Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantum formalism for cognitive psychology

The cognitive state of mind concerning a range of choices to be made can effectively be modelled in terms of an element of a high-dimensional Hilbert space. The dynamics of the state of mind resulting form information acquisition is characterised by the von Neumann-L\"uders projection postulate of quantum theory. This is shown to give rise to an uncertainty-minimising dynamical behaviour equivalent to the Bayesian updating, hence providing an alternative approach to characterising the dynamics of cognitive state that is consistent with the free energy principle in brain science. The quantum formalism however goes beyond the range of applicability of classical reasoning in explaining cognitive behaviours, thus opens up new and intriguing possibilities.
Analyzing covariate clustering effects in healthcare cost subgroups: insights and applications for prediction

Healthcare cost prediction is a challenging task due to the high-dimensionality and high correlation among covariates. Additionally, the skewed, heavy-tailed, and often multi-modal nature of cost data can complicate matters further due to unobserved heterogeneity. In this study, we propose a novel framework for finite mixture regression models that incorporates covariate clustering methods to better account for the effects of clustered covariates on subgroups of the outcome, which enables a more accurate characterization of the complex distribution of the data. The proposed framework can be formulated as a convex optimization problem with an additional penalty term based on the prior similarity of the covariates. To efficiently solve this optimization problem, a specialized EM-ADMM algorithm is proposed that integrates the alternating direction multiplicative method (ADMM) into the iterative process of the expectation-maximizing (EM) algorithm. The convergence of the algorithm and the efficiency of the covariate clustering method are verified using simulation data, and the superiority of the approach over traditional regression techniques is demonstrated using two real Chinese medical expenditure datasets. Our empirical results provide valuable insights into the complex network graph of the covariates and can inform business practices, such as the design and pricing of medical insurance products.
Clustering with minimum spanning trees: How good can it be?

Minimum spanning trees (MSTs) provide a convenient representation of datasets in numerous pattern recognition activities. Moreover, they are relatively fast to compute. In this paper, we quantify the extent to which they can be meaningful in partitional data clustering tasks in low-dimensional spaces. By identifying the upper bounds for the agreement between the best (oracle) algorithm and the expert labels from a large battery of benchmark data, we discover that MST methods are overall very competitive. Next, instead of proposing yet another algorithm that performs well on a limited set of examples, we review, study, extend, and generalise existing, state-of-the-art MST-based partitioning schemes. This leads to a few new and noteworthy approaches. Overall, Genie and the information-theoretic methods often outperform the non-MST algorithms such as k-means, Gaussian mixtures, spectral clustering, Birch, density-based, and classical hierarchical agglomerative procedures. Nevertheless, we identify that there is still some room for improvement, and thus the development of novel algorithms is encouraged.
Towards better traffic volume estimation: Tackling both underdetermined and non-equilibrium problems via a correlation-adaptive graph convolution network

Trafﬁc volume is an indispensable ingredient to provide ﬁne-grained information for trafﬁc management and control. However, due to limited deployment of trafﬁc sensors, obtaining full-scale volume information is far from easy. Existing works on this topic primarily focus on improving the overall estimation accuracy of a particular method and ignore the underlying challenges of volume estimation, thereby having inferior performances on some critical tasks. This paper studies two key problems with regard to trafﬁc volume estimation: (1) underdetermined trafﬁc ﬂows caused by fully undetected paths that can allow arbitrary volume values without violating the conservation law, where using local side information is insufﬁcient to tackle, and (2) non-equilibrium trafﬁc ﬂows arise when trafﬁc ﬂow vary in density over space and time due to congestion propagation delay, which produce time-shifted volume readings to varying degrees. Here we demonstrate a graph-based deep learning method that offers a data-driven, equation-free and correlation-adaptive approach to tackle the above issues and perform accurate network-wide trafﬁc volume estimation. Particularly, in order to quantify the dynamic and nonlinear speed-volume relationships for the estimation of underdetermined ﬂows, a speed pattern-adaptive adjacent matrix based on graph attention is developed and integrated into the graph convolution process, to capture non-local correlations between volumes. To measure the impacts of non-equilibrium ﬂows, a temporal masked and clipped attention combined with a gated temporal convolution layer is customized to capture time-asynchronous correlations between upstream and downstream sensors under varying impacts of congestion delay. We then evaluate our model on a real-world highway trafﬁc volume dataset and compare it with several benchmark models. It is demonstrated that the proposed model achieves high estimation accuracy even under 20% sensor coverage rate and outperforms other baselines signiﬁcantly, especially on underdetermined and non-equilibrium ﬂow locations. Furthermore, comprehensive quantitative model analysis are also carried out to justify the model designs.
Exploring Recurrent Long-term Temporal Fusion for Multi-view 3D Perception

