cssi/SciSciGPT-SciSciNet huggingface dataset:
Learning Ising models from one or multiple samples

There have been two main lines of work on estimating Ising models: (1) estimating them from multiple independent samples under minimal assumptions about the model's interaction matrix ; and (2) estimating them from one sample in restrictive settings. We propose a unified framework that smoothly interpolates between these two settings, enabling significantly richer estimation guarantees from one, a few, or many samples. Our main theorem provides guarantees for one-sample estimation, quantifying the estimation error in terms of the metric entropy of a family of interaction matrices. As corollaries of our main theorem, we derive bounds when the model's interaction matrix is a (sparse) linear combination of known matrices, or it belongs to a finite set, or to a high-dimensional manifold. In fact, our main result handles multiple independent samples by viewing them as one sample from a larger model, and can be used to derive estimation bounds that are qualitatively similar to those obtained in the afore-described multiple-sample literature. Our technical approach benefits from sparsifying a model's interaction network, conditioning on subsets of variables that make the dependencies in the resulting conditional distribution sufficiently weak. We use this sparsification technique to prove strong concentration and anti-concentration results for the Ising model, which we believe have applications beyond the scope of this paper.
Understanding Decoupled and Early Weight Decay.

Weight decay (WD) is a traditional regularization technique in deep learning, but despite its ubiquity, its behavior is still an area of active research. Golatkar et al. have recently shown that WD only matters at the start of the training in computer vision, upending traditional wisdom. Loshchilov et al. show that for adaptive optimizers, manually decaying weights can outperform adding an l2 penalty to the loss. This technique has become increasingly popular and is referred to as decoupled WD. The goal of this paper is to investigate these two recent empirical observations. We demonstrate that by applying WD only at the start, the network norm stays small throughout training. This has a regularizing effect as the effective gradient updates become larger. However, traditional generalizations metrics fail to capture this effect of WD, and we show how a simple scale-invariant metric can. We also show how the growth of network weights is heavily influenced by the dataset and its generalization properties. For decoupled WD, we perform experiments in NLP and RL where adaptive optimizers are the norm. We demonstrate that the primary issue that decoupled WD alleviates is the mixing of gradients from the objective function and the l2 penalty in the buffers of Adam (which stores the estimates of the first-order moment). Adaptivity itself is not problematic and decoupled WD ensures that the gradients from the l2 term cannot "drown out" the true objective, facilitating easier hyperparameter tuning.
Reconstructing Mixtures of Coded Strings from Prefix and Suffix Compositions

The problem of string reconstruction from substring information has found many applications due to its relevance in DNA- and polymer-based data storage. One practically important and challenging paradigm requires reconstructing mixtures of strings based on the union of compositions of their prefixes and suffixes, generated by mass spectrometry readouts. We describe new coding methods that allow for unique joint reconstruction of subsets of strings selected from a code and provide matching upper and lower bounds on the asymptotic rate of the underlying codebooks. Under certain mild constraints on the problem parameters, one can show that the largest possible rate of a codebook that allows for all subcollections of less than or equal to h codestrings to be uniquely reconstructable from the prefix-suffix information equals 1/h.
Stable Matchings with Restricted Preferences: Structure and Complexity

It is well known that every stable matching instance I has a rotation poset R(I) that can be computed efficiently and the downsets of R(I) are in one-to-one correspondence with the stable matchings of I. Furthermore, for every poset P, an instance I(P) can be constructed efficiently so that the rotation poset of I(P) is isomorphic to P. In this case, we say that I(P) realizes P. Many researchers exploit the rotation poset of an instance to develop fast algorithms or to establish the hardness of stable matching problems. In order to gain a parameterized understanding of the complexity of sampling stable matchings, Bhatnagar et al.[1] introduced stable matching instances whose preference lists are restricted but nevertheless model situations that arise in practice. In this paper, we study four such parameterized restrictions. Our goal is to characterize the rotation posets that arise from these models: k-bounded, where each agent has at most k acceptable partners; k-attribute, where each agent a has an associated vector a ∈ Rk, and a ranks agents in decreasing order according to a linear function φa : Rk → R; (k1, k2)-list, where the men and women can be partitioned into k1 and k2 sets (respectively) such that within each set all agents have identical preferences; k-range, where for each woman w, the men's rankings of w differ by at most k-1, and symmetrically for the women's rankings of each man m. We prove that there is a constant k so that every rotation poset is realized by some instance in models 1--3 (k ≥ 3 for k-bounded, k ≥ 6 for k-attribute, and k1 ≥ 2, k2 = ∞ for (k1, k2)-list, respectively). We describe efficient algorithms for constructing such instances given the Hasse diagram of a poset. As a consequence, the fundamental problem of counting stable matchings remains #BIS-complete even for these restricted instances. For k-range preferences, we show that a poset P is realizable if and only if the Hasse diagram of P has pathwidth bounded by functions of k. Using this characterization, we show that the following problems are fixed parameter tractable when parameterized by the range of the instance: exactly counting and uniformly sampling stable matchings, finding median, sex-equal, and balanced stable matchings.
A locomotor neural circuit persists and functions similarly in larvae and adult Drosophila.

