Miracle-dz/UniFAR-FTUs huggingface dataset:
Sampling Approximately Low-Rank Ising Models: MCMC meets Variational Methods

We consider Ising models on the hypercube with a general interaction matrix $J$, and give a polynomial time sampling algorithm when all but $O(1)$ eigenvalues of $J$ lie in an interval of length one, a situation which occurs in many models of interest. This was previously known for the Glauber dynamics when *all* eigenvalues fit in an interval of length one; however, a single outlier can force the Glauber dynamics to mix torpidly. Our general result implies the first polynomial time sampling algorithms for low-rank Ising models such as Hopfield networks with a fixed number of patterns and Bayesian clustering models with low-dimensional contexts, and greatly improves the polynomial time sampling regime for the antiferromagnetic/ferromagnetic Ising model with inconsistent field on expander graphs. It also improves on previous approximation algorithm results based on the naive mean-field approximation in variational methods and statistical physics. Our approach is based on a new fusion of ideas from the MCMC and variational inference worlds. As part of our algorithm, we define a new nonconvex variational problem which allows us to sample from an exponential reweighting of a distribution by a negative definite quadratic form, and show how to make this procedure provably efficient using stochastic gradient descent. On top of this, we construct a new simulated tempering chain (on an extended state space arising from the Hubbard-Stratonovich transform) which overcomes the obstacle posed by large positive eigenvalues, and combine it with the SGD-based sampler to solve the full problem.
Enhanced Behavioral Cloning with Environmental Losses for Self-Driving Vehicles

Learned path planners have attracted research interest due to their ability to model human driving behavior and rapid inference. Recent works on behavioral cloning show that simple imitation of expert observations is not sufficient to handle complex driving scenarios. Besides, predictions that land outside drivable areas can lead to potentially dangerous situations. This paper proposes a set of loss functions, namely Social loss and Road loss, which account for modelling risky social interactions in path planning. These losses act as a repulsive scalar field that surrounds non-drivable areas. Predictions that land near these regions incur in a higher training cost, which is minimized using backpropagation. This methodology provides additional environment feedback to the traditional supervised learning set up. We validated this approach on a large-scale urban driving dataset. The results show the agent learns to imitate human driving while exhibiting better safety metrics. Furthermore, the proposed methodology has positive effects on inference without the need to artificially generate unsafe driving examples. The explanability study suggests that the benefits obtained are associated with a higher relevance of non-drivable areas in the agent's decisions compared to classical behavioral cloning.
BADDr: Bayes-Adaptive Deep Dropout RL for POMDPs

While reinforcement learning (RL) has made great advances in scalability, exploration and partial observability are still active research topics. In contrast, Bayesian RL (BRL) provides a principled answer to both state estimation and the exploration-exploitation trade-off, but struggles to scale. To tackle this challenge, BRL frameworks with various prior assumptions have been proposed, with varied success. This work presents a representation-agnostic formulation of BRL under partially observability, unifying the previous models under one theoretical umbrella. To demonstrate its practical significance we also propose a novel derivation, Bayes-Adaptive Deep Dropout rl (BADDr), based on dropout networks. Under this parameterization, in contrast to previous work, the belief over the state and dynamics is a more scalable inference problem. We choose actions through Monte-Carlo tree search and empirically show that our method is competitive with state-of-the-art BRL methods on small domains while being able to solve much larger ones.
Identification, mass and age of primary red clump stars from spectral features derived with the LAMOST DR7

Although red clump (RC) stars are easy to identify due to their stability of luminosity and color, about 20-50% are actually red giant branch (RGB) stars in the same location on the HR diagram. In this paper, a sample of 210,504 spectra for 184,318 primary RC (PRC) stars from the LAMOST DR7 is identified, which has a purity of higher than 90 percent. The RC and the RGB stars are successfully distinguished through LAMOST spectra(R∼1800 and SNR>10) by adopting the XGBoost ensemble learning algorithm, and the secondary RC stars are also removed. The SHapley Additive exPlanations (SHAP) value is used to explain the top features that the XGBoost model selected. The features are around Fe5270, MgH & Mg Ib, Fe4957, Fe4207, Cr5208, and CN, which can successfully distinguish RGB and RC stars. The XGBoost is also used to estimate the ages and masses of PRC stars by training their spectra with Kepler labeled asteroseismic parameters. The uncertainties of mass and age are 13 and 31 percent, respectively. Verifying the feature attribution model, we find the age-sensitive elements XGBoost gets are consistent with the literature. Distances of the PRC stars are derived by KS absolute magnitude calibrated by Gaia EDR3, which has an uncertainty of about 6 percent and shows the stars mainly locate at the Galactic disk. We also test the XGBoost with R∼250, which is the resolution of the Chinese Space Station Telescope(CSST) under construction, it is still capable of finding sensitive features to distinguish RC and RGB.
R2-D2: Repetitive Reprediction Deep Decipher for Semi-Supervised Deep Learning

