Miracle-dz/UniFAR-FTUs huggingface dataset:
Computational Learning Theory 4 : Boosting

Let us revisit the definition of PAC-learning. The requirements for a concept class to be PAClearnable are quite stringent. The learning algorithm has to work for all target concepts in the class, for all input distributions, and for any setting of accuracy ( ) and confidence (δ) parameters. It is worthwhile considering what happens when we relax some of these requirements. We have already seen that fixing the confidence paramter to be a constant, e.g., δ = 1/4, leaves the notion of PAC-learnability unchanged. On the other hand, if we only require the learning algorithm to succeed with respect to certain input distributions, then PAC-learning is possible for concept classes that are provably not PAC-learnable in the distribution-free sense, i.e., algorithms that have to work with respect to all distributions.1 For example, the concept class of convex subsets of [0, 1]2 is not (distribution-free) PAC-learnable, but when restricted to the uniform distribution over [0, 1]2, it is learnable. In this lecture, we focus on the accuracy parameter, . The problem of learning is trivial if ≥ 1/2 as we can make a random prediction on an input x ∈ X and achieve an error of 1/2.2 The question we are interested in is what happens when = 1/2 − γ, for some γ > 0? For example, is it possible to learn some concept classe up to error 1/4, but not to an arbitrarily small ? Surprisingly, the answer to this question is no, i.e., if we can learn a concept class up to error at most 1/2 − γ, then we can learn this class up to error bounded by any > 0. This method is known as boosting, as we take a “weak learning” algorithm and boost it to produce a “strong learning” algorithm.
Electrochemical Desalination of Seawater and Hypersaline Brines with Coupled Electricity Storage

We present a zinc|ferricyanide hybrid flow battery that achieves extensive first-pass desalination while simultaneously supplying electrical energy (10 Wh/L). We demonstrate 85% salt removal from simulated seawater (35 g/L NaCl) and 86% from hypersaline brine (100 g/L NaCl), together with reversible battery operation over 100 h with high round-trip efficiency (84.8%). The system has a high operating voltage (E0 = +1.25 V), low specific energy consumption (2.11 Wh/L for 85% salt removal), and a desalination flux (4.7 mol/m2·h) on par with that of reverse osmosis membranes. Salt removal was similarly effective at higher feed salinities, for which reverse osmosis becomes physically impossible because of the pressure required. The results have positive implications for regions that rely on desalination for their freshwater needs, especially where sea salinity is high. Alternatively, the battery may also be useful in minimal liquid discharge wastewater treatment if operated as a brine concentrator.
MOLUCINATE: A Generative Model for Molecules in 3D Space

Recent advances in machine learning have enabled generative models for both optimization and de novo generation of drug candidates with desired properties. Previous generative models have focused on producing SMILES strings or 2D molecular graphs, while attempts at producing molecules in 3D have focused on reinforcement learning (RL), distance matrices, and pure atom density grids. Here we present MOLUCINATE (MOLecUlar ConvolutIoNal generATive modEl), a novel architecture that simultaneously generates topological and 3D atom position information. We demonstrate the utility of this method by using it to optimize molecules for desired radius of gyration. In the future, this model can be used for more useful optimization such as binding affinity for a protein target.
Hollow particles are produced by the burying of sulfate end-groups inside particles prepared by emulsion polymerization of styrene with potassium persulfate as initiator in the absence/presence of a nonionic emulsifier

We prepared submicrometer-sized polystyrene (PS) particles by emulsion polymerization with/without polyoxyethylene nonylphenyl ether nonionic emulsifier (Emulgen 911), and examined how water absorption was affected by polymer end-groups derived from hydrophilic (ionic) or hydrophobic (nonionic) initiator and/or incorporated nonionic emulsifier when they were inside these particles. PS particles having sulfate end-groups, which were prepared using a potassium persulfate initiator with/without Emulgen 911, absorbed a certain amount of water inside them, whereas those having isobutyronitrile end-groups derived from 2,2′-azobis(isobutyronitrile) initiator did not. Absorption of water inside the particles caused by the incorporated Emulgen 911 alone was minimal in the absence of the sulfate end-groups. Considering that sulfate end-groups existing at the particle surface do not contribute to water absorption into the particles, these results indicate that sulfate end-groups did not only exist at the surface of the PS particles but were also buried inside the PS particles during the emulsion polymerization. This offers a clear explanation of a longtime enigma in (emulsifier-free) emulsion polymerization. Both the ionic end-groups buried in the particles and the nonionic emulsifier incorporated inside cooperate to absorb water, thus resulting in the formation of hollow PS particles.
Modeling Interactions of Autonomous Vehicles and Pedestrians with Deep Multi-Agent Reinforcement Learning for Collision Avoidance

