Miracle-dz/UniFAR-FTUs huggingface dataset:
Point Process Regression

Point processes in time have a wide range of applications that include the claims arrival process in insurance or the analysis of queues in operations research. Due to advances in technology, such samples of point processes are increasingly encountered. A key object of interest is the local intensity function. It has a straightforward interpretation that allows to understand and explore point process data. We consider functional approaches for point processes, where one has a sample of repeated realizations of the point process. This situation is inherently connected with Cox processes, where the intensity functions of the replications are modeled as random functions. Here we study a situation where one records covariates for each replication of the process, such as the daily temperature for bike rentals. For modeling point processes as responses with vector covariates as predictors we propose a novel regression approach for the intensity function that is intrinsically nonparametric. While the intensity function of a point process that is only observed once on a fixed domain cannot be identified, we show how covariates and repeated observations of the process can be utilized to make consistent estimation possible, and we also derive asymptotic rates of convergence without invoking parametric assumptions.
Indirect Detection of Dark Matter in the Galaxy

I present a short overview of the latest developments in indirect searches for dark matter using gamma rays, X-rays, charged cosmic rays, micro waves, radio waves, and neutrinos. I briefly outline key past, present, and future experiments and their search strategies. In several searches there are exciting anomalies which could potentially be emerging dark matter signals. I discuss these anomalous signals, and some future prospects to determine their origins.
Anti-Atherosclerotic Effects of Probucol on Rabbits Differ between Fast Growers and Slow Growers

The anti-atherosclerotic effect of probucol was tested on weaned male New Zealand white rabbits. The rabbits, weighing 2kg, were first fed regular rabbit food ad libitum; and when their weight reached 3kg, they were placed on one of the following diets for 10 weeks: 1% cholesterol and 1% probucol rabbit food diet, or 1% cholesterol and regular rabbit food diet. At the end of the experiment we found that the presence of probucol in the diet reduced the level of plasma cholesterol and increased the resistance of low density lipoprotein to oxidation, whereas the drug had no significant beneficial effect of reducing the area of the aorta occupied by atherosclerotic plaques. When the data were probed further, it became apparent that the area of atherosclerotic plaques of the probucol/cholesterol-fed rabbit group was bimodal in distribution. In one subgroup of these rabbits, whose body weight reached 3kg between 6 and 8 weeks (fast grower) during the conditioning period, the area of atherosclerotic plaques (91.0±1.8% of total aortic surface) was the same as that of the cholesterol-fed fast growers (81.8±8%). In the other subgroup of rabbits, whose body weight reached 3kg between 10 and 11 weeks (slow grower) during the conditioning period, the area of atherosclerotic plaques (37.6±6.2%) was significantly (p<0.05) less than that of the cholesterol-fed slow growers (66.4±11.1%). In the comparison of the fast and slow growers on the probucol diet, the rate of weight gain was the only significant difference found. These observations suggest that the anti-atherosclerotic effect of probucol depends upon the rate of weight gain and that the effect may be mediated by an unknown mechanism.
Soybean Yield and Pest Management as Influenced by Nematodes, Herbicides, and Defoliating Insects

Pest management strategies based on crop losses from single stress factors may be inadequate where multiple stresses occur. Consequently, experiments were conducted in 1989, 1990, and 1991 to quantify interactive effects of stresses from soybean cyst nematode (Heterodera glycines Ichinohe), acifluorfen(5-[2-chloro-4-(trifluoromethyl)phenoxy]-2-nitrobenzoic acid) plus bentazon [3-(l-methylethyl)-(l//)-2,l,3-benzothiadiazin- 4(3ff)-one 2,2-dioxide] herbicides, and green cloverworm [Plathypena scabra (F.)] on seed yield of soybean [Glycine max (L.) Merr. ]. Treatments were combinations of at-planting soybean cyst nematode soil density, acifluorfen plus bentazon rate, and green cloverworm defoliation level. Herbicides were applied at V6 soybean development, while green cloverworm feeding was simulated during R2 to R4 soybean development. Plot yield and yield components (pods per plant, seeds per pod, and weight per seed) were determined. Herbicide injury and defoliation independently reduced yield in 1990 and 1991. Yield reductions from defoliation were attributed to fewer pods per plant, while herbicide injury decreased pods per plant (1990) and weight per seed (1990 and 1991). Moreover, herbicide injury and defoliation interacted over 1990 and 1991 to decrease yield. Although stress from soybean cyst nematode alone never affected yield, interactions with herbicides and with defoliation reduced yield in 1990. No treatment effects on yield were found in 1989, probably due to uncontrolled environmental factors. Because of the 2-yr herbicides x defoliation effect, lower economic injury levels for green cloverworm were developed for soybean treated with acifluorfen plus bentazon. To account for potential interactions involving soybean cyst nematode, growers will be advised to consider adjusting weed and insect management strategies for soybean fields infested with soybean cyst nematode.
Pivotal bootstrap for quantile-based modal regression