Most recent semi-supervised deep learning (deep SSL) methods used a similar paradigm: use network predictions to update pseudo-labels and use pseudo-labels to update network parameters iteratively. However, they lack theoretical support and cannot explain why predictions are good candidates for pseudo-labels in the deep learning paradigm. In this paper, we propose a principled end-to-end framework named deep decipher (D2) for SSL. Within the D2 framework, we prove that pseudo-labels are related to network predictions by an exponential link function, which gives a theoretical support for using predictions as pseudo-labels. Furthermore, we demonstrate that updating pseudo-labels by network predictions will make them uncertain. To mitigate this problem, we propose a training strategy called repetitive reprediction (R2). Finally, the proposed R2-D2 method is tested on the large-scale ImageNet dataset and outperforms state-of-the-art methods by 5 percentage points.
Electrical charge control of h-BN single photon sources

Colour centres of hexagonal boron nitride (h-BN) have been discovered as promising and practical single photon sources due to their high brightness and narrow spectral linewidth at room-temperature. In order to realize h-BN based photonic quantum communications, the ability to electrically activate the single photon fluorescence using an external electric field is crucial. In this work, we show the electrical switching of the photoluminescence from h-BN quantum emitters, enabled by the controllable electron transfer from the nearby charge reservoir. By tuning the Fermi level of graphene next to the h-BN defects, we observed luminescence brightening of quantum emitters upon the application of a voltage due to the direct charge state manipulation. In addition, the correlation measurement of the single photon sources with the graphene’s Raman spectroscopy allows us to extract the exact charge transition level of quantum emitters, providing the information on the crystallographic nature of the defect structure. With the complete on-off switching of emission intensity of h-BN quantum emitters using a voltage, our result paves the way for the van der Waals colour centre based photonic quantum information processing, cryptography and memory applications.
The irreducible mass of LIGO’s black holes

The mass of a Kerr black hole can be separated into irreducible and rotational components —the former is a lower limit to the energy that cannot be possibly extracted from the event horizon and is related to its area. Here we compute the irreducible masses of the stellar-mass black holes observed by gravitational-wave interferometers LIGO and Virgo. Using single-event data, we present a re-parametrization of the posterior distribution that explicitly highlights the irreducible and rotational contributions to the total energy. We exploit the area law to rank the black-hole mergers observed to date according to their irreversibility. Using population ﬁts, we compute the rate by which the total area of black-hole horizons increases due to the observable mergers.
On the Application of Differential Evolution to the Analysis of X-Ray Spectra

Using methods of differential evolution (DE), we determined the coronal elemental abundances and the differential emission measure (DEM) distributions for the plasma flaring on 2003 January 21. The analyses have been made based on RESIK X-ray spectra. DE belongs to the family of evolutionary algorithms. DE is conceptually simple and easy to implement, so it has been applied to solve many problems in science and engineering. In this study we apply this method in a new context: simultaneous determination of plasma composition and DEM. In order to increase the confidence of the results obtained using DE, we tested the use of its algorithms by comparing the DE synthesized with respective spectra observed by RESIK. Extensive discussion of the DE method used and the obtained physical characteristics of flaring plasma is presented.
Effective Urban Region Representation Learning Using Heterogeneous Urban Graph Attention Network (HUGAT)

Revealing the hidden patterns shaping the urban environment is essential to understand its dynamics and to make cities smarter. Recent studies have demonstrated that learning the representations of urban regions can be an effective strategy to uncover the intrinsic characteristics of urban areas. However, existing studies lack in incorporating diversity in urban data sources. In this work, we propose heterogeneous urban graph attention network (HUGAT), which incorporates heterogeneity of diverse urban datasets. In HUGAT, heterogeneous urban graph (HUG) incorporates both the geo-spatial and temporal people movement variations in a single graph structure. Given a HUG, a set of meta-paths are designed to capture the rich urban semantics as composite relations between nodes. Region embedding is carried out using heterogeneous graph attention network (HAN). HUGAT is designed to consider multiple learning objectives of city's geo-spatial and mobility variations simultaneously. In our extensive experiments on NYC data, HUGAT outperformed all the state-of-the-art models. Moreover, it demonstrated a robust generalization capability across the various prediction tasks of crime, average personal income, and bike flow as well as the spatial clustering task.
Fast online inference for nonlinear contextual bandit based on Generative Adversarial Network

