Miracle-dz/UniFAR-FTUs huggingface dataset:
Peer effects and endogenous social interactions

We introduce an approach to deal with self-selection of peers in the linear-in-means model. Contrary to the existing proposals we do not require to specify a model for how the selection of peers comes about. Rather, we exploit two restrictions that are inherent to many such specifications to construct intuitive instrumental variables. These restrictions are that link decisions that involve a given individual are not all independent of one another, but that they are independent of the link behavior between other pairs of individuals. A two-stage least-squares estimator of the linear-in-means model is then readily obtained.
AB3DMOT: A Baseline for 3D Multi-Object Tracking and New Evaluation Metrics

3D multi-object tracking (MOT) is essential to applications such as autonomous driving. Recent work focuses on developing accurate systems giving less attention to computational cost and system complexity. In contrast, this work proposes a simple real-time 3D MOT system with strong performance. Our system first obtains 3D detections from a LiDAR point cloud. Then, a straightforward combination of a 3D Kalman filter and the Hungarian algorithm is used for state estimation and data association. Additionally, 3D MOT datasets such as KITTI evaluate MOT methods in 2D space and standardized 3D MOT evaluation tools are missing for a fair comparison of 3D MOT methods. We propose a new 3D MOT evaluation tool along with three new metrics to comprehensively evaluate 3D MOT methods. We show that, our proposed method achieves strong 3D MOT performance on KITTI and runs at a rate of $207.4$ FPS on the KITTI dataset, achieving the fastest speed among modern 3D MOT systems. Our code is publicly available at this http URL.
Superconvergence of time invariants for the Gross-Pitaevskii equation

This paper considers the numerical treatment of the time-dependent Gross-Pitaevskii equation. In order to conserve the time invariants of the equation as accurately as possible, we propose a Crank-Nicolson-type time discretization that is combined with a suitable generalized finite element discretization in space. The space discretization is based on the technique of Localized Orthogonal Decompositions (LOD) and allows to capture the time invariants with an accuracy of order $\mathcal{O}(H^6)$ with respect to the chosen mesh size $H$. This accuracy is preserved due to the conservation properties of the time stepping method. Furthermore, we prove that the resulting scheme approximates the exact solution in the $L^{\infty}(L^2)$-norm with order $\mathcal{O}(\tau^2 + H^4)$, where $\tau$ denotes the step size. The computational efficiency of the method is demonstrated in numerical experiments for a benchmark problem with known exact solution.
A Finite Graph Approach to the Probabilistic Hadwiger-Nelson Problem.

We advance a probabilistic approach to the Hadwiger-Nelson problem initially developed by the Polymath16 project, in particular relating the approach to finite unit-distance graphs. We define the numerical \textit{badness} of a given $k$-coloring of the plane to be the probability that a randomly chosen unit-distance edge is monochromatic under the coloring, and we provide lower bounds on the badness of arbitrary $k$-colorings using a probabilistic technique relating to finite graphs. The contrapositive of the resulting bounds lets us compute lower bounds on the order of non $k$-colorable unit-distance graphs, improving bounds produced by Pritikin and the Polymath16 project in the $k = 4$ and $k = 5$ cases. Additionally, we make partial progress on a probabilistic analog of the de Bruijn-Erdős compactness theorem.
Reinforcement Learning with Quantum Variational Circuits

The development of quantum computational techniques has advanced greatly in recent years, parallel to the advancements in techniques for deep reinforcement learning. This work explores the potential for quantum computing to facilitate reinforcement learning problems. Quantum computing approaches offer important potential improvements in time and space complexity over traditional algorithms because of its ability to exploit the quantum phenomena of superposition and entanglement. Specifically, we investigate the use of quantum variational circuits, a form of quantum machine learning. We present our techniques for encoding classical data for a quantum variational circuit, we further explore pure and hybrid quantum algorithms for DQN and Double DQN. Our results indicate both hybrid and pure quantum variational circuit have the ability to solve reinforcement learning tasks with a smaller parameter space. These comparison are conducted with two OpenAI Gym environments: CartPole and Blackjack, The success of this work is indicative of a strong future relationship between quantum machine learning and deep reinforcement learning.
Joint Variational Autoencoders for Recommendation with Implicit Feedback