Reliable pedestrian crash avoidance mitigation (PCAM) systems are crucial components of safe autonomous vehicles (AVs). The nature of the vehicle-pedestrian interaction where decisions of one agent directly affect the other agent’s optimal behavior, and vice versa, is a challenging yet often neglected aspect of such systems. We address this issue by modeling a Markov decision process (MDP) for a simulated AV-pedestrian interaction at an unmarked crosswalk. The AV’s PCAM decision policy is learned through deep reinforcement learning (DRL). Since modeling pedestrians realistically is challenging, we compare two levels of intelligent pedestrian behavior. While the baseline model follows a predefined strategy, our advanced pedestrian model is defined as a second DRL agent. This model captures continuous learning and the uncertainty inherent in human behavior, making the AV-pedestrian interaction a deep multi-agent reinforcement learning (DMARL) problem. We benchmark the developed PCAM systems according to the collision rate and the resulting traffic flow efficiency with a focus on the influence of observation uncertainty on the decision-making of the agents. The results show that the AV is able to completely mitigate collisions under the majority of the investigated conditions and that the DRL pedestrian model learns an intelligent crossing behavior.
Embedding in MDS codes and Latin cubes

An embedding of a code is a mapping that preserves distances between codewords. We prove that any code with code distance d $d$ and length n $n$ can be embedded into an maximum distance separable (MDS) code with the same code distance and length but under a larger alphabet. As a corollary we obtain embeddings of systems of partial mutually orthogonal Latin cubes and n $n$ ‐ary quasigroups.
Selection rules and dynamic magnetoelectric effect of the spin waves in multiferroic 
BiFeO3

D. G. Farkas, 2 D. Szaller, 1 I. Kézsmárki, 4 U. Nagel, T. Rõõm, L. Peedu, J. Viirok, J. S. White, R. Cubitt, T. Ito, R. S. Fishman, and S. Bordács 10 Department of Physics, Budapest University of Technology and Economics, 1111 Budapest, Hungary MTA-BME Condensed Matter Research Group, 1111 Budapest, Hungary Institute of Solid State Physics, TU Wien, 1040 Vienna, Austria Experimental Physics V, Center for Electronic Correlations and Magnetism, Institute of Physics, University of Augsburg, 86159 Augsburg, Germany National Institute of Chemical Physics and Biophysics, Akadeemia tee 23, 12618 Tallinn, Estonia Laboratory for Neutron Scattering and Imaging (LNS), Paul Scherrer Institut (PSI), CH-5232 Villigen, Switzerland Institut Laue-Langevin, 71 avenue des Martyrs, CS 20156, 38042 Grenoble cedex 9, France National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, 305-8562 Ibaraki, Japan Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA Hungarian Academy of Sciences, Premium Postdoctor Program, 1051 Budapest, Hungary (Dated: October 1, 2021)
Ionised emission and absorption in a large sample of ultraluminous X-ray sources