This work addresses the efficiency concern on inferring a nonlinear contextual bandit when the number of arms $n$ is very large. We propose a neural bandit model with an end-to-end training process to efficiently perform bandit algorithms such as Thompson Sampling and UCB during inference. We advance state-of-the-art time complexity to $O(\log n)$ with approximate Bayesian inference, neural random feature mapping, approximate global maxima and approximate nearest neighbor search. We further propose a generative adversarial network to shift the bottleneck of maximizing the objective for selecting optimal arms from inference time to training time, enjoying significant speedup with additional advantage of enabling batch and parallel processing. %The generative model can inference an approximate argmax of the posterior sampling in logarithmic time complexity with the help of approximate nearest neighbor search. Extensive experiments on classification and recommendation tasks demonstrate order-of-magnitude improvement in inference time no significant degradation on the performance.
On tempered fractional calculus with respect to functions and the associated fractional differential equations

The prime aim of the present paper is to continue developing the theory of tempered fractional integrals and derivatives of a function with respect to another function. This theory combines the tempered fractional calculus with the Ψ$$ \Psi $$ ‐fractional calculus, both of which have found applications in topics including continuous time random walks. After studying the basic theory of the Ψ$$ \Psi $$ ‐tempered operators, we prove mean value theorems and Taylor's theorems for both Riemann–Liouville‐type and Caputo‐type cases of these operators. Furthermore, we study some non‐linear fractional differential equations involving Ψ$$ \Psi $$ ‐tempered derivatives, proving existence‐uniqueness theorems by using the Banach contraction principle and proving stability results by using Grönwall type inequalities.
Solving Dynamic Principal-Agent Problems with a Rationally Inattentive Principal

Principal-Agent (PA) problems describe a broad class of economic relationships characterized by misaligned incentives and asymmetric information. The Principal's problem is to find optimal incentives given the available information, e.g., a manager setting optimal wages for its employees. Whereas the Principal is often assumed rational, comparatively little is known about solutions when the Principal is boundedly rational, especially in the sequential setting, with multiple Agents, and with multiple information channels. Here, we develop RIRL, a deep reinforcement learning framework that solves such complex PA problems with a rationally inattentive Principal. Such a Principal incurs a cost for paying attention to information, which can model forms of bounded rationality. We use RIRL to analyze rich economic phenomena in manager-employee relationships. In the single-step setting, 1) RIRL yields wages that are consistent with theoretical predictions; and 2) non-zero attention costs lead to simpler but less profitable wage structures, and increased Agent welfare. In a sequential setting with multiple Agents, RIRL shows opposing consequences of the Principal's inattention to different information channels: 1) inattention to Agents' outputs closes wage gaps based on ability differences; and 2) inattention to Agents' efforts induces a social dilemma dynamic in which Agents work harder, but essentially for free. Moreover, RIRL reveals non-trivial relationships between the Principal's inattention and Agent types, e.g., if Agents are prone to sub-optimal effort choices, payment schedules are more sensitive to the Principal's attention cost. As such, RIRL can reveal novel economic relationships and enables progress towards understanding the effects of bounded rationality in dynamic settings.
Dynamic Precision Adjusting Shipboard Radar

The dynamic precision adjusting shipboard radar based on differential GPS is proposed in this paper,algorithm of conversion GPS-80 coordinate into distance and bearing of target relative with shipboard radar is given.One data processing method for synchronous measurement between shipboard search radar and tracking radar or dynamic precision adjusting suitable for the sensors on the maritime movable platform is designed in this paper.Experiments on dynamic precision adjusting show that method had features of high accuracy and real-time.
Temporal variation may have diverse impacts on range limits

Environmental fluctuations are pervasive in nature, but the influence of non-directional temporal variation on range limits has received scant attention. We synthesize insights from the literature and use simple models to make conceptual points about the potentially wide range of ecological and evolutionary effects of temporal variation on range limits. Because organisms respond nonlinearly to environmental conditions, temporal variation can directionally alter long-term growth rates, either to shrink or to expand ranges. We illustrate this diversity of outcomes with a model of competition along a mortality gradient. Temporal variation can permit transitions between alternative states, potentially facilitating range expansion. We show this for variation in dispersal, using simple source–sink population models (with strong Allee effects, or with gene flow hampering local adaptation). Temporal variation enhances extinction risk owing to demographic stochasticity, rare events, and loss of genetic variation, all tending to shrink ranges. However, specific adaptations to exploit variation (including dispersal) may permit larger ranges than in similar but constant environments. Grappling with temporal variation is essential both to understand eco-evolutionary dynamics at range limits and to guide conservation and management strategies. This article is part of the theme issue ‘Species’ ranges in the face of changing environments (Part II)’.
Environmental variation and biotic interactions limit adaptation at ecological margins: lessons from rainforest Drosophila and European butterflies