Variational Autoencoders (VAEs) have recently shown promising performance in collaborative filtering with implicit feedback. These existing recommendation models learn user representations to reconstruct or predict user preferences. We introduce joint variational autoencoders (JoVA), an ensemble of two VAEs, in which VAEs jointly learn both user and item representations and collectively reconstruct and predict user preferences. This design allows JoVA to capture user-user and item-item correlations simultaneously. By extending the objective function of JoVA with a hinge-based pairwise loss function (JoVA-Hinge), we further specialize it for top-k recommendation with implicit feedback. Our extensive experiments on several real-world datasets show that JoVA-Hinge outperforms a broad set of state-of-the-art collaborative filtering methods, under a variety of commonly-used metrics. Our empirical results also confirm the outperformance of JoVA-Hinge over existing methods for cold-start users with a limited number of training data.
Attention-Based Fully Gated CNN-BGRU for Russian Handwritten Text

This article considers the task of handwritten text recognition using attention-based encoder–decoder networks trained in the Kazakh and Russian languages. We have developed a novel deep neural network model based on a fully gated CNN, supported by multiple bidirectional gated recurrent unit (BGRU) and attention mechanisms to manipulate sophisticated features that achieve 0.045 Character Error Rate (CER), 0.192 Word Error Rate (WER), and 0.253 Sequence Error Rate (SER) for the first test dataset and 0.064 CER, 0.24 WER and 0.361 SER for the second test dataset. Our proposed model is the first work to handle handwriting recognition models in Kazakh and Russian languages. Our results confirm the importance of our proposed Attention-Gated-CNN-BGRU approach for training handwriting text recognition and indicate that it can lead to statistically significant improvements (p-value < 0.05) in the sensitivity (recall) over the tests dataset. The proposed method’s performance was evaluated using handwritten text databases of three languages: English, Russian, and Kazakh. It demonstrates better results on the Handwritten Kazakh and Russian (HKR) dataset than the other well-known models.
Optimal Best-Arm Identification Methods for Tail-Risk Measures

Conditional value-at-risk (CVaR) and value-at-risk (VaR) are popular tail-risk measures in finance and insurance industries where often the underlying probability distributions are heavy-tailed. We use the multi-armed bandit best-arm identification framework and consider the problem of identifying the arm-distribution from amongst finitely many that has the smallest CVaR or VaR. We first show that in the special case of arm-distributions belonging to a single-parameter exponential family, both these problems are equivalent to the best mean-arm identification problem, which is widely studied in the literature. This equivalence however is not true in general. We then propose optimal $\delta$-correct algorithms that act on general arm-distributions, including heavy-tailed distributions, that match the lower bound on the expected number of samples needed, asymptotically (as $ \delta$ approaches $0$). En-route, we also develop new non-asymptotic concentration inequalities for certain functions of these risk measures for the empirical distribution, that may have wider applicability.
Experimental Analysis on Variations and Accuracy of Crosstalk in Trench-Assisted Multi-core Fibers

Space division multiplexing using multi-core fiber (MCF) is a promising solution to cope with the capacity crunch in standard single-mode fiber based optical communication systems. Nevertheless, the achievable capacity of MCF is limited by inter-core crosstalk (IC-XT). Many existing researches treat IC-XT as a static interference, however, recent research shows that IC-XT varies with time, wavelength and baud rate. This inherent stochastic feature requires a comprehensive characterization of the behaviour of MCF to its application in practical transmission systems and the theoretical understanding of IC-XT phenomenon. In this paper, we experimentally investigate the IC-XT behaviour of an 8-core trench-assisted MCF in a temperature-controlled environment, using popular modulation formats. We compare the measured results with the theoretical prediction to validate the analytical IC-XT models previously developed. Moreover, we explore the effects of the measurement configurations on the IC-XT accuracy and present an analysis on the IC-XT step distribution. Our results indicate that a number of transmission parameters have significant influence on the strength and volatility of IC-XT. Moreover, the averaging time of the power meter and the observation time window can affect the value of the observed IC-XT, the degrees of the effects vary with the type of the source signals.
On the Convergence of Consensus Algorithms with Markovian noise and Gradient Bias