Individual neurons can undergo drastic structural changes, known as neuronal remodeling or structural plasticity. One example of this is in response to hormones, such as during puberty in mammals or metamorphosis in insects. However, in each of these examples it remains unclear whether the remodeled neuron resumes prior patterns of connectivity, and if so, whether the persistent circuits drive similar behaviors. Here, we utilize a well-characterized neural circuit in the Drosophila larva: the Moonwalking Descending Neuron (MDN) circuit. We previously showed that larval MDN induces backward crawling, and synapses onto the Pair1 interneuron to inhibit forward crawling (Carreira-Rosario et al., 2018). MDN is remodeled during metamorphosis and regulates backward walking in the adult fly. We investigated whether Pair1 is remodeled during metamorphosis and functions within the MDN circuit during adulthood. We assayed morphology and molecular markers to demonstrate that Pair1 is remodeled during metamorphosis and persists in the adult fly. MDN-Pair1 connectivity is lost during early pupal stages, when both neurons are severely pruned back, but connectivity is re-established at mid-pupal stages and persist into the adult. In the adult, optogenetic activation of Pair1 resulted in arrest of forward locomotion, similar to what is observed in larvae. Thus, the MDN-Pair1 neurons are an interneuronal circuit - a pair of synaptically connected interneurons - that is re-established during metamorphosis, yet generates similar locomotor behavior at both larval and adult stages.
Investigating the Role of Renewable Energies in Integrated Energy-Water Nexus Planning under Uncertainty Using Fuzzy Logic

Energy and water systems are highly interconnected. Energy is required to extract, transmit, and treat water and wastewater, and water is needed for cooling energy systems. There is a rapid increase in demand for energy and water due to factors such as population and economic growth. In less than 30 years, the need for energy and water will nearly double globally. As the energy and water resources are limited, it is critical to have a sustainable energy-water nexus framework to meet these growing demands. Renewable energies provide substantial opportunities in energy-water nexuses by boosting energy and water reliability and sustainability and can be less water-intensive than conventional technologies. These resources, such as wind and solar power, do not need water inputs. As a result, they can be used as a supplement to the energy-water nexus portfolio. In this paper, renewable energies in energy-water nexus have been investigated for a range of possible scenarios. As renewable energy resources are not deterministic, fuzzy logic is used to model the uncertainty. The results show that renewable energies can significantly improve the energy-water nexus planning; however, the power grid reliability on renewable energy should be aligned with the level of systems uncertainty. The gap between the decisions extracted from the Fuzzy model and the deterministic model amplifies the importance of considering uncertainty to generate reliable decisions.
Proof-of-Stake Mining Games with Perfect Randomness

Proof-of-Stake blockchains based on a longest-chain consensus protocol are an attractive energy-friendly alternative to the Proof-of-Work paradigm. However, formal barriers to "getting the incentives right" were recently discovered, driven by the desire to use the blockchain itself as a source of pseudorandomness. We consider instead a longest-chain Proof-of-Stake protocol with perfect, trusted, external randomness (e.g. a randomness beacon). We produce two main results. First, we show that a strategic miner can strictly outperform an honest miner with just 32.8% of the total stake. Note that a miner of this size cannot outperform an honest miner in the Proof-of-Work model. This establishes that even with access to a perfect randomness beacon, incentives in Proof-of-Work and Proof-of-Stake longest-chain protocols are fundamentally different. Second, we prove that a strategic miner cannot outperform an honest miner with 30.8% of the total stake. This means that, while not quite as secure as the Proof-of-Work regime, desirable incentive properties of Proof-of-Work longest-chain protocols can be approximately recovered via Proof-of-Stake with a perfect randomness beacon. The space of possible strategies in a Proof-of-Stake mining game is significantly richer than in a Proof-of-Work game. Our main technical contribution is a characterization of potentially optimal strategies for a strategic miner, and in particular a proof that the corresponding infinite-state MDP admits an optimal strategy that is positive recurrent.
Maintaining Quality, Enhancing Distribution by Open Access