Most Ultraluminous X-ray sources (ULXs) are thought to be powered by super-Eddington accretion on to stellar-mass compact objects. Accretors in this extreme regime are naturally expected to ionise copious amounts of plasma in their vicinity and launch powerful radiation-driven outflows from their discs. High spectral resolution X-ray observations (with RGS gratings onboard XMM-Newton) of a few ULXs with the best datasets indeed found complex line spectra and confirmed such extreme (0.1 − 0.3c) winds. However, a search for plasma signatures in a large ULX sample with a rigorous technique has never been performed, thereby preventing us from understanding their statistical properties such as the rate of occurrence, to constrain the outflow geometry and its duty cycle. We developed a fast method for automated line detection in X-ray spectra and applied it to the full RGS ULX archive, rigorously quantifying the statistical significance of any candidate lines. Collecting the 135 most significant features detected in 89 observations of 19 objects, we created the first catalogue of spectral lines detected in soft X-ray ULX spectra. We found that the detected emission lines are concentrated around known rest-frame elemental transitions and thus originate from low-velocity material. The absorption lines instead avoid these transitions, suggesting they were imprinted by blueshifted outflows. Such winds therefore appear common among the ULX population. Additionally, we found that spectrally hard ULXs show fewer line detections than soft ULXs, indicating some difference in their accretion geometry and orientation, possibly causing over-ionisation of plasma by the harder spectral energy distributions of harder ULXs.
Influence of Electrospun Poly(vinylidene difluoride) Nanofiber Matrix on the Ion Dynamics of a Protic Organic Ionic Plastic Crystal

The organic ionic plastic crystal (OIPC) family of electrolyte materials has provided a universal material platform for many electrochemical devices such as all-solid-state lithium ion batteries and medium-temperature (T = 120∼180 °C) fuel cells. These OIPC materials usually benefit from being compounded with various matrix materials which not only provide mechanical support but also dramatically affect the ion transport within the composites by forming a percolated interfacial region and/or changing the solid-state structure and dynamics of the bulk phase. Therefore, a fundamental understanding of the influence of the matrix on the ion dynamics of the OIPCs is crucial. In this work composite membranes based on protic organic ionic plastic crystal (POIPC) 1-(N,N-dimethylammonium)-2-(ammonium)ethane triflate ([DMEDAH2][Tf]2) and poly(vinylidene difluoride) (PVDF) have been prepared and characterized systematically. Particular attention has been paid to the influence of PVDF nanofibers on the thermal proper.
Genealogical Population-Based Training for Hyperparameter Optimization

HyperParameter Optimization (HPO) aims at finding the best HyperParameters (HPs) of learning models, such as neural networks, in the fastest and most efficient way possible. Most recent HPO algorithms try to optimize HPs regardless of the model that obtained them, assuming that for different models, same HPs will produce very similar results. We break free from this paradigm and propose a new take on preexisting methods that we called Genealogical Population Based Training (GPBT). GPBT, via the shared histories of"genealogically"-related models, exploit the coupling of HPs and models in an efficient way. We experimentally demonstrate that our method cuts down by 2 to 3 times the computational cost required, generally allows a 1% accuracy improvement on computer vision tasks, and reduces the variance of the results by an order of magnitude, compared to the current algorithms. Our method is search-algorithm agnostic so that the inner search routine can be any search algorithm like TPE, GP, CMA or random search.
Biologically Plausible Training Mechanisms for Self-Supervised Learning in Deep Networks

We develop biologically plausible training mechanisms for self-supervised learning (SSL) in deep networks. Specifically, by biologically plausible training we mean (i) all updates of weights are based on current activities of pre-synaptic units and current, or activity retrieved from short term memory of post synaptic units, including at the top-most error computing layer, (ii) complex computations such as normalization, inner products and division are avoided, (iii) asymmetric connections between units, and (iv) most learning is carried out in an unsupervised manner. SSL with a contrastive loss satisfies the third condition as it does not require labeled data and it introduces robustness to observed perturbations of objects, which occur naturally as objects or observers move in 3D and with variable lighting over time. We propose a contrastive hinge based loss whose error involves simple local computations satisfying (ii), as opposed to the standard contrastive losses employed in the literature, which do not lend themselves easily to implementation in a network architecture due to complex computations involving ratios and inner products. Furthermore, we show that learning can be performed with one of two more plausible alternatives to backpropagation that satisfy conditions (i) and (ii). The first is difference target propagation (DTP), which trains network parameters using target-based local losses and employs a Hebbian learning rule, thus overcoming the biologically implausible symmetric weight problem in backpropagation. The second is layer-wise learning, where each layer is directly connected to a layer computing the loss error. The layers are either updated sequentially in a greedy fashion (GLL) or in random order (RLL), and each training stage involves a single hidden layer network. Backpropagation through one layer needed for each such network can either be altered with fixed random feedback weights (RF) or using updated random feedback weights (URF) as in Amity's study 2019. Both methods represent alternatives to the symmetric weight issue of backpropagation. By training convolutional neural networks (CNNs) with SSL and DTP, GLL or RLL, we find that our proposed framework achieves comparable performance to standard BP learning downstream linear classifier evaluation of the learned embeddings.
Impulse Response Measurement of the Transfer Matrix of a 45-mode Fiber using Superconductive Nanowire Single-Photon Detector

