Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning One Abstract Bit at a Time Through Self-Invented Experiments Encoded as Neural Networks

There are two important things in science: (A) Finding answers to given questions, and (B) Coming up with good questions. Our artificial scientists not only learn to answer given questions, but also continually invent new questions, by proposing hypotheses to be verified or falsified through potentially complex and time-consuming experiments, including thought experiments akin to those of mathematicians. While an artificial scientist expands its knowledge, it remains biased towards the simplest, least costly experiments that still have surprising outcomes, until they become boring. We present an empirical analysis of the automatic generation of interesting experiments. In the first setting, we investigate self-invented experiments in a reinforcement-providing environment and show that they lead to effective exploration. In the second setting, pure thought experiments are implemented as the weights of recurrent neural networks generated by a neural experiment generator. Initially interesting thought experiments may become boring over time.
Q-Switching and Mode-Locking in Highly Doped Zr $_{2}$ O $_{3}$ –Al $_{2}$ O $_{3}$ –Er $_{2}$ O $_{3}$ -Doped Fiber Lasers Using Graphene as a Saturable Absorber

The application of graphene as a saturable absorber (SA) for generating Q-switched and mode-locked pulses in a Zirconia-Erbium-doped fiber (Zr-EDF) laser is explored. Graphene-based SAs have a very wide operational range, which complements the extended operational bandwidth of the Zr-EDF. The Zr-EDF has an erbium concentration of about 4320 ppm, with absorption rates of 22.0 and 58.0 dB/m at 987 and 1550 nm. The system is capable of generating Q-switched pulses with pulsewidths and energies of 4.6 μs and 16.8 nJ, respectively, as well as peak powers of 3.6 mW at a repetition rate of 50.1 kHz. The Zr-EDF laser can also generate mode-locked pulses with pulsewidths, average output powers, pulse energies, and peak powers of 730 fs, 1.6 mW, 23.1 pJ, and 31.6 W, respectively, at a repetition rate of 69.3 MHz. Both the Q-switched and mode-locked output pulses are highly stable, allowing for their application in a multitude of real-world applications.
Radiative Effects of Cirrus Clouds

Abstract The divergence of net radiation in a tropical atmosphere with cirrus clouds has been examined in terms of two bulk radiative properties of the cloud: effective shortwave optical thickness τ* and broad–band infrared emissivity ϵ&ast. The effective shortwave optical thickness of the cirrus cloud is the primary factor controlling the radiative energy budget at the earth's surface while the cloud's broad–band infrared emissivity is the primary factor influencing the heat budget of the atmosphere. The net radiative energy budget of the tropical atmosphere over an ocean surface in the presence and absence of a cirrus cloud has been examined. The total net radiative energy at the top of the atmosphere is relatively unchanged from the clear sky value by the presence of a cirrus cloud layer while the surface energy budget shows a significant decrease in the shortwave component. The total net radiative energy loss of the atmosphere when a cirrus layer is present is shown to be 22 to 78% of the loss from a .
WL-Align: Weisfeiler-Lehman Relabeling for Aligning Users Across Networks via Regularized Representation Learning

Aligning users across networks using graph representation learning has been found effective where the alignment is accomplished in a low-dimensional embedding space. Yet, highly precise alignment remains challenging, especially for nodes with long-range connectivity to labeled anchors. To alleviate this limitation, we propose WL-Align which employs a regularized representation learning framework to learn distinctive node representations. It extends the Weisfeiler-Lehman Isormorphism Test and learns the alignment in alternating phases of “across-network Weisfeiler-Lehman relabeling” and “proximity-preserving representation learning”. The across-network Weisfeiler-Lehman relabeling is achieved through iterating the anchor-based label propagation and a similarity-based hashing to exploit the known anchors’ connectivity to different nodes in an efficient and robust manner. The representation learning module preserves the second-order proximity within individual networks and is regularized by the across-network Weisfeiler-Lehman hash labels. Extensive experiments on real-world and synthetic datasets have demonstrated that our proposed WL-Align outperforms the state-of-the-art methods, achieving significant performance improvements in the “exact matching” scenario.
CellTranspose: Few-shot Domain Adaptation for Cellular Instance Segmentation

Automated cellular instance segmentation is a process utilized for accelerating biological research for the past two decades, and recent advancements have produced higher quality results with less e ort from the biologist. Most current endeavors focus on completely cutting the researcher out of the picture by generating highly generalized models. However, these models in-variably fail when faced with novel data, distributed differently than the ones used for training. Rather than approaching the problem with methods that presume the availability of large amounts of target data and computing power for retraining, in this work we address the even greater challenge of designing an approach that requires minimal amounts of new annotated data as well as training time. We do so by designing specialized contrastive losses that leverage the few annotated samples very efficiently. A large set of results show that 3 to 5 annotations lead to models with accuracy that: 1) significantly mitigate the covariate shift effects; 2) matches or surpasses other adaptation methods; 3) even approaches methods that have been fully retrained on the target distribution. The adaptation training is only a few minutes, paving a path towards a balance be-tween model performance, computing requirements and expert-level annotation needs.
Deep Active Learning Using Barlow Twins