This paper presents a finite time convergence analysis for a decentralized stochastic approximation (SA) scheme. The scheme generalizes several algorithms for decentralized machine learning and multi-agent reinforcement learning. Our proof technique involves separating the iterates into their respective consensual parts and consensus error. The consensus error is bounded in terms of the stationarity of the consensual part, while the updates of the consensual part can be analyzed as a perturbed SA scheme. Under the Markovian noise and time varying communication graph assumptions, the decentralized SA scheme has an expected convergence rate of $\mathcal{O}(\log T/\sqrt T )$, where T is the iteration number, in terms of squared norms of gradient for nonlinear SA with smooth but non-convex cost function. This rate is comparable to the best known performances of SA in a centralized setting with a non-convex potential function.
A Geodesic Interior-Point Method for Linear Optimization over Symmetric Cones

We develop a new interior-point method for symmetric-cone optimization, a common generalization of linear, second-order-cone, and semidefinite programming. Our key idea is updating iterates with a geodesic of the cone instead of the kernel of the linear constraints. This approach yields a primal-dual-symmetric, scale-invariant, and line-search-free algorithm that uses just half the variables of a standard primal-dual method. With elementary arguments, we establish polynomial-time convergence matching the standard square-root-n bound. Finally, we prove global convergence of a long-step variant and compare the approaches computationally. For linear programming, our algorithms reduce to central-path tracking in the log domain.
Enhanced anomalous Nernst effect in disordered Dirac and Weyl materials

We analyze the thermoelectric response of Dirac and Weyl semimetals using the semiclassical approach, focusing on the extrinsic contributions due to skew-scattering and side jump. Our results apply to linear response Nernst effect in ferromagnetic Dirac materials such as Fe$_3$Sn$_2$, Weyl semimetal Co$_3$Sn$_2$S$_2$ and to second order response of monolayer graphene on hBN with trigonal warping. Our analysis indicates that the extrinsic contributions can be a significant component of anomalous Nernst response and used to explain an enhanced thermoelectric response.
Bayesian neural networks and dimensionality reduction

In conducting non-linear dimensionality reduction and feature learning, it is common to suppose that the data lie near a lower-dimensional manifold. A class of model-based approaches for such problems includes latent variables in an unknown non-linear regression function; this includes Gaussian process latent variable models and variational auto-encoders (VAEs) as special cases. VAEs are artificial neural networks (ANNs) that employ approximations to make computation tractable; however, current implementations lack adequate uncertainty quantification in estimating the parameters, predictive densities, and lower-dimensional subspace, and can be unstable and lack interpretability in practice. We attempt to solve these problems by deploying Markov chain Monte Carlo sampling algorithms (MCMC) for Bayesian inference in ANN models with latent variables. We address issues of identifiability by imposing constraints on the ANN parameters as well as by using anchor points. This is demonstrated on simulated and real data examples. We find that current MCMC sampling schemes face fundamental challenges in neural networks involving latent variables, motivating new research directions.
Nutrient levels and trade-offs control diversity in a serial dilution ecosystem

Microbial communities feature an immense diversity of species and this diversity is linked to outcomes ranging from ecosystem stability to medical prognoses. Yet the mechanisms underlying microbial diversity are under debate. While simple resource-competition models don't allow for coexistence of a large number of species, it was recently shown that metabolic trade-offs can allow unlimited diversity. Does this diversity persist with more realistic, intermittent nutrient supply? Here, we demonstrate theoretically that in serial dilution culture, metabolic trade-offs allow for high diversity. When a small amount of nutrient is supplied to each batch, the serial dilution dynamics mimic a chemostat-like steady state. If more nutrient is supplied, community diversity shifts due to an 'early-bird' effect. The interplay of this effect with different environmental factors and diversity-supporting mechanisms leads to a variety of relationships between nutrient supply and diversity, suggesting that real ecosystems may not obey a universal nutrient-diversity relationship.
INFLUENCES OF THERMAL AND HYDRIC ENVIRONMENTS ON INCUBATING EGGS AND RESULTANT HATCHLINGS IN A COLUBRID SNAKE (XENOCHROPHIS PISCATOR)