Long-term temporal fusion is a crucial but often overlooked technique in camera-based Bird's-Eye-View (BEV) 3D perception. Existing methods are mostly in a parallel manner. While parallel fusion can benefit from long-term information, it suffers from increasing computational and memory overheads as the fusion window size grows. Alternatively, BEVFormer adopts a recurrent fusion pipeline so that history information can be efficiently integrated, yet it fails to benefit from longer temporal frames. In this paper, we explore an embarrassingly simple long-term recurrent fusion strategy built upon the LSS-based methods and find it already able to enjoy the merits from both sides, i.e., rich long-term information and efficient fusion pipeline. A temporal embedding module is further proposed to improve the model's robustness against occasionally missed frames in practical scenarios. We name this simple but effective fusing pipeline VideoBEV. Experimental results on the nuScenes benchmark show that VideoBEV obtains leading performance on various camera-based 3D perception tasks, including object detection (55.4% mAP and 62.9% NDS), segmentation (48.6% vehicle mIoU), tracking (54.8% AMOTA), and motion prediction (0.80m minADE and 0.463 EPA). Code will be available.
Eringen’s model via linearization of nonlocal hyperelasticity

We consider Riesz’ fractional gradient and a truncated version of it. The equations of nonlocal nonlinear elasticity based on those gradients are known. We perform a formal linearization and arrive at the equations of linear elasticity based on those nonlocal operators. We prove the existence of solutions of the linear equations, notably, by a nonlocal version of Korn’s inequality. Finally, we show that the linearizations obtained are particular cases of Eringen’s model with singular kernels.
Quantum Acoustics Spawns Planckian Resistivity

Strange metals exhibit universal linear-in-temperature resistivity described by a Planckian scattering rate, the origin of which remains elusive. By employing a novel approach inspired by quantum optics, we arrive at the coherent state limit of lattice vibrations: quantum acoustics. Utilizing this nonperturbative framework, we demonstrate that lattice vibrations are active drivers in the Planckian resistivity phenomenon, challenging prevailing theories. Treating charge carriers as quantum wave packets negotiating the dynamic acoustic field, competition ensues between localization and delocalization, settling on the conjectured universal quantum bound of diffusion and capturing the enigmatic $T$-linear resistivity over hundreds of degrees. Our research not only elucidates the critical role of phonons in Planckian resistivity but also redefines their significance in the broader landscape of high-temperature superconductivity and condensed matter physics.
Sufficient Control of Complex Networks

In this paper, we propose to study on sufficient control of complex networks which is to control a sufficiently large portion of the network, where only the quantity of controllable nodes matters. To the best of our knowledge, this is the first time that such a problem is investigated. We prove that the sufficient controllability problem can be converted into a minimum cost flow problem, for which an algorithm can be easily devised with polynomial complexity. Further, we study the problem of minimum-cost sufficient control, which is to drive a sufficiently large subset of the network nodes to any predefined state with the minimum cost using a given number of controllers. It is proved that the problem is NP-hard. We propose an ``extended $L_{\mathrm{0}}$-norm-constraint-based Projected Gradient Method"(eLPGM) algorithm which may achieve suboptimal solutions for the problems at small or medium sizes. To tackle the large-scale problems, we propose to convert the control problem into a graph algorithm problem, and devise an efficient low-complexity ``Evenly Divided Control Paths"(EDCP) algorithm to tackle the graph problem. Simulation results on both synthetic and real-life networks are provided, demonstrating the satisfactory performance of the proposed methods.
On the Fusion Strategies for Federated Decision Making

We consider the problem of information aggregation in federated decision making, where a group of agents collaborate to infer the underlying state of nature without sharing their private data with the central processor or each other. We analyze the non-Bayesian social learning strategy in which agents incorporate their individual observations into their opinions (i.e., soft-decisions) with Bayes rule, and the central processor aggregates these opinions by arithmetic or geometric averaging. Building on our previous work, we establish that both pooling strategies result in asymptotic normality characterization of the system, which, for instance, can be utilized to derive approximate expressions for the error probability. We verify the theoretical findings with simulations and compare both strategies.
Improving Text-Audio Retrieval by Text-Aware Attention Pooling and Prior Matrix Revised Loss

In text-audio retrieval (TAR) tasks, due to the heterogeneity of contents between text and audio, the semantic information contained in the text is only similar to certain frames within the audio. Yet, existing works aggregate the entire audio without considering the text, such as mean-pooling over the frames, which is likely to encode misleading audio information not described in the given text. In this paper, we present a text-aware attention pooling (TAP) module for TAR, which is essentially a scaled dot product attention for a text to attend to its most semantically similar frames. Furthermore, previous methods only conduct the softmax for every single-side retrieval, ignoring the potential cross-retrieval information. By exploring the intrinsic prior of each text-audio pair, we introduce a prior matrix revised (PMR) loss to filter the hard case with high (or low) text-to-audio but low (or high) audio-to-text similarity scores, thus achieving the dual optimal match. Experiments show that our TAP significantly outperforms various text-agnostic pooling functions. Moreover, our PMR loss also shows stable performance gains on multiple datasets.
Confirmation of Color-dependent Centroid Shift Measured After 1.8 Years with HST