The generalisation performance of a convolutional neural networks (CNN) is majorly predisposed by the quantity, quality, and diversity of the training images. All the training data needs to be annotated in-hand before, in many real-world applications data is easy to acquire but expensive and time-consuming to label. The goal of the Active learning for the task is to draw most informative samples from the unlabeled pool which can used for training after annotation. With total different objective, self-supervised learning which have been gaining meteoric popularity by closing the gap in performance with supervised methods on large computer vision benchmarks. self-supervised learning (SSL) these days have shown to produce low-level representations that are invariant to distortions of the input sample and can encode invariance to artificially created distortions, e.g. rotation, solarization, cropping etc. self-supervised learning (SSL) approaches rely on simpler and more scalable frameworks for learning. In this paper, we unify these two families of approaches from the angle of active learning using self-supervised learning mainfold and propose Deep Active Learning using BarlowTwins(DALBT), an active learning method for all the datasets using combination of classifier trained along with self-supervised loss framework of Barlow Twins to a setting where the model can encode the invariance of artificially created distortions, e.g. rotation, solarization, cropping etc.
Selecting Classifiers by Pooling over Cross-Validation Results in More Consistency in Small-Sample Classification of Atrial Flutter Localization

Selecting learning machines such as classifiers is an important task when using AI in the clinic. K -fold cross-validation is a practical technique that allows simple inference of such machines. However, the recipe generates many models and does not provide a means to determine the best one. In this paper, a modified recipe is presented, that generates more consistent machines with similar on-average performance, but less extra-sample loss variance and less feature bias. A use case is provided by applying the recipe onto the atrial flutter localization problem
Learning Implicit Functions for Dense 3D Shape Correspondence of Generic Objects

The objective of this paper is to learn dense 3D shape correspondence for topology-varying generic objects in an unsupervised manner. Conventional implicit functions estimate the occupancy of a 3D point given a shape latent code. Instead, our novel implicit function produces a probabilistic embedding to represent each 3D point in a part embedding space. Assuming the corresponding points are similar in the embedding space, we implement dense correspondence through an inverse function mapping from the part embedding vector to a corresponded 3D point. Both functions are jointly learned with several effective and uncertainty-aware loss functions to realize our assumption, together with the encoder generating the shape latent code. During inference, if a user selects an arbitrary point on the source shape, our algorithm can automatically generate a confidence score indicating whether there is a correspondence on the target shape, as well as the corresponding semantic point if there is one. Such a mechanism inherently benefits man-made objects with different part constitutions. The effectiveness of our approach is demonstrated through unsupervised 3D semantic correspondence and shape segmentation.
Countering Malicious Content Moderation Evasion in Online Social Networks: Simulation and Detection of Word Camouflage

Content moderation is the process of screening and monitoring user-generated content online. It plays a crucial role in stopping content resulting from unacceptable behaviors such as hate speech, harassment, violence against specific groups, terrorism, racism, xenophobia, homophobia, or misogyny, to mention some few, in Online Social Platforms. These platforms make use of a plethora of tools to detect and manage malicious information; however, malicious actors also improve their skills, developing strategies to surpass these barriers and continuing to spread misleading information. Twisting and camouflaging keywords are among the most used techniques to evade platform content moderation systems. In response to this recent ongoing issue, this paper presents an innovative approach to address this linguistic trend in social networks through the simulation of different content evasion techniques and a multilingual Transformer model for content evasion detection. In this way, we share with the rest of the scientific community a multilingual public tool, named"pyleetspeak"to generate/simulate in a customizable way the phenomenon of content evasion through automatic word camouflage and a multilingual Named-Entity Recognition (NER) Transformer-based model tuned for its recognition and detection. The multilingual NER model is evaluated in different textual scenarios, detecting different types and mixtures of camouflage techniques, achieving an overall weighted F1 score of 0.8795. This article contributes significantly to countering malicious information by developing multilingual tools to simulate and detect new methods of evasion of content on social networks, making the fight against information disorders more effective.
Sharp approximation and hitting times for stochastic invasion processes

We are interested in the invasion phase for stochastic processes with interactions when a single mutant with positive fitness arrives in a resident population at equilibrium. By a now classic approach, the first stage of the invasion is well approximated by a branching process. The macroscopic phase, when the mutant population is of the same order of the resident population, is described by the limiting dynamical system. We obtain sharper estimates and capture the intermediate mesoscopic phase for the invasive population. It allows us to characterize the hitting times of thresholds, which inherit a large variance from the first stages. These issues are motivated in particular by quantifying times to reach critical values for cancer population or epidemics.
Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces

Explainable AI transforms opaque decision strategies of ML models into explanations that are interpretable by the user, for example, identifying the contribution of each input feature to the prediction at hand. Such explanations, however, entangle the potentially multiple factors that enter into the overall complex decision strategy. We propose to disentangle explanations by finding relevant subspaces in activation space that can be mapped to more abstract human-understandable concepts and enable a joint attribution on concepts and input features. To automatically extract the desired representation, we propose new subspace analysis formulations that extend the principle of PCA and subspace analysis to explanations. These novel analyses, which we call principal relevant component analysis (PRCA) and disentangled relevant subspace analysis (DRSA), optimize relevance of projected activations rather than the more traditional variance or kurtosis. This enables a much stronger focus on subspaces that are truly relevant for the prediction and the explanation, in particular, ignoring activations or concepts to which the prediction model is invariant. Our approach is general enough to work alongside common attribution techniques such as Shapley Value, Integrated Gradients, or LRP. Our proposed methods show to be practically useful and compare favorably to the state of the art as demonstrated on benchmarks and three use cases.
Forecasts of Time Averages with a Numerical Weather Prediction Model

Abstract Forecasts of time averages of 1–10 days in duration by an operational numerical weather prediction model are documented for the global 500 mb height field in spectral space. The error growth of this model is compared to the error growth in a simple idealized model. Useful forecast intervals for time averages are about twice the useful forecast intervals of instantaneous events. The skill of the forecasts for time averages can be increased still further by ignoring numerical weather prediction model forecasts of instantaneous events past certain stopping point. The window of useful forecast time, for the predictions of ten-day averages considered here, is approximately one week. The documented transient spectra have the largest values in the planetary scales, along with the largest variability. The error spectra grow from a relatively flat initial spectra to an asymptotic spectral shape similar to the transient spectra. The largest increase in error occurs on the initial day. Temporal variations i.
Reservoir kernels and Volterra series

A universal kernel is constructed whose sections approximate any causal and time-invariant filter in the fading memory category with inputs and outputs in a finite-dimensional Euclidean space. This kernel is built using the reservoir functional associated with a state-space representation of the Volterra series expansion available for any analytic fading memory filter. It is hence called the Volterra reservoir kernel. Even though the state-space representation and the corresponding reservoir feature map are defined on an infinite-dimensional tensor algebra space, the kernel map is characterized by explicit recursions that are readily computable for specific data sets when employed in estimation problems using the representer theorem. We showcase the performance of the Volterra reservoir kernel in a popular data science application in relation to bitcoin price prediction.
Unbound dwarf spheroidal galaxies and the mass of the Milky Way

There are reasonable dynamical arguments to support the claim that at least some of the Local Group dwarf spheroidal (dSph) galaxies are not internally bound collections of stars. There are now even a few observations that bolster this view. This Letter points out that the lifetimes of these systems may be longer than is commonly appreciated, and that (as unbound systems) dynamical observations of the dSph may tell us much more about the mass and dark matter content of the Milky Way (MW) than they do about the dark matter content of the dwarf spheroidal
Capacity Analysis of Holographic MIMO Channels With Practical Constraints

Holographic Multiple-Input and Multiple-Output (MIMO) is envisioned as a promising technology to realize unprecedented spectral efficiency by integrating a large number of antennas into a compact space. Most research on holographic MIMO is based on isotropic scattering environments, and the antenna gain is assumed to be unlimited by deployment space. However, the channel might not satisfy isotropic scattering because of generalized angle distributions, and the antenna gain is limited by the array aperture in reality. In this letter, we aim to analyze the holographic MIMO channel capacity under practical angle distribution and array aperture constraints. First, we calculate the spectral density for generalized angle distributions by introducing a wavenumber domain-based method. And then, the capacity under generalized angle distributions is analyzed and two different aperture schemes are considered. Finally, numerical results show that the capacity is obviously affected by angle distribution at high signal-to-noise ratio (SNR) but hardly affected at low SNR, and the capacity will not increase infinitely with antenna density due to the array aperture constraint.
Interference effects in polarization-controlled Rayleigh scattering in twisted bilayer graphene