For more than 110 years, the raison d’etre of The Electrochemical Society has been the broad and effective dissemination of technical and scientific information through technical meetings and publications. Specifically, archival publications make available, to current and future generations, peer-reviewed advances in scientific and engineering fields within the scope of ECS. Many of these entities can benefit greatly from this content via online publication. Our intent is to provide the best quality and widest dissemination possible to our members and to the scientific/technological community at large. The radical changes in scholarly publishing venues made possible by the Internet, and recent advances in electronics technology, greatly expand the potential for wide availability of the content covered by ECS. Of immediate interest in this regard is the unrestricted access to scholarly work via the Internet referred to as Open Access (OA). Because professional societies serve authors, readers, and developers of scientific and technological content, we feel that ECS and other societies should lead the way in the establishment of low cost, effective, and efficient OA approaches. Toward that end, ECS has embarked on a quest to ultimately offer full OA (no charge to authors, readers, libraries, or institutional members) for our four peerreviewed journals. The first step toward this goal is the implementation of our Author Choice Open Access program, which began early this year. As the name implies, authors now have the choice of having their accepted papers published as Open Access articles. Because this intent represents a new paradigm for ECS, we must determine the appropriate business model to realize our goal. Details of the various possibilities are described in other articles in the spring 2014 issue of Interface (http://www.electrochem.org/dl/ interface/spr/spr14/if_spr14.htm) and on the ECS website (http:// www.electrochem.org/oa/).
Good peers have asymmetric gendered effects on female educational outcomes: Experimental evidence from Mexico

Abstract This study examines the gendered effects of early and sustained exposure to high-performing peers on female educational trajectories. Exploiting random allocation to classrooms within middle schools, we measure the effect of male and female high performers on girls’ high school placement outcomes. We examine the effects that peers have on two distinct outcomes: academic performance and preferences for schools with more-selective admission standards. We focus on the effects of peers along the distribution of baseline academic performance. Exposure to high-performing peers of either sex reduces the degree to which high-achieving girls seek placement in more-selective schools. High-achieving boys have particularly strong, negative effects on high-performing girls’ admission scores and preferences for more-selective and more-academic schools. By contrast, high-achieving girls improve low-performing girls’ placement outcomes, but exclusively through a positive effect on exam scores.
Scalable and High-Fidelity Quantum Random Access Memory in Spin-Photon Networks

A quantum RAM architecture based on photonic integrated circuits and existing technologies is proposed.
Optimal portfolio with power utility of absolute and relative wealth

Abstract Portfolio managers often evaluate performance relative to benchmark, usually taken to be the Standard & Poor 500 stock index fund. This relative portfolio wealth is defined as the absolute portfolio wealth divided by wealth from investing in the benchmark (including reinvested dividends). The classic Merton problem for portfolio optimization considers absolute portfolio wealth. We combine absolute and relative wealth in our new utility function. We also consider the case of multiple benchmarks. To both absolute and relative wealth, we apply power utility functions, possibly with different exponents. We obtain an explicit solution and compare it to the classic Merton solution. We apply our results to the Capital Asset Pricing Model setting.
Countering the potential re-emergence of a deadly infectious disease-Information warfare, identifying strategic threats, launching countermeasures.

Objectives Eradicated infectious diseases like smallpox can re-emerge through accident or the designs of bioterrorists, and cause heavy casualties. Presently, the populace is largely susceptible as only a small percentage is vaccinated, and their immunity is likely to have waned. And when the disease re-emerges, the susceptible individuals may be manipulated by disinformation on Social Media to refuse vaccines. Thus, a combination of countermeasures consisting of antiviral drugs and vaccines and a range of policies for their application need to be investigated. Opinions regarding whether to receive vaccines evolve over time through social exchanges via networks that overlap with but are not identical to the disease propagation networks. These couple the spread of the biological and information contagion and necessitate a joint investigation of the two. Methods We develop a computationally tractable metapopulation epidemiological model that captures the joint spatio-temporal evolution of an infectious disease (e.g., smallpox, COVID-19) and opinion dynamics. Results Considering smallpox, the computations based on the model show that opinion dynamics have a substantial impact on the fatality count. Towards understanding how perpetrators are likely to seed the infection, we identify a) the initial distribution of infected individuals that maximize the overall fatality count; and b) which habitation structures are more vulnerable to outbreaks. We assess the relative efficacy of different countermeasures and conclude that a combination of vaccines and drugs minimize the fatalities, and by itself, drugs reduce fatalities more than the vaccine. Accordingly, we assess the impact of increase in the supply of drugs and identify the most effective among a collection of policies for administering of drugs for various parameter combinations. Many of the observed patterns are stable to variations of a diverse set of parameters. Conclusions Our findings provide a quantitative foundation for various important elements of public health discourse that have largely been conducted qualitatively.
Priority Design in Centralized Matching Markets