We measured the precise masses of the host and planet in the OGLE-2003-BLG-235 system, when the lens and source were resolving, with 2018 Keck high resolution images. This measurement is in agreement with the observation taken in 2005 with the Hubble Space Telescope (HST). In the 2005 data, the lens and sources were not resolved and the measurement was made using color-dependent centroid shift only. The Nancy Grace Roman Space Telescope will measure masses using data typically taken within 3–4 yr of the peak of the event, which is a much shorter baseline when compared to most of the mass measurements to date. Hence, the color-dependent centroid shift will be one of the primary methods of mass measurements for the Roman telescope. Yet, mass measurements of only two events (OGLE-2003-BLG-235 and OGLE-2005-BLG-071) have been done using the color-dependent centroid shift method so far. The accuracy of the measurements using this method are neither completely known nor well studied. The agreement of the Keck and HST results, as shown in this paper, is very important because this agreement confirms the accuracy of the mass measurements determined at a small lens-source separation using the color-dependent centroid shift method. It also shows that with >100 high resolution images, the Roman telescope will be able to use color-dependent centroid shift at a 3–4 yr time baseline and produce mass measurements. We find that OGLE-2003-BLG-235 is a planetary system that consists of a 2.34 ± 0.43M Jup planet orbiting a 0.56 ± 0.06M ⊙ K-dwarf host star at a distance of 5.26 ± 0.71 kpc from the Sun.
Generalization analysis of an unfolding network for analysis-based Compressed Sensing

Unfolding networks have shown promising results in the Compressed Sensing (CS) field. Yet, the investigation of their generalization ability is still in its infancy. In this paper, we perform generalization analysis of a state-of-the-art ADMM-based unfolding network, which jointly learns a decoder for CS and a sparsifying redundant analysis operator. To this end, we first impose a structural constraint on the learnable sparsifier, which parametrizes the network's hypothesis class. For the latter, we estimate its Rademacher complexity. With this estimate in hand, we deliver generalization error bounds for the examined network. Finally, the validity of our theory is assessed and numerical comparisons to a state-of-the-art unfolding network are made, on synthetic and real-world datasets. Our experimental results demonstrate that our proposed framework complies with our theoretical findings and outperforms the baseline, consistently for all datasets.
Statistical Frameworks for Oncology Dose-Finding Designs with Late-Onset Toxicities: A Review

In oncology dose-finding trials, due to staggered enrollment, it might be desirable to make dose-assignment decisions in real-time in the presence of pending toxicity outcomes, for example, when the dose-limiting toxicity is late-onset. Patients' time-to-event information may be utilized to facilitate such decisions. We review statistical frameworks for time-to-event modeling in dose-finding trials and summarize existing designs into two classes: TITE designs and POD designs. TITE designs are based on inference on toxicity probabilities, while POD designs are based on inference on dose-finding decisions. These two classes of designs contain existing individual designs as special cases and also give rise to new designs. We discuss and study the theoretical properties of these designs, including large-sample convergence properties, coherence principles, and the underlying decision rules. To facilitate the use of these designs in practice, we introduce efficient computational algorithms and review common practical considerations, such as safety rules and suspension rules. Finally, the operating characteristics of several designs are evaluated and compared through computer simulations.
Maximal Objectives in the Multiarmed Bandit with Applications

In several applications of the stochastic multiarmed bandit problem, the traditional objective of maximizing the expected total reward can be inappropriate. In this paper, we study a new objective in the classic setup. Given K arms, instead of maximizing the expected total reward from T pulls (the traditional “sum” objective), we consider the vector of total rewards earned from each of the K arms at the end of T pulls and aim to maximize the expected highest total reward across arms (the “max” objective). For this objective, we show that any policy must incur an instance-dependent asymptotic regret of [Formula: see text] (with a higher instance-dependent constant compared with the traditional objective) and a worst case regret of [Formula: see text]. We then design an adaptive explore-then-commit policy featuring exploration based on appropriately tuned confidence bounds on the mean reward and an adaptive stopping criterion, which adapts to the problem difficulty and simultaneously achieves these bounds (up to logarithmic factors). We then generalize our algorithmic insights to the problem of maximizing the expected value of the average total reward of the top m arms with the highest total rewards. Our numerical experiments demonstrate the efficacy of our policies compared with several natural alternatives in practical parameter regimes. We discuss applications of these new objectives to the problem of conditioning an adequate supply of value-providing market entities (workers/sellers/service providers) in online platforms and marketplaces. This paper was accepted by Vivek Farias, data science. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022. 00801 .
Complexity in the scaling of velocity fluctuations in the high-latitude F-region ionosphere