In this paper, we develop uniform inference methods for the conditional mode based on quantile regression. Specifically, we propose to estimate the conditional mode by minimizing the derivative of the estimated conditional quantile function defined by smoothing the linear quantile regression estimator, and develop a novel bootstrap method, which we call the pivotal bootstrap, for our conditional mode estimator. Building on high-dimensional Gaussian approximation techniques, we establish the validity of simultaneous confidence rectangles constructed from the pivotal bootstrap for the conditional mode. We also extend the preceding analysis to the case where the dimension of the covariate vector is increasing with the sample size. Finally, we conduct simulation experiments and a real data analysis using U.S. wage data to demonstrate the finite sample performance of our inference method.
An Accreting, Anomalously Low-mass Black Hole at the Center of Low-mass Galaxy IC 750

We present a multiwavelength study of the active galactic nucleus in the nearby (D = 14.1 Mpc) low-mass galaxy IC 750, which has circumnuclear 22 GHz water maser emission. The masers trace a nearly edge-on, warped disk ∼0.2 pc in diameter, coincident with the compact nuclear X-ray source that lies at the base of the ∼kiloparsec-scale extended X-ray emission. The position–velocity structure of the maser emission indicates that the central black hole (BH) has a mass less than 1.4 × 105 M⊙. Keplerian rotation curves fitted to these data yield enclosed masses between 4.1 × 104 M⊙ and 1.4 × 105 M⊙, with a mode of 7.2 × 104 M⊙. Fitting the optical spectrum, we measure a nuclear stellar velocity dispersion km s−1. From near-infrared photometry, we fit a bulge mass of (7.3 ± 2.7) × 108 M⊙ and a stellar mass of 1.4 × 1010 M⊙. The mass upper limit of the intermediate-mass BH in IC 750 falls roughly two orders of magnitude below the MBH–σ* relation and roughly one order of magnitude below the MBH–MBulge and MBH–M* relations—larger than the relations’ intrinsic scatters of 0.58 ± 0.09 dex, 0.69 dex, and 0.65 ± 0.09 dex, respectively. These offsets could be due to larger scatter at the low-mass end of these relations. Alternatively, BH growth is intrinsically inefficient in galaxies with low bulge and/or stellar masses, which causes the BHs to be undermassive relative to their hosts, as predicted by some galaxy evolution simulations.
(Locally) Differentially Private Combinatorial Semi-Bandits

In this paper, we study Combinatorial Semi-Bandits (CSB) that is an extension of classic Multi-Armed Bandits (MAB) under Differential Privacy (DP) and stronger Local Differential Privacy (LDP) setting. Since the server receives more information from users in CSB, it usually causes additional dependence on the dimension of data, which is a notorious side-effect for privacy preserving learning. However for CSB under two common smoothness assumptions \cite{kveton2015tight,chen2016combinatorial}, we show it is possible to remove this side-effect. In detail, for $B_{\infty}$-bounded smooth CSB under either $\varepsilon$-LDP or $\varepsilon$-DP, we prove the optimal regret bound is $\Theta(\frac{mB^2_{\infty}\ln T } {\Delta\epsilon^2})$ or $\tilde{\Theta}(\frac{mB^2_{\infty}\ln T} { \Delta\epsilon})$ respectively, where $T$ is time period, $\Delta$ is the gap of rewards and $m$ is the number of base arms, by proposing novel algorithms and matching lower bounds. For $B_1$-bounded smooth CSB under $\varepsilon$-DP, we also prove the optimal regret bound is $\tilde{\Theta}(\frac{mKB^2_1\ln T} {\Delta\epsilon})$ with both upper bound and lower bound, where $K$ is the maximum number of feedback in each round. All above results nearly match corresponding non-private optimal rates, which imply there is no additional price for (locally) differentially private CSB in above common settings.
An inverse problem of a simultaneous reconstruction of the dielectric constant and conductivity from experimental backscattering data