Time-of-flight measurement is an important method to study distributed mode coupling and differential group delay in multimode fibers (MMFs). However, current approaches using regular photodetectors prevents us from studying very weak signals/interactions in fiber due to a limited sensitivity. Here we demonstrate the use of a high-sensitivity superconductive nanowire single-photon detector (SNSPD) to measure the intensity transfer matrix of a commercial 45-mode graded-index multimode fiber. We also observed cladding modes of the MMF as a pedestal of the pulse in the measurement. This work paves way for applications like quantum communication using spatial modes of fiber in the future.
USIS: Unsupervised Semantic Image Synthesis

Semantic Image Synthesis (SIS) is a subclass of image-to-image translation where a photorealistic image is synthesized from a segmentation mask. SIS has mostly been addressed as a supervised problem. However, state-of-the-art methods depend on a huge amount of labeled data and cannot be applied in an unpaired setting. On the other hand, generic unpaired image-to-image translation frameworks underperform in comparison, because they color-code semantic layouts and feed them to traditional convolutional networks, which then learn correspondences in appearance instead of semantic content. In this initial work, we propose a new Unsupervised paradigm for Semantic Image Synthesis (USIS) as a first step towards closing the performance gap between paired and unpaired settings. Notably, the framework deploys a SPADE generator that learns to output images with visually separable semantic classes using a self-supervised segmentation loss. Furthermore, in order to match the color and texture distribution of real images without losing high-frequency information, we propose to use whole image wavelet-based discrimination. We test our methodology on 3 challenging datasets and demonstrate its ability to generate multimodal photorealistic images with an improved quality in the unpaired setting.
A Priori Calibration of Transient Kinetics Data via Machine Learning

The temporal analysis of products reactor provides a vast amount of transient kinetic information that may be used to describe a variety of chemical features including the residence time distribution, kinetic coefficients, number of active sites, and the reaction mechanism. However, as with any measurement device, the TAP reactor signal is convoluted with noise. To reduce the uncertainty of the kinetic measurement and any derived parameters or mechanisms, proper preprocessing must be performed prior to any advanced analysis. This preprocessing consists of baseline correction, i.e., a shift in the voltage response, and calibration, i.e., a scaling of the flux response based on prior experiments. The current methodology of preprocessing requires significant user discretion and reliance on previous experiments that may drift over time. Herein we use machine learning techniques combined with physical constraints to convert the raw instrument signal to chemical information. As such, the proposed methodology demonstrates clear benefits over the traditional preprocessing in the calibration of the inert and feed mixture products without need of prior calibration experiments or heuristic input from the user.
Who Explains the Explanation? Quantitatively Assessing Feature Attribution Methods

AI explainability seeks to increase the transparency of models, making them more trustworthy in the process. The need for transparency has been recently motivated by the emergence of deep learning models, which are particularly obscure by nature. Even in the domain of images, where deep learning has succeeded the most, explainability is still poorly assessed. Multiple feature attribution methods have been proposed in the literature with the purpose of explaining a DL model’s behavior using visual queues, but no standardized metrics to assess or select these methods exist. In this paper we propose a novel evaluation metric — the Focus — designed to quantify the faithfulness of explanations provided by feature attribution methods, such as LRP or GradCAM. First, we show the robustness of the metric through randomization experiments, and then use Focus to evaluate and compare three popular explainability techniques using multiple architectures and datasets. Our results ﬁnd LRP and GradCAM to be consistent and reliable, the former being more accurate for high performing models, while the latter remains most competitive even when applied to poorly performing models. Finally, we identify a strong relation between Focus and factors like model architecture and task, unveiling a new unsupervised approach for the assessment of models.
Note on random Latin squares and the triangle removal process