Influence of thermal and hydric environments on incubating eggs, embryonic use of energy and hatchling characteristics were studied in a colubrid snake, Xenochrophis piscator . All viable eggs increased in mass over the course of incubation because these eggs absorbed water from surroundings, and mass gain during incubation was dependent on initial egg mass and substrate water potential. Duration of incubation, hatching success, embryonic use of nutrients and energy and sex ratio, size and mass of hatchlings were unaffected over a wide range of substrate moisture. Duration of incubation decreased as incubation temperature increased. There was a strong family effect on incubation length. Incubation temperature did not determine sex of hatchlings, but profoundly affected hatching success, embryonic use of nutrients and energy, size and mass of hatchlings, carcass mass and post hatching yolk mass. There was no significant difference in total length (SVL plus TL) between female and male hatchlings, but the newborns did exhibit sexual dimorphism in snout vent length and tail length, with females having longer SVL and shorter TL than did males. 32℃ was not a suitable temperature for incubating X. piscator eggs for two reasons: (1) eggs at 32℃ produced hatchlings with less developed carcass, greater amount of unutilized yolk and abnormal tail; (2) embryos expended more energy at 32℃ than at lower temperatures to complete development. Given that eggs at 24℃ and 26℃ both produced well developed hatchlings of which the measured variables were nearly the same for the newborns from the two temperatures, we conclude that 26℃ is the best incubation temperature for X. piscator in this study.
Fully nonlinear dead-core systems

We study fully nonlinear dead-core systems coupled with strong absorption terms. We discover a chain reaction, exploiting properties of an equation along the system. The lack of both the classical Perron's method and comparison principle for the systems requires new tools for tackling the problem. By means of a fixed point argument, we prove existence of solutions, and obtain higher sharp regularity across the free boundary. Additionally, we prove a variant of a weak comparison principle and derive several geometric measure estimates for the free boundary, as well as a Liouville type theorems for entire solutions. These results are new even for linear dead-core systems.
Generators of the plane Cremona group over the field with two elements

The plane Cremona group over the finite field $\mathbb{F}_2$ is generated by three infinite families and finitely many birational maps with small base orbits. One family preserves the pencil of lines through a point, the other two preserve the pencil of conics through four points that form either one Galois orbit of size 4, or two Galois orbits of size 2. For each family, we give a generating set that is either parametrized by the rational functions over $\mathbb{F}_2$, or by a conic in the affine plane over the rational function field of $\mathbb{F}_2$. Moreover, we describe the finitely many remaining maps and give an upper bound on the number needed to generate the Cremona group. Finally, we prove that the plane Cremona group over $\mathbb{F}_2$ is generated by involutions.
Depth Completion with RGB Prior

Depth cameras are a prominent perception system for robotics, especially when operating in natural unstructured environments. Industrial applications, however, typically involve reflective objects under harsh lighting conditions, a challenging scenario for depth cameras, as it induces numerous reflections and deflections, leading to loss of robustness and deteriorated accuracy. Here, we developed a deep model to correct the depth channel in RGBD images, aiming to restore the depth information to the required accuracy. To train the model, we created a novel industrial dataset that we now present to the public. The data was collected with low-end depth cameras and the ground truth depth was generated by multi-view fusion.
Decentralized Accelerated Gradient Methods With Increasing Penalty Parameters