In many centralized matching markets, agents' property rights over objects are derived from a coarse transformation of an underlying score. Prominent examples include the distance-based system employed by Boston Public Schools, where students who lived within a certain radius of each school were prioritized over all others, and the income-based system used in New York public housing allocation, where eligibility is determined by a sharp income cutoff. Motivated by this, we study how to optimally coarsen an underlying score. Our main result is that, for any continuous objective function and under stable matching mechanisms, the optimal design can be attained by splitting agents into at most three indifference classes for each object. We provide insights into this design problem in three applications: distance-based scores in Boston Public Schools, test-based scores for Chicago exam schools, and income-based scores in New York public housing allocation.
Hodge Structures of CM-Type

We show that any effective Hodge structure of CM-type occurs (without having to take a Tate twist) in the cohomology of some CM abelian variety over C. As a consequence we get a simple proof of the theorem (due to Hazama) that the usual Hodge conjecture for the class of all CM abelian varieties implies the general Hodge conjecture for the same class.
Clifford quantum cellular automata: Trivial group in 2D and Witt group in 3D

We study locality preserving automorphisms of operator algebras on D-dimensional uniform lattices of prime p-dimensional qudits quantum cellular automata (QCAs), specializing in those that are translation invariant (TI), and map every prime p-dimensional Pauli matrix to a tensor product of Pauli matrices (Clifford). We associate antihermitian forms of the unit determinant over Laurent polynomial rings to TI Clifford QCA with lattice boundaries and prove that the form determines the QCA up to Clifford circuits and shifts (trivial). It follows that every 2D TI Clifford QCA is trivial since the antihermitian form in this case is always trivial. Furthermore, we prove that for any D, the fourth power of any TI Clifford QCA is trivial. We present explicit examples of nontrivial TI Clifford QCA for D = 3 and any odd prime p and show that the Witt group of the finite field Fp is a subgroup of the group C(D=3,p) of all TI Clifford QCA modulo trivial ones. That is, C(D=3,p≡1mod4)⊇Z2×Z2 and C(D=3,p≡3mod4)⊇Z4. The examples are found by disentangling the ground state of a commuting Pauli Hamiltonian, which is constructed by coupling layers of prime dimensional toric codes such that an exposed surface has an anomalous topological order that is not realizable by commuting Pauli Hamiltonians strictly in two dimensions. In an appendix independent of the main body of this paper, we revisit a recent theorem of Freedman and Hastings that any two-dimensional QCA, which is not necessarily Clifford or translation invariant, is a constant depth quantum circuit followed by a shift. We give a more direct proof of the theorem without using any ancillas.
Identifying Periodic Variable Stars and Eclipsing Binary Systems with Long-term Las Cumbres Observatory Photometric Monitoring of ZTF J0139+5245

We present the results of our search for variable stars using the long-term Las Cumbres Observatory (LCO) monitoring of white dwarf ZTF J0139+5245 with the two 1.0-m telescope nodes located at McDonald Observatory using the Sinistro imaging instrument. In this search, we find 38 variable sources, of which 27 are newly discovered or newly classified (71%) based on comparisons with previously published catalogs, thereby increasing the number of detections in the field-of-view under consideration by a factor of $\approx$ 2.5. We find that the improved photometric precision per-exposure due to longer exposure time for LCO images combined with the greater time-sampling of LCO photometry enables us to increase the total number of detections in this field-of-view. Each LCO image covers a field-of-view of $26' \times 26'$ and observes a region close to the Galactic plane ($b = -9.4^\circ$) abundant in stars with an average stellar density of $\approx 8$ arcmin$^{-2}$. We perform aperture photometry and Fourier analysis on over 2000 stars across 1560 LCO images spanning 537 days to find 28 candidate BY Draconis variables, 3 candidate eclipsing binaries of type EA, and 7 candidate eclipsing binaries of type EW. In assigning preliminary classifications to our detections, we demonstrate the applicability of the Gaia color-magnitude diagram (CMD) as a powerful classification tool for variable star studies.
Does Bayesian model averaging improve polynomial extrapolations? Two toy problems as tests

We assess the accuracy of Bayesian polynomial extrapolations from small parameter values, x, to large values of x. We consider a set of polynomials of fixed order, intended as a proxy for a fixed-order effective field theory (EFT) description of data. We employ Bayesian Model Averaging (BMA) to combine results from different order polynomials (EFT orders). Our study considers two "toy problems" where the underlying function used to generate data sets is known. We use Bayesian parameter estimation to extract the polynomial coefficients that describe these data at low x. A "naturalness" prior is imposed on the coefficients, so that they are O(1). We Bayesian-Model-Average different polynomial degrees by weighting each according to its Bayesian evidence and compare the predictive performance of this Bayesian Model Average with that of the individual polynomials. The credibility intervals on the BMA forecast have the stated coverage properties more consistently than does the highest evidence polynomial, though BMA does not necessarily outperform every polynomial.
Machine learning dynamic correlation in chemical kinetics.