This report extends our recent progress in tackling a challenging 3D inverse scattering problem governed by the Helmholtz equation. Our target application is to reconstruct dielectric constants, electric conductivities and shapes of front surfaces of objects buried very closely under the ground. These objects mimic explosives, like, e.g. antipersonnel land mines and improvised explosive devices. We solve a coefficient inverse problem with the backscattering data generated by a moving source at a fixed frequency. This scenario has been studied so far by our newly developed convexification method that consists in a new derivation of a boundary value problem for a coupled quasilinear elliptic system. However, in our previous work only the unknown dielectric constants of objects and shapes of their front surfaces were calculated. Unlike this, in the current work performance of our numerical convexification algorithm is verified for the case when the dielectric constants, the electric conductivities and those shapes of objects are unknown. By running several tests with experimentally collected backscattering data, we find that we can accurately image both the dielectric constants and shapes of targets of interests including a challenging case of targets with voids. The computed electrical conductivity serves for reliably distinguishing conductive and non-conductive objects. The global convergence of our numerical procedure is shortly revisited.
Production of germline transgenic Pacific salmonids with dramatically increased growth performance

Transgenic Pacific salmon have been produced by microinjection of a DNA construct consisting of chinook salmon (Oncorhynchus tshawytscha) growth hormone sequences driven by an ocean pout (Macrozoarces americanus) antifreeze protein promoter. This construct was retained in approximately 4% of fish derived from injected eggs, and resulted in dramatic enhancement of growth relative to controls. For coho salmon (O. kisutch) at 15 months of age, the average size of transgenic fish was more than 10-fold that of controls, with the largest fish more than 30-fold larger than nontransgenic siblings. Dramatic growth enhancement was also observed in transgenic rainbow trout (O. mykiss), cutthroat trout (O. clarki), and chinook salmon using this same gene construct. Transgenic coho salmon underwent precocious parr–smolt transformation during their first fall, approximately 6 months in advance of their nontransgenic siblings. At 2 years of age, five male transgenic coho salmon became sexually mature, and four of these .
EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain–Machine Interfaces

In recent years, deep learning (DL) has contributed significantly to the improvement of motor-imagery brain–machine interfaces (MI-BMIs) based on electroencephalography (EEG). While achieving high classification accuracy, DL models have also grown in size, requiring a vast amount of memory and computational resources. This poses a major challenge to an embedded BMI solution that guarantees user privacy, reduced latency, and low power consumption by processing the data locally. In this paper, we propose EEG-TCNet, a novel temporal convolutional network (TCN) that achieves outstanding accuracy while requiring few trainable parameters. Its low memory footprint and low computational complexity for inference make it suitable for embedded classification on resource-limited devices at the edge. Experimental results on the BCI Competition IV-2a dataset show that EEG-TCNet achieves 77.35% classification accuracy in 4-class MI. By finding the optimal network hyperparameters per subject, we further improve the accuracy to 83.84%. Finally, we demonstrate the versatility of EEG-TCNet on the Mother of All BCI Benchmarks (MOABB), a large scale test benchmark containing 12 different EEG datasets with MI experiments. The results indicate that EEG-TCNet successfully generalizes beyond one single dataset, outperforming the current state-of-the-art (SoA) on MOABB by a meta-effect of 0.25.
Normal State of 
Nd1−xSrxNiO2
 from Self-Consistent 
GW+EDMFT