This is a companion note to the paper"Almost all Steiner triple systems have perfect matchings (arXiv:1611.02246). That paper contains several general lemmas about random Steiner triple systems; in this note we record analogues of these lemmas for random Latin squares, which in particular are necessary ingredients for our recent paper"Large deviations in random Latin squares"(arXiv:2106.11932). Most important is a relationship between uniformly random order-$n$ Latin squares and the triangle removal process on the complete tripartite graph $K_{n,n,n}$.
Physics and Equality Constrained Artificial Neural Networks: Application to Partial Differential Equations

Physics-informed neural networks (PINNs) have been proposed to learn the solution of partial diﬀerential equations (PDE). In PINNs, the residual form of the PDE of interest and its boundary conditions are lumped into a composite objective function as an unconstrained optimization problem, which is then used to train a deep feed-forward neural network. Here, we show that this speciﬁc way of formulating the objective function is the source of severe limitations in the PINN approach when applied to diﬀerent kinds of PDEs. To address these limitations, we propose a versatile framework that can tackle both inverse and forward problems. The framework is adept at multi-ﬁdelity data fusion and can seamlessly constrain the governing physics equations with proper initial and boundary conditions. The backbone of the proposed framework is a nonlinear, equality-constrained optimization problem formulation aimed at minimizing a loss functional, where an augmented Lagrangian method (ALM) is used to formally convert a constrained-optimization problem into an unconstrained-optimization problem. We implement the ALM within a stochastic, gradient-descent type training algorithm in a way that scrupulously focuses on meeting the constraints without sacriﬁcing other loss terms. Additionally, as a modiﬁcation of the original residual layers, we propose lean residual layers in our neural network architecture to address the so-called vanishing-gradient problem. We demonstrate the eﬃcacy and versatility of our physics-and equality-constrained deep-learning framework by applying it to learn the solutions of various multi-dimensional PDEs, including a nonlinear inverse problem from the hydrology ﬁeld with multi-ﬁdelity data fusion. The results produced with our proposed model match exact solutions very closely for all the cases considered.
Gaps, Ambiguity, and Establishing Complexity-Class Containments via Iterative Constant-Setting

Cai and Hemachandra used iterative constant-setting to prove that Few $\subseteq$ $\oplus$P (and thus that FewP $\subseteq$ $\oplus$P). In this paper, we note that there is a tension between the nondeterministic ambiguity of the class one is seeking to capture, and the density (or, to be more precise, the needed"nongappy"-ness) of the easy-to-find"targets"used in iterative constant-setting. In particular, we show that even less restrictive gap-size upper bounds regarding the targets allow one to capture ambiguity-limited classes. Through a flexible, metatheorem-based approach, we do so for a wide range of classes including the logarithmic-ambiguity version of Valiant's unambiguous nondeterminism class UP. Our work lowers the bar for what advances regarding the existence of infinite, P-printable sets of primes would suffice to show that restricted counting classes based on the primes have the power to accept superconstant-ambiguity analogues of UP. As an application of our work, we prove that the Lenstra-Pomerance-Wagstaff Conjecture implies that all (O(1) + loglogn)-ambiguity NP sets are in the restricted counting class $\rm RC_{PRIMES}$.
On WL-rank and WL-dimension of some Deza dihedrants

The WL-rank of a graph Γ is defined to be the rank of the coherent configuration of Γ. The WL-dimension of Γ is defined to be the smallest positive integer m for which Γ is identified by the m-dimensional Weisfeiler-Leman algorithm. We establish that some families of strictly Deza dihedrants have WL-rank 4 or 5 and WL-dimension 2. Computer calculations imply that every strictly Deza dihedrant with at most 59 vertices is circulant or belongs to one of the above families. We also construct a new infinite family of strictly Deza dihedrants whose WL-rank is a linear function of the number of vertices.