Lattice models are a useful tool to simulate the kinetics of surface reactions. Since it is expensive to propagate the probabilities of the entire lattice configurations, it is practical to consider the occupation probabilities of a typical site or a cluster of sites instead. This amounts to a moment closure approximation of the chemical master equation. Unfortunately, simple closures, such as the mean-field and the pair approximation (PA), exhibit weaknesses in systems with significant long-range correlation. In this paper, we show that machine learning (ML) can be used to construct accurate moment closures in chemical kinetics using the lattice Lotka–Volterra model as a model system. We trained feedforward neural networks on kinetic Monte Carlo (KMC) results at select values of rate constants and initial conditions. Given the same level of input as PA, the ML moment closure (MLMC) gave accurate predictions of the instantaneous three-site occupation probabilities. Solving the kinetic equations in conjunction with MLMC gave drastic improvements in the simulated dynamics and descriptions of the dynamical regimes throughout the parameter space. In this way, MLMC is a promising tool to interpolate KMC simulations or construct pretrained closures that would enable researchers to extract useful insight at a fraction of the computational cost.
Asymptotic expansions of the prime counting function

Abstract We provide several asymptotic expansions of the prime counting function π ( x ) and related functions. We define an asymptotic continued fraction expansion of a complex-valued function of a real or complex variable to be a possibly divergent continued fraction whose approximants provide an asymptotic expansion of the given function. We show that, for each positive integer n, two well-known continued fraction expansions of the exponential integral function E n ( z ) correspondingly yield two asymptotic continued fraction expansions of π ( x ) / x . We prove this by first establishing some general results about asymptotic continued fraction expansions. We show, for instance, that the “best” rational function approximations of a function possessing an asymptotic Jacobi continued fraction expansion are precisely the approximants of the continued fraction, and as a corollary we determine all of the best rational function approximations of the function π ( e x ) / e x . Finally, we generalize our results on π ( x ) to any arithmetic semigroup satisfying Axiom A, and thus to any number field.
A unifying modeling abstraction for infinite-dimensional optimization

Abstract Infinite-dimensional optimization (InfiniteOpt) problems involve modeling components (variables, objectives, and constraints) that are functions defined over infinite-dimensional domains. Examples include continuous-time dynamic optimization (time is an infinite domain and components are a function of time), PDE optimization problems (space and time are infinite domains and components are a function of space-time), as well as stochastic and semi-infinite optimization (random space is an infinite domain and components are a function of such random space). InfiniteOpt problems also arise from combinations of these problem classes (e.g., stochastic PDE optimization). Given the infinite-dimensional nature of objectives and constraints, one often needs to define appropriate quantities (measures) to properly pose the problem. Moreover, InfiniteOpt problems often need to be transformed into a finite dimensional representation so that they can be solved numerically. In this work, we present a unifying abstraction that facilitates the modeling, analysis, and solution of InfiniteOpt problems. The proposed abstraction enables a general treatment of infinite-dimensional domains and provides a measure-centric paradigm to handle associated variables, objectives, and constraints. This abstraction allows us to transfer techniques across disciplines and with this identify new, interesting, and useful modeling paradigms (e.g., event constraints and risk measures defined over time domains). Our abstraction serves as the backbone of an intuitive Julia -based modeling package that we call InfiniteOpt.jl . We demonstrate the developments using diverse case studies arising in engineering.
Seeking an Optimal Approach for Computer-Aided Pulmonary Embolism Detection

Pulmonary embolism (PE) represents a thrombus (“blood clot”), usually originating from a lower extremity vein, that travels to the blood vessels in the lung, causing vascular obstruction and in some patients, death. This disorder is commonly diagnosed using CT pulmonary angiography (CTPA). Deep learning holds great promise for the computer-aided CTPA diagnosis (CAD) of PE. However, numerous competing methods for a given task in the deep learning literature exist, causing great confusion regarding the development of a CAD PE system. To address this confusion, we present a comprehensive analysis of competing deep learning methods applicable to PE diagnosis using CTPA at the both image and exam levels. At the image level, we compare convolutional neural networks (CNNs) with vision transformers, and contrast self-supervised learning (SSL) with supervised learning, followed by an evaluation of transfer learning compared with training from scratch. At the exam level, we focus on comparing conventional classification (CC) with multiple instance learning (MIL). Our extensive experiments consistently show: (1) transfer learning consistently boosts performance despite differences between natural images and CT scans, (2) transfer learning with SSL surpasses its supervised counterparts; (3) CNNs outperform vision transformers, which otherwise show satisfactory performance; and (4) CC is, surprisingly, superior to MIL. Compared with the state of the art, our optimal approach provides an AUC gain of 0.2% and 1.05% for image-level and exam-level, respectively.
Higher dimensional generalization of the Benjamin-Ono equation: 2D case