The recent discovery of superconductivity in hole-doped NdNiO$_2$ thin films has captivated the condensed matter physics community. Such compounds with a formal Ni$^+$ valence have been theoretically proposed as possible analogues of the cuprates, and the exploration of their electronic structure and pairing mechanism may provide important insights into the phenomenon of unconventional superconductivity. At the modeling level, there are however fundamental issues that need to be resolved. While it is generally agreed that the low-energy properties of cuprates can to a large extent be captured by a single-band model, there has been a controversy in the recent literature about the importance of a multi-band description of the nickelates. The origin of this controversy is that studies based entirely on density functional theory (DFT) calculations miss important correlation and multi-orbital effects induced by Hund coupling, while model calculations or simulations based on the combination of DFT and (extended) dynamical mean field theory ((E)DMFT) involve ad-hoc parameters and double counting corrections that substantially affect the results. Here we use a multi-site extension of the recently developed $GW$+EDMFT method, which is free of adjustable parameters, to self-consistently compute the interaction parameters and electronic structure of hole-doped NdNiO$_2$. This full ab-initio simulation demonstrates the importance of a multi-orbital description, even for the undoped compound, and produces results for the resistivity and Hall conductance in qualitative agreement with experiment.
Multiorder Laplacian for synchronization in higher-order networks

Traditionally, interaction systems have been described as networks, where links encode information on the pairwise influences among the nodes. Yet, in many systems, interactions take place in larger groups. Recent work has shown that higher-order interactions between oscillators can significantly affect synchronization. However, these early studies have mostly considered interactions up to 4 oscillators at time, and analytical treatments are limited to the all-to-all setting. Here, we propose a general framework that allows us to effectively study populations of oscillators where higher-order interactions of all possible orders are considered, for any complex topology described by arbitrary hypergraphs, and for general coupling functions. To this scope, we introduce a multi-order Laplacian whose spectrum determines the stability of the synchronized solution. Our framework is validated on three structures of interactions of increasing complexity. First, we study a population with all-to-all interactions at all orders, for which we can derive in a full analytical manner the Lyapunov exponents of the system, and for which we investigate the effect of including attractive and repulsive interactions. Second, we apply the multi-order Laplacian framework to synchronization on a synthetic model with heterogeneous higher-order interactions. Finally, we compare the dynamics of coupled oscillators with higher-order and pairwise couplings only, for a real dataset describing the macaque brain connectome, highlighting the importance of faithfully representing the complexity of interactions in real-world systems. Taken together, our multi-order Laplacian allows us to obtain a complete analytical characterization of the stability of synchrony in arbitrary higher-order networks, paving the way towards a general treatment of dynamical processes beyond pairwise interactions.
Spiral defect chaos in Rayleigh-Bénard convection: Asymptotic and numerical studies of azimuthal flows induced by rotating spirals

Rotating spiral patterns in Rayleigh-Benard convection are known to induce azimuthal flows, which raises the question of how different neighboring spirals interact with each other in spiral chaos, and the role of hydrodynamics in this regime. Far from the core, we show that spiral rotations lead to an azimuthal body force that is irrotational and of magnitude proportional to the topological index of the spiral and its angular frequency. The force, although irrotational, cannot be included in the pressure field as it would lead to a nonphysical, multivalued pressure. We calculate the asymptotic dependence of the resulting flow, and show that it leads to a logarithmic dependence of the azimuthal velocity on distance r away from the spiral core in the limit of negligible damping coefficient. This solution dampens to approximately $1/r$ when accounting for no-slip boundary conditions for the convection cell's plate. This flow component can provide additional hydrodynamic interactions among spirals including those observed in spiral defect chaos. We show that the analytic prediction for the azimuthal velocity agrees with numerical results obtained from both two-dimensional generalized Swift-Hohenberg and three-dimensional Boussinesq models, and find that the velocity field is affected by the size and charges of neighboring spirals. Numerically, we identify a correlation between the appearance of spiral defect chaos and the balancing between the mean-flow advection and the diffusive dynamics related to roll unwinding.
Inferring Point Cloud Quality via Graph Similarity