Models of local adaptation to spatially varying selection predict that maximum rates of evolution are determined by the interaction between increased adaptive potential owing to increased genetic variation, and the cost genetic variation brings by reducing population fitness. We discuss existing and new results from our laboratory assays and field transplants of rainforest Drosophila and UK butterflies along environmental gradients, which try to test these predictions in natural populations. Our data suggest that: (i) local adaptation along ecological gradients is not consistently observed in time and space, especially where biotic and abiotic interactions affect both gradient steepness and genetic variation in fitness; (ii) genetic variation in fitness observed in the laboratory is only sometimes visible to selection in the field, suggesting that demographic costs can remain high without increasing adaptive potential; and (iii) antagonistic interactions between species reduce local productivity, especially at ecological margins. Such antagonistic interactions steepen gradients and may increase the cost of adaptation by increasing its dimensionality. However, where biotic interactions do evolve, rapid range expansion can follow. Future research should test how the environmental sensitivity of genotypes determines their ecological exposure, and its effects on genetic variation in fitness, to predict the probability of evolutionary rescue at ecological margins. This article is part of the theme issue ‘Species’ ranges in the face of changing environments (Part II)’.
Space to Think : Lessons and Impact of the IVY Project

The Interventions for Vulnerable Youth (IVY) service was developed and commenced in September 2013, aiming to fill the gap in service provisions for high risk young people. It is a nationwide service designed to meet the needs of a much marginalised and challenging group of young people. The project is funded by the Scottish Government and based in the Centre for Youth and Criminal Justice at the University of Strathclyde. Two years on from the birth of the project, this service evaluation was carried out by the Centre for Youth and Criminal Justice to measure where the project is now and where it will go in the future.
Algorithmic Identification of Tone Color: A Comparison of Algorithmic Identification and Identification by Survey Respondents

Tone color is an important timbral descriptor for sound that is commonly referred to using semantic descriptors such as warm, dark and bright. This article introduces a novel algorithm using hidden Markov models to computationally identify a best fit semantic descriptor of tone color for a given sound file. The algorithm is developed using dataset annotated by trained individuals and tested against thousands of sound samples obtained from an online database. A survey is conducted to alongside the algorithm development in order to compare identification results from the algorithm against identification results by survey respondents. Results from the algorithm shows positive match in general suggesting its viability. Algorithmic identification of tone color finds its application in music production, where it can be used as a parameter recommendation tool for synthesizers.
Automated information extraction from free‐text medical documents for stroke key performance indicators: a pilot study

Automated information extraction might be able to assist with the collection of stroke key performance indicators (KPI). The feasibility of using natural language processing for classification‐based KPI and datetime field extraction was assessed. Using free‐text discharge summaries, random forest models achieved high levels of performance in classification tasks (area under the receiver operator curve 0.95–1.00). The datetime field extraction method was successful in 29 of 43 (67.4%) cases. Further studies are indicated.
Design of a versatile microfluidic device for imaging precision-cut-tissue slices

Precision-cut-tissues (PCTs), which preserve many aspects of a tissue’s microenvironment, are typically imaged using conventional sample dishes and chambers. These can require large amounts of reagent and, when used for flow-through experiments, the shear forces applied on the tissues are often ill-defined. Their physical design also makes it difficult to image large volumes and repetitively image smaller regions of interest in the living slice. We report here on the design of a versatile microfluidic device capable of holding mouse or human pancreas PCTs for 3D fluorescence imaging using confocal and selective plane illumination microscopy (SPIM). Our design positions PCTs within a 5 × 5 mm × 140 µm deep chamber fitted with 150 µm tall channels to facilitate media exchange. Shear stress in the device is localized to small regions on the surface of the tissue and can be easily controlled. This design allows for media exchange at flowrates ∼10-fold lower than those required for conventional chambers. Finally, this design allows for imaging the same immunofluorescently labeled PCT with high resolution on a confocal and with large field of view on a SPIM, without adversely affecting image quality.
Introduction to Communication Electronic Warfare Systems

Focusing mainly on the engineering aspects of communications electronic warfare (EW) systems, this thoroughly updated and revised edition of a popular Artech House book offers a current and complete introduction to the subject. The second edition adds a wealth of new material, including expanded treatments of two critical areas - RF noise and the effects of signal fading. Extensively supported with over 700 equations, this authoritative resource details the components, systems, and operations of electronic warfare systems dedicated to protecting and attacking military communications networks. The book offers a complete understanding of how modern direction finders for communication signals work, along with their limitations. It shows how to measure performance and define the basic operations necessary for communication EW systems. Professionals learn how to identify state-of-the-art EW technology and gain a solid understanding of basic EW processes.