In this article, we study the communication, and (sub)gradient computation costs in distributed optimization. We present two algorithms based on the framework of the accelerated penalty method with increasing penalty parameters. Our first algorithm is for smooth distributed optimization, and it obtains the near optimal <inline-formula><tex-math notation="LaTeX">$O(\sqrt{\frac{L}{\epsilon (1-\sigma _2(W))}}\log \frac{1}{\epsilon })$</tex-math></inline-formula> communication complexity, and the optimal <inline-formula><tex-math notation="LaTeX">$O(\sqrt{\frac{L}{\epsilon }})$</tex-math></inline-formula> gradient computation complexity for <inline-formula><tex-math notation="LaTeX">$L$</tex-math></inline-formula>-smooth convex problems, where <inline-formula><tex-math notation="LaTeX">$\sigma _2(W)$</tex-math></inline-formula> denotes the second largest singular value of the weight matrix <inline-formula><tex-math notation="LaTeX">$W$</tex-math></inline-formula> associated to the network, and <inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> is the target accuracy. When the problem is <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>-strongly convex, and <inline-formula><tex-math notation="LaTeX">$L$</tex-math></inline-formula>-smooth, our algorithm has the near optimal <inline-formula><tex-math notation="LaTeX">$O(\sqrt{\frac{L}{\mu (1-\sigma _2(W))}}\log ^2\frac{1}{\epsilon })$</tex-math></inline-formula> complexity for communications, and the optimal <inline-formula><tex-math notation="LaTeX">$O(\sqrt{\frac{L}{\mu }}\log \frac{1}{\epsilon })$</tex-math></inline-formula> complexity for gradient computations. Our communication complexities are only worse by a factor of <inline-formula><tex-math notation="LaTeX">$(\log \frac{1}{\epsilon })$</tex-math></inline-formula> than the lower bounds. Our second algorithm is designed for nonsmooth distributed optimization, and it achieves both the optimal <inline-formula><tex-math notation="LaTeX">$O(\frac{1}{\epsilon \sqrt{1-\sigma _2(W)}})$</tex-math></inline-formula> communication complexity, and <inline-formula><tex-math notation="LaTeX">$O(\frac{1}{\epsilon ^2})$</tex-math></inline-formula> subgradient computation complexity, which match the lower bounds for nonsmooth distributed optimization.
Spectropolarimetric observations of the CIZA J2242.8+5301 northern radio relic: no evidence of high-frequency steepening

Observations of radio relics at very high frequency (>10 GHz) can help to understand how particles age and are (re-)accelerated in galaxy cluster outskirts and how magnetic fields are amplified in these environments. In this work, we present new single-dish 18.6 GHz Sardinia Radio Telescope and 14.25 GHz Effelsberg observations of the well-known northern radio relic of CIZA J2242.8+5301. We detected the relic which shows a length of ∼1.8 Mpc and a flux density equal to $S_{14.25\, \mathrm{ GHz}}=(9.5\pm 3.9)\, \mathrm{ mJy}$ and $S_{18.6\, \mathrm{ GHz}}=(7.67\pm 0.90)\, \mathrm{ mJy}$ at 14.25 and 18.6 GHz, respectively. The resulting best-fitting model of the relic spectrum from 145 MHz to 18.6 GHz is a power-law spectrum with spectral index α = 1.12 ± 0.03: no evidence of steepening has been found in the new data presented in this work. For the first time, polarization properties have been derived at 18.6 GHz, revealing an averaged polarization fraction of $\sim\! 40{{\ \rm per\ cent}}$ and a magnetic field aligned with the ’filaments’ or ’sheets’ of the relic.
Influence of Low Temperature Stress on Proline Accumulation in Leaves of Sorghum Seedling

The proline content of five strong cold tolerance and seven sensitive cold tolerance sorghum materials was measured under normal temperature and low temperature stress. The results showed that proline content of two kinds of materials was significantly increased after low temperature stress. The proline content of strong cold tolerance materials was higher than sensitive cold tolerance materials under normal temperature. The proline content of strong cold tolerance materials had greater increase than the sensitive cold tolerance materials under low temperature stress. It suggests that proline metabolism at sorghum seedling stage was closely related to cold resistance and the accumulation capacity of proline was positively related with cold tolerance of sorghum materials. Proline accumulation could be regarded as an index of evaluation for cold tolerance at sorghum seedling stage.