We calculate the \tco{polarization}-controlled Rayleigh scattering response of twisted bilayer graphene (tBLG) based on the continuum electronic band model developed by Bistritzer and MacDonald while considering its refinements which address the effects of structural corrugation, doping-dependent Hartree interactions and particle-hole asymmetry. The dominant wave vectors for the Rayleigh scattering process emanate from various regions of the Moir\'e Brillouin zone (MBZ) in contrast to single-layer graphene (SLG) and AB-stacked bilayer graphene (AB-BLG), where the dominant contributions always stem from the vicinity of the $\bm{K}$ point for optical laser energies and below. Compared to SLG, the integrated Rayleigh intensity is strongly enhanced for small twist angles (\emph{e.g.}, at a twist angle $ \theta = 1.2^{\circ} $, the integrated Rayleigh intensity at laser energy $ E_l=2~\si{\electronvolt} $ enhances by a factor of $\sim $ 100 for the case of parallel \tco{polarization}). While for the case of cross-\tco{polarization}, it exhibits a markedly complex \tco{behavior} suggestive of strong interference effects mediated by the optical matrix elements. We find that at small twist angles, \emph{e.g.}, $ \theta = 1.05^{\circ} $, the corrugation effects strongly enhances the ratio $ \bm{R}_A = \frac{ \text{integrated Rayleigh intensity for parallel \tco{polarization}}}{\text{integrated Rayleigh intensity for cross-\tco{polarization}}} $ by $ \sim $ $ 1300 $ times \emph{viz a viz} SLG or AB-BLG.
Gaussian Heteroskedastic Empirical Bayes without Independence

In this note, we propose empirical Bayes methods under heteroskedastic Gaussian location models, without assuming that the unknown location parameters are independent from the known scale parameters. We derive the finite-sample convergence rate of the mean-squared error regret of our method. We also derive a minimax regret lower bound that matches the upper bound up to logarithmic factors. Moreover, we link decision objectives of other economic problems to meansquared error control. We illustrate our method with a simulation calibrated to the Opportunity Atlas (Chetty, Friedman, Hendren, Jones and Porter, 2018) and Creating Moves to Opportunity (Bergman, Chetty, DeLuca, Hendren, Katz and Palmer, 2019). JEL codes. C10, C11, C44
Eigenvalue Decomposition Based Modified Newton Algorithm

When the Hessian matrix is not positive, the Newton direction maybe not the descending direction. A new method named eigenvalue decomposition based modified Newton algorithm is presented, which first takes eigenvalue decomposition on the Hessian matrix, then replaces the negative eigenvalues with their absolutely values, finally reconstruct Hessian matrix and modify searching direction. The new searching direction is always the descending direction, and the convergence of the algorithm is proved and conclusion on convergence rate is presented qualitatively. At last, a numerical experiment is given for comparing the convergence domains of modified algorithm and classical algorithm.
Tailorable, Visible Light Emission From Silicon Nanocrystals

Crystalline, size-selected Si nanocrystals in the size range 1.8–10 nm grown in inverse micellar cages exhibit highly structured optical absorption and photoluminescence (PL) across the visible range of the spectrum. The most intense PL for the smallest nanocrystals produced (∼2 nm) was in the blue (∼365 nm) with a radiative lifetime of ∼1 ns and is attributed to direct recombination at zone center.
Computational estimation strategies of professional mathematicians.

Forty-four academic pure mathematicians were given Levine's (1982) computational estimation task, which involved estimating the answers to 20 multiplication and division problems and describing the strategies used. The mathematicians were accurate estimators, and they used a great variety of strategies, as many as 23 for a single problem. All but 2 of the mathematicians used some strategy that was not used by the other 43 for at least one problem. When 18 mathematicians were retested after an interval of several months, they used different strategies on the second test for 9 to 17 of the 20 problems. Some theoretical implications of these findings are discussed.
A Fast and Efficient Incremental Approach toward Dynamic Community Detection

Community detection is a discovery tool used by network scientists to analyze the structure of real-world networks. It seeks to identify natural divisions that may exist in the input networks that partition the vertices into coherent modules (or communities). While this problem space is rich with efficient algorithms and software, most of this literature caters to the static use-case where the underlying network does not change. However, many emerging real-world use-cases give rise to a need to incorporate dynamic graphs as inputs. In this paper, we present a fast and efficient incremental approach toward dynamic community detection. The key contribution is a generic technique called Δ-screening, which examines the most recent batch of changes made to an input graph and selects a subset of vertices to reevaluate for potential community (re)assignment. This technique can be incorporated into any of the community detection methods that use modularity as its objective function for clustering. For demonstration purposes, we incorporated the technique into two well-known community detection tools. Our experiments demonstrate that our new incremental approach is able to generate performance speedups without compromising on the output quality (despite its heuristic nature). For instance, on a real-world network with 63M temporal edges (over 12 time steps), our approach was able to complete in 1056 seconds, yielding a 3 × speedup over a baseline implementation. In addition to demonstrating the performance benefits, we also show how to use our approach to delineate appropriate intervals of temporal resolutions at which to analyze an input network.