The temporal scaling properties of F-region velocity fluctuations, δ v los , were characterised over 17 octaves of temporal scale from τ=1 s to v los would collapse onto the same PDF using the scaling P s (δ v s , τ)=τ α P (δ v los , τ) and δ v s =δ v los τ −α where α is the scaling exponent. The variations in scaling exponents α and multi-fractal behaviour were estimated using peak scaling and generalised structure function (GSF) analyses, and a new method based upon minimising the differences between re-scaled probability density functions (PDFs). The efficiency of this method enabled calculation of "α spectra", the temporal spectra of scaling exponents from τ=1 s to ~2048 s. The large number of samples enabled calculation of α spectra for data separated into 2-h bins of MLT as well as two main physical regimes: Population A echoes with Doppler spectral width −1 concentrated on closed field lines, and Population B echoes with spectral width >150 m s −1 concentrated on open field lines. For all data there was a scaling break at τ~10 s and the similarity of the fluctuations beneath this scale may be related to the large spatial averaging (~100 km×45 km) employed by SuperDARN radars. For Tasmania Population B, the velocity fluctuations exhibited approximately mono fractal power law scaling between τ~8 s and 2048 s (34 min), and probably up to several hours. The scaling exponents were generally less than that expected for basic MHD turbulence (α=0.25), except close to magnetic dusk where they peaked towards the basic MHD value. For Population A, the scaling exponents were larger than for Population B, having values generally in the range expected for basic MHD and Kolmogorov turbulence (α=0.25–0.33). The α spectra exhibited complicated variations with MLT and τ which must be related to different physical processes exerting more or less influence.
On the velocity fields of elliptical galaxies

A family of self-consistent maximum entropy dynamical models is presented for the triaxial 'perfect ellipsoids'. These models are projected in different viewing geometries to explore the possible morphologies of the 2D radial-velocity fields of elliptical galaxies. It is found that, typically, about half the time an 'ordinary' velocity field is seen with a mixture of major- and minor-axis rotation. For other lines of sight, the velocity fields are more complicated, showing multiple peaks, steep gradients, strongly twisted contours, or kinematically distinct or counter-rotating cores. The origin of each of these velocity field morphologies is explained in terms of competing contributions from the major families of tube orbits.
A Two-Phase Combined Gradient-Tunneling Based Algorithm for Constrained Integer Programming Problems

Abstract We propose a gradient-based optimization procedure consisting of two distinct phases for locating the global optimum of integer problems with a linear or non-linear objective function subject to linear or non-linear constraints. In the first phase a local minimum of the function is found using the gradient approach coupled with hemstitching moves when a constraint is violated in order to return the search to a feasible region. In the second phase a tunneling function is constructed at this local minimum in order to find another point on the other side of the barrier where the function value is approximately the same. In the next cycle the search for the global optimum commences again using this new found point as the starting state. The search continues until no improvement in function the result is found. Results from the proposed optimization method are presented and compared with those from the gradient method involving 3n enumeration proposed earlier, as well as with other published results.
Cosmography of OB stars in the solar neighbourhood

We construct a 3D map of the spatial density of OB stars within 500 pc from the Sun using the Hipparcos catalogue and find three large-scale stream-like structures that allow a new view on the solar neighbourhood. The spatial coherence of these blue streams and the monotonic age sequence over hundreds of parsecs suggest that they are made of young stars, similar to the young streams that are conspicuous in nearby spiral galaxies. The three streams are 1) the Scorpius to Canis Majoris stream, covering 350 pc and 65 Myr of star formation history; 2) the Vela stream, encompassing at least 150 pc and 25 Myr of star formation history; and 3) the Orion stream, including not only the well-known Orion OB1abcd associations, but also a large previously unreported foreground stellar group lying only 200 pc from the Sun. The map also reveals a remarkable and previously unknown nearby OB association, between the Orion stream and the Taurus molecular clouds, which might be responsible for the observed structure and star formation activity in this cloud complex. This new association also appears to be the birthplace of Betelgeuse, as indicated by the proximity and velocity of the red giant. If this is confirmed, it would solve the long-standing puzzle of the origin of Betelgeuse. The well-known nearby star-forming low-mass clouds, including the nearby T and R associations Lupus, Cha, Oph, CrA, Taurus, Vela R1, and various low-mass cometary clouds in Vela and Orion, appear in this new view of the local neighbourhood to be secondary star formation episodes that most likely were triggered by the feedback from the massive stars in the streams. We also recover well-known star clusters of various ages that are currently cruising through the solar neighbourhood. Finally, we find no evidence of an elliptical structure such as the Gould belt, a structure we suggest is a 2D projection effect, and not a physical ring.