We consider a higher-dimensional version of the Benjamin-Ono (HBO) equation in the 2D setting: $u_t- \mathcal{R}_1 \Delta u + \frac{1}{2}(u^2)_x=0, (x,y) \in \mathbb{R}^2$, which is $L^2$-critical, and investigate properties of solutions both analytically and numerically. For a generalized equation (fractional 2D gKdV) after deriving the Pohozaev identities, we obtain non-existence conditions for solitary wave solutions, then prove uniform bounds in the energy space or conditional global existence, and investigate the radiation region, a specific wedge in the negative $x$-direction. We then introduce our numerical approach in a general context, and apply it to obtain the ground state solution in the 2D critical HBO equation, then show that its mass is a threshold for global vs. finite time existing solutions, which is typical in the focusing (mass-)critical dispersive equations. We also observe that globally existing solutions tend to disperse completely into the radiation in this nonlocal equation. The blow-up solutions travel in the positive $x$-direction with the rescaled ground state profile while also radiating dispersive oscillations into the radiative wedge. We conclude with examples of different interactions of two solitary wave solutions, including weak and strong interactions.
The disk partition function in string theory

We investigate the disk partition function for the open string. This is a subtle problem because of the presence of a residual gauge group PSL(2, ℝ) on the worldsheet even after fixing the conformal gauge. It naively has infinite volume and leads to a vanishing answer. We use different methods that all demonstrate that PSL(2, ℝ) effectively behaves like a group with finite negative volume in the path integral, which leads to a simple prescription for the computation of the disk partition function. We apply our findings to give a simple rederivation of the D-brane tensions.
Intensity Harmonization for Airborne LiDAR

Constructing a point cloud for a large geographic region, such as a state or country, can require multiple years of effort. Often several vendors will be used to acquire LiDAR data, and a single region may be captured by multiple LiDAR scans. A key challenge is maintaining consistency between these scans, which includes point density, number of returns, and intensity. Intensity in particular can be very different between scans, even in areas that are overlapping. Harmonizing the intensity between scans to remove these discrepancies is expensive and time consuming. In this paper, we propose a novel method for point cloud harmonization based on deep neural networks. We evaluate our method quantitatively and qualitatively using a high quality real world LiDAR dataset. We compare our method to several baselines, including standard interpolation methods as well as histogram matching. We show that our method performs as well as the best baseline in areas with similar intensity distributions, and outperforms all baselines in areas with different intensity distributions. Source code is available at https://github.com/mvrl/lidar-harmonization.
Limiting fluctuation and trajectorial stability of multilayer neural networks with mean field training

The mean field (MF) theory of multilayer neural networks centers around a particular infinite-width scaling, where the learning dynamics is closely tracked by the MF limit. A random fluctuation around this infinite-width limit is expected from a large-width expansion to the next order. This fluctuation has been studied only in shallow networks, where previous works employ heavily technical notions or additional formulation ideas amenable only to that case. Treatment of the multilayer case has been missing, with the chief difficulty in finding a formulation that captures the stochastic dependency across not only time but also depth. In this work, we initiate the study of the fluctuation in the case of multilayer networks, at any network depth. Leveraging on the neuronal embedding framework recently introduced by Nguyen and Pham, we systematically derive a system of dynamical equations, called the second-order MF limit, that captures the limiting fluctuation distribution. We demonstrate through the framework the complex interaction among neurons in this second-order MF limit, the stochasticity with cross-layer dependency and the nonlinear time evolution inherent in the limiting fluctuation. A limit theorem is proven to relate quantitatively this limit to the fluctuation of large-width networks. We apply the result to show a stability property of gradient descent MF training: in the large-width regime, along the training trajectory, it progressively biases towards a solution with "minimal fluctuation" (in fact, vanishing fluctuation) in the learned output function, even after the network has been initialized at or has converged (sufficiently fast) to a global optimum. This extends a similar phenomenon previously shown only for shallow networks with a squared loss in the ERM setting, to multilayer networks with a loss function that is not necessarily convex in a more general setting.
Practical, Provably-Correct Interactive Learning in the Realizable Setting: The Power of True Believers