Objective quality estimation of media content plays a vital role in a wide range of applications. Though numerous metrics exist for 2D images and videos, similar metrics are missing for 3D point clouds with unstructured and non-uniformly distributed points. In this paper, we propose <inline-formula><tex-math notation="LaTeX">${\sf GraphSIM}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">GraphSIM</mml:mi></mml:math><inline-graphic xlink:href="yang-ieq1-3047083.gif"/></alternatives></inline-formula>—a metric to accurately and quantitatively predict the human perception of point cloud with superimposed geometry and color impairments. Human vision system is more sensitive to the high spatial-frequency components (e.g., contours and edges), and weighs local structural variations more than individual point intensities. Motivated by this fact, we use graph signal gradient as a quality index to evaluate point cloud distortions. Specifically, we first extract geometric <italic>keypoints</italic> by resampling the reference point cloud geometry information to form an object skeleton. Then, we construct <italic>local graphs</italic> centered at these keypoints for both reference and distorted point clouds. Next, we compute three <italic>moments of color gradients</italic> between centered keypoint and all other points in the same local graph for <italic>local significance</italic> similarity feature. Finally, we obtain similarity index by pooling the local graph significance across all color channels and averaging across all graphs. We evaluate <inline-formula><tex-math notation="LaTeX">${\sf GraphSIM}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">GraphSIM</mml:mi></mml:math><inline-graphic xlink:href="yang-ieq2-3047083.gif"/></alternatives></inline-formula> on two large and independent point cloud assessment datasets that involve a wide range of impairments (e.g., re-sampling, compression, and additive noise). <inline-formula><tex-math notation="LaTeX">${\sf GraphSIM}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">GraphSIM</mml:mi></mml:math><inline-graphic xlink:href="yang-ieq3-3047083.gif"/></alternatives></inline-formula> provides state-of-the-art performance for all distortions with noticeable gains in predicting the subjective mean opinion score (MOS) in comparison with point-wise distance-based metrics adopted in standardized reference software. Ablation studies further show that <inline-formula><tex-math notation="LaTeX">${\sf GraphSIM}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">GraphSIM</mml:mi></mml:math><inline-graphic xlink:href="yang-ieq4-3047083.gif"/></alternatives></inline-formula> can be generalized to various scenarios with consistent performance by adjusting its key modules and parameters. Models and associated materials will be made available at <uri>https://njuvision.github. io/GraphSIM</uri> or <uri>http://smt.sjtu. edu.cn/papers/GraphSIM</uri>.
Convergence of solutions for some degenerate
discounted Hamilton–Jacobi equations

We study solutions of Hamilton--Jacobi equations of the form $$\lambda \alpha(x) u_\lambda(x) + H(x, D_x u_\lambda) = c,$$ where $\alpha$ is a nonnegative function, $\lambda$ a positive constant, $c$ a constant and $H $ a convex coercive Hamiltonian. Under suitable conditions on $\alpha$ we prove that the functions $u_\lambda$ converge as $\lambda\to 0$ to a function $u_0$ that is a solution of the critical equation $H(x, D_x u_0) = c$.
Manipulating the Distributions of Experience used for Self-Play Learning in Expert Iteration

Expert Iteration (ExIt) is an effective framework for learning game-playing policies from self-play. ExIt involves training a policy to mimic the search behaviour of a tree search algorithm -— such as Monte-Carlo tree search -— and using the trained policy to guide it. The policy and the tree search can then iteratively improve each other, through experience gathered in self-play between instances of the guided tree search algorithm. This paper outlines three different approaches for manipulating the distribution of data collected from self-play, and the procedure that samples batches for learning updates from the collected data. Firstly, samples in batches are weighted based on the durations of the episodes in which they were originally experienced. Secondly, Prioritized Experience Replay is applied within the ExIt framework, to prioritise sampling experience from which we expect to obtain valuable training signals. Thirdly, a trained exploratory policy is used to diversify the trajectories experienced in self-play. This paper summarises the effects of these manipulations on training performance evaluated in fourteen different board games. We find major improvements in early training performance in some games, and minor improvements averaged over fourteen games.