher's augmented Lagrangian

We address the problem of minimizing a smooth function under smooth equality constraints. Under regularity assumptions on these constraints, we propose a notion of approximate first- and second-order critical point which relies on the geometric formalism of Riemannian optimization. Using a smooth exact penalty function known as Fletcher's augmented Lagrangian, we propose an algorithm to minimize the penalized cost function which reaches $\varepsilon$-approximate second-order critical points of the original optimization problem in at most $\mathcal{O}(\varepsilon^{-3})$ iterations. This improves on current best theoretical bounds. Along the way, we show new properties of Fletcher's augmented Lagrangian, which may be of independent interest.
Efficient, Uncertainty-based Moderation of Neural Networks Text Classifiers

To maximize the accuracy and increase the overall acceptance of text classifiers, we propose a framework for the efficient, in-operation moderation of classifiers’ output. Our framework focuses on use cases in which F1-scores of modern Neural Networks classifiers (ca. 90%) are still inapplicable in practice. We suggest a semi-automated approach that uses prediction uncertainties to pass unconfident, probably incorrect classifications to human moderators. To minimize the workload, we limit the human moderated data to the point where the accuracy gains saturate and further human effort does not lead to substantial improvements. A series of benchmarking experiments based on three different datasets and three state-of-the-art classifiers show that our framework can improve the classification F1-scores by 5.1 to 11.2% (up to approx. 98 to 99%), while reducing the moderation load up to 73.3% compared to a random moderation.
Learning Neural Acoustic Fields

Our environment is filled with rich and dynamic acoustic information. When we walk into a cathedral, the reverberations as much as appearance inform us of the sanctuary's wide open space. Similarly, as an object moves around us, we expect the sound emitted to also exhibit this movement. While recent advances in learned implicit functions have led to increasingly higher quality representations of the visual world, there have not been commensurate advances in learning spatial auditory representations. To address this gap, we introduce Neural Acoustic Fields (NAFs), an implicit representation that captures how sounds propagate in a physical scene. By modeling acoustic propagation in a scene as a linear time-invariant system, NAFs learn to continuously map all emitter and listener location pairs to a neural impulse response function that can then be applied to arbitrary sounds. We demonstrate that the continuous nature of NAFs enables us to render spatial acoustics for a listener at an arbitrary location, and can predict sound propagation at novel locations. We further show that the representation learned by NAFs can help improve visual learning with sparse views. Finally, we show that a representation informative of scene structure emerges during the learning of NAFs.
Characterizing Urban Lifestyle Signatures Using Motif Properties in Network of Places

The lifestyles of urban dwellers could reveal important insights regarding the dynamics and complexity of cities. The availability of human movement data captured from cell phones enables characterization of distinct and recurrent human daily visitation patterns. Despite growing research on analysis of lifestyle patterns in cities, little is known about the characteristics of people’s lifestyle patterns at urban scale. This limitation is primarily due to challenges in restriction of human movement data to protect the privacy of users. To address this gap, this study constructed networks of places to model cities based on location-based human visitation data. We examined the motifs in the networks of places to map and characterize lifestyle patterns at urban scale. The results show that (1) people’s lifestyles in cities can be well depicted and quantified based on distribution and attributes of motifs in networks of places; (2) motifs show stability in quantity and distance as well as periodicity on weekends and weekdays indicating the stability of lifestyle patterns in cities; (3) networks of places and lifestyle patterns show similarities across different metropolitan areas implying the universality of lifestyle signatures across cities; (4) lifestyles represented by attributed motifs are spatially heterogeneous suggesting variations of lifestyle patterns within different population groups based on where they live in a city. The findings provide deeper insights into urban lifestyle signatures and significant implications for data-informed urban planning and management.
Safe Controller for Output Feedback Linear Systems using Model-Based Reinforcement Learning

The objective of this research is to enable safety-critical systems to simultaneously learn and execute optimal control policies in a safe manner to achieve complex autonomy. Learning optimal policies via trial and error, i.e., traditional reinforcement learning, is difficult to implement in safety-critical systems, particularly when task restarts are unavailable. Safe model-based reinforcement learning techniques based on a barrier transformation have recently been developed to address this problem. However, these methods rely on full state feedback, limiting their usability in a real-world environment. In this work, an output-feedback safe model-based reinforcement learning technique based on a novel barrier-aware dynamic state estimator has been designed to address this issue. The developed approach facilitates simultaneous learning and execution of safe control policies for safety-critical linear systems. Simulation results indicate that barrier transformation is an effective approach to achieve online reinforcement learning in safety-critical systems using output feedback.
Langevin Diffusion: An Almost Universal Algorithm for Private Euclidean (Convex) Optimization

In this paper we revisit the problem of differentially private empirical risk minimization (DP-ERM) and differentially private stochastic convex optimization (DP-SCO). We show that a well-studied continuous time algorithm from statistical physics, called Langevin diffusion (LD), simultaneously provides optimal privacy/utility trade-offs for both DP-ERM and DP-SCO, under ε -DP, and ( ε , δ ) -DP. Using the uniform stability properties of LD, we provide the optimal excess population risk guarantee for (cid:96) 2 -Lipschitz convex losses under ε -DP (even up to log ( n ) factors), thus improving on [ALD21]. Along the way, we provide various technical tools, which can be of independent interest: i) A new R´enyi divergence bound for LD, when run on loss functions over two neighboring data sets, ii) Excess empirical risk bounds for last-iterate LD, analogous to that of Shamir and Zhang [SZ13] for noisy stochastic gradient descent (SGD), and iii) A two phase excess risk analysis of LD, where the ﬁrst phase is when the diffusion has not converged in any reasonable sense to a stationary distribution, and in the second phase when the diffusion has converged to a variant of Gibbs distribution. Our universality results crucially rely on the dynamics of LD. When it has converged to a stationary distribution, we obtain the optimal bounds under ε -DP. When it is run only for a very short time ∝ 1/ p , we obtain the optimal bounds under ( ε , δ ) -DP. Here, p is the dimensionality of the model space. Our work initiates a systematic study of DP continuous time optimization. We believe this may have ramiﬁcations in the design of discrete time DP optimization algorithms analogous to that in the non-private setting, where continuous time dynamical viewpoints have helped in designing new algorithms, including the celebrated mirror-descent (see the discussion in Nemirovski and Yudin (near) universality of LD to an optimal DP-SCO error bounds -DP.
A Modern Theory for High-dimensional Cox Regression Models

: The proportional hazards model has been extensively used in many ﬁelds such as biomedicine to estimate and perform statistical signiﬁcance testing on the eﬀects of covariates inﬂu-encing the survival time of patients. The classical theory of maximum partial-likelihood estimation (MPLE) is used by most software packages to produce inference, e.g., the coxph function in R and the PHREG procedure in SAS. In this paper, we investigate the asymptotic behavior of the MPLE in the regime in which the number of parameters p is of the same order as the number of samples n . The main results are (i) existence of the MPLE undergoes a sharp ‘phase transition’; (ii) the classical MPLE theory leads to invalid inference in the high-dimensional regime. We show that the asymptotic behavior of the MPLE is governed by a new asymptotic theory. These ﬁndings are further corroborated through numerical studies. The main technical tool in our proofs is the Convex Gaussian Min-max Theorem (CGMT), which has not been previously used in the analysis of partial likelihood. Our results thus extend the scope of CGMT and shed new light on the use of CGMT for examining the existence of MPLE and non-separable objective functions.
Safe Backstepping with Control Barrier Functions

Complex control systems are often described in a layered fashion, represented as higher-order systems where the inputs appear after a chain of integrators. While Control Barrier Functions (CBFs) have proven to be powerful tools for safety-critical controller design of nonlinear systems, their application to higher-order systems adds complexity to the controller synthesis process—it necessitates dynamically extending the CBF to include higher order terms, which consequently modifies the safe set in complex ways. We propose an alternative approach for addressing safety of higher-order systems through Control Barrier Function Backstepping. Drawing inspiration from the method of Lyapunov backstep-ping, we provide a constructive framework for synthesizing safety-critical controllers and CBFs for higher-order systems from a top-level dynamics safety specification and controller design. Furthermore, we integrate the proposed method with Lyapunov backstepping, allowing the tasks of stability and safety to be expressed individually but achieved jointly. We demonstrate the efficacy of this approach in simulation.
Hierarchical Reinforcement Learning under Mixed Observability

The framework of mixed observable Markov decision processes (MOMDP) models many robotic domains in which some state variables are fully observable while others are not. In this work, we identify a significant subclass of MOMDPs defined by how actions influence the fully observable components of the state and how those, in turn, influence the partially observable components and the rewards. This unique property allows for a two-level hierarchical approach we call HIerarchical Reinforcement Learning under Mixed Observability (HILMO), which restricts partial observability to the top level while the bottom level remains fully observable, enabling higher learning efficiency. The top level produces desired goals to be reached by the bottom level until the task is solved. We further develop theoretical guarantees to show that our approach can achieve optimal and quasi-optimal behavior under mild assumptions. Empirical results on long-horizon continuous control tasks demonstrate the efficacy and efficiency of our approach in terms of improved success rate, sample efficiency, and wall-clock training time. We also deploy policies learned in simulation on a real robot.