We consider interactive learning in the realizable setting and develop a general framework to handle problems ranging from best arm identification to active classification. We begin our investigation with the observation that agnostic algorithms \emph{cannot} be minimax-optimal in the realizable setting. Hence, we design novel computationally efficient algorithms for the realizable setting that match the minimax lower bound up to logarithmic factors and are general-purpose, accommodating a wide variety of function classes including kernel methods, H{\"o}lder smooth functions, and convex functions. The sample complexities of our algorithms can be quantified in terms of well-known quantities like the extended teaching dimension and haystack dimension. However, unlike algorithms based directly on those combinatorial quantities, our algorithms are computationally efficient. To achieve computational efficiency, our algorithms sample from the version space using Monte Carlo "hit-and-run" algorithms instead of maintaining the version space explicitly. Our approach has two key strengths. First, it is simple, consisting of two unifying, greedy algorithms. Second, our algorithms have the capability to seamlessly leverage prior knowledge that is often available and useful in practice. In addition to our new theoretical results, we demonstrate empirically that our algorithms are competitive with Gaussian process UCB methods.
Adiabatic quantum linear regression.

A major challenge in machine learning is the computational expense of training these models. Model training can be viewed as a form of optimization used to fit a machine learning model to a set of data, which can take up significant amount of time on classical computers. Adiabatic quantum computers have been shown to excel at solving optimization problems, and therefore, we believe, present a promising alternative to improve machine learning training times. In this paper, we present an adiabatic quantum computing approach for training a linear regression model. In order to do this, we formulate the regression problem as a quadratic unconstrained binary optimization (QUBO) problem. We analyze our quantum approach theoretically, test it on the D-Wave adiabatic quantum computer and compare its performance to a classical approach that uses the Scikit-learn library in Python. Our analysis shows that the quantum approach attains up to $${2.8 \times }$$ speedup over the classical approach on larger datasets, and performs at par with the classical approach on the regression error metric. The quantum approach used the D-Wave 2000Q adiabatic quantum computer, whereas the classical approach used a desktop workstation with an 8-core Intel i9 processor. As such, the results obtained in this work must be interpreted within the context of the specific hardware and software implementations of these machines.
CoAtNet: Marrying Convolution and Attention for All Data Sizes

Transformers have attracted increasing interests in computer vision, but they still fall behind state-of-the-art convolutional networks. In this work, we show that while Transformers tend to have larger model capacity, their generalization can be worse than convolutional networks due to the lack of the right inductive bias. To effectively combine the strengths from both architectures, we present CoAtNets(pronounced "coat" nets), a family of hybrid models built from two key insights:(1) depthwise Convolution and self-Attention can be naturally unified via simple relative attention; (2) vertically stacking convolution layers and attention layers in a principled way is surprisingly effective in improving generalization, capacity and efficiency. Experiments show that our CoAtNets achieve state-of-the-art performance under different resource constraints across various datasets. For example, CoAtNet achieves 86.0% ImageNet top-1 accuracy without extra data, and 89.77% with extra JFT data, outperforming prior arts of both convolutional networks and Transformers. Notably, when pre-trained with 13M images fromImageNet-21K, our CoAtNet achieves 88.56% top-1 accuracy, matching ViT-huge pre-trained with 300M images from JFT while using 23x less data.
Stoney vs. Histed: Quantifying the spatial effects of intracortical microstimulation.

Abstract Background Intracortical microstimulation (ICMS) is used to map neural circuits and restore lost sensory modalities such as vision, hearing, and somatosensation. The spatial effects of ICMS remain controversial: Stoney and colleagues proposed that the volume of somatic activation increased with stimulation intensity, while Histed et al., suggested activation density, but not somatic activation volume, increases with stimulation intensity. Objective We used computational modeling to quantify the spatial effects of ICMS intensity and unify the apparently paradoxical findings of Histed and Stoney. Methods We implemented a biophysically-based computational model of a cortical column comprising neurons with realistic morphology and representative synapses. We quantified the spatial effects of single pulses and short trains of ICMS, including the volume of activated neurons and the density of activated neurons as a function of stimulation intensity. Results At all amplitudes, the dominant mode of somatic activation was by antidromic propagation to the soma following axonal activation, rather than via transsynaptic activation. There were no occurrences of direct activation of somata or dendrites. The volume over which antidromic action potentials were initiated grew with stimulation amplitude, while the volume of somatic activation increased marginally. However, the density of somatic activation within the activated volume increased with stimulation amplitude. Conclusions The results resolve the apparent paradox between Stoney and Histed's results by demonstrating that the volume over which action potentials are initiated grows with ICMS amplitude, consistent with Stoney. However, the volume occupied by the activated somata remains approximately constant, while the density of activated neurons within that volume increase, consistent with Histed.
Chromatin bridges, not micronuclei, activate cGAS after drug-induced mitotic errors in human cells.

Mitotic errors can activate cyclic GMP–AMP synthase (cGAS) and induce type I interferon (IFN) signaling. Current models propose that chromosome segregation errors generate micronuclei whose rupture activates cGAS. We used a panel of antimitotic drugs to perturb mitosis in human fibroblasts and measured abnormal nuclear morphologies, cGAS localization, and IFN signaling in the subsequent interphase. Micronuclei consistently recruited cGAS without activating it. Instead, IFN signaling correlated with formation of cGAS-coated chromatin bridges that were selectively generated by microtubule stabilizers and MPS1 inhibitors. cGAS activation by chromatin bridges was suppressed by drugs that prevented cytokinesis. We confirmed cGAS activation by chromatin bridges in cancer lines that are unable to secrete IFN by measuring paracrine transfer of 2′3′-cGAMP to fibroblasts, and in mouse cells. We propose that cGAS is selectively activated by self-chromatin when it is stretched in chromatin bridges. Immunosurveillance of cells that fail mitosis, and antitumor actions of taxanes and MPS1 inhibitors, may depend on this effect.
Energy and Energy Flux in Rossby Waves

In discussing the energy and its flux for Rossby waves, we encounter the problem that the natural definition of the energy flux at the lowest order pu. is horizontally non-divergent and therefore has no effect on the wave energy. To discuss the real energy flux, one has to include the divergent, non-geostrophic O(e) part of the velocity field as well as the pressure contribution at this order. This would be a messy business, and what is worse is that the solution of the quasi-geostrophic potential vorticity equation doesn’t give us these quantities as part of the solution. Is there a way we can describe the energy flux entirely within the quasi-geostrophic framework? The answer is yes, and it follows from a direct consideration of the linear quasi-geostrophic equation. First, though, let us orient the y-axis in the direction of the gradient of the ambient potential vorticity, VQ, and call the magnitude of the gradient β for obvious reasons. As long as the gradient is a constant, there is no loss of generality. It will be up to the student to try to generalize these results when the gradient is not constant. The linear qgpve is $${\textstyle{\partial \over {\partial t}}}[{\nabla ^2}\psi - {a^2}\psi ] + \beta {\textstyle{{\partial \psi } \over {\partial x}}} = 0$$ (16.1)
Antibiotics: β‐Lactamase Inhibitors

Mechanistic Aspects of β-Lactamase Inhibition β-Lactam-Based Inhibitors Economic Aspects Keywords: acetylene
Integrable Hamiltonian Systems Associated to Families of Curves and Their Bi-Hamiltonian Structure

In this paper we show how there is associated an integrable Hamiltonian system to a certain set of algebraic-geometric data. Roughly speaking these data consist of a family of algebraic curves, parametrized by an affine algebraic variety B, a subalgebra C of O(B) and a polynomial φ(x, y) in two variables. The phase space is constructed geometrically from the family of curves and has a natural projection onto B; the regular functions on B lead to an algebra of functions in involution and the level sets of the moment map are symmetric products of algebraic curves.
Atomic ionization by intense laser fields

A method, alternate to that of conventional perturbation theory, is developed and illustrated for the computation of the photoionization of atoms by photons with energies less than the atomic ionization potential. The method allows treatment of the electromagnetic field to all orders at a rather simple level of analysis. It represents, in fact, a slight generalization of the Born approach to the theory of scattering. As a consequence of certain approximations, simple analytic formulae are obtained to describe multiple photon ionization events. Because of one of the approximations (a situation straightforwardly rectifiable, but not attempted here for the sake of illustrative simplicity), the formulae obtained here are probably only qualitatively interesting.
The Present Status of the Genetic Code

Publisher Summary This chapter discusses the present status of the genetic code. All coding theories implicitly or explicitly took for granted that the nucleic acid message and its corresponding polypeptide chain were collinear. Punctuation is of interest, not only for what sequences are involved but also in terms of the mechanisms, by which the process operates. Thus, punctuation is in a sense a transition point between the purely formal considerations of what codons correspond to what amino acids and the consideration of the process of decoding. The essence of this in vitro approach to codon assignments is to use input RNAs, of either the defined primary structure or of the undefined primary structure, simple enough, so that the gross composition of the output polypeptide reveals, either the exact codon assignments or very narrow limits, for the codon assignment for an amino acid. Addition or deletion of base pairs from a gene has, of course, a more far reaching and drastic effect than base substitution. The cellular processes of nucleic acid replication, transcription, and translation work exactly like tape-reading processes. DNA or messenger RNA (mRNA) can be regarded as input tapes that feed through such tape readers, as the replication polymerase, the transcription polymerase, and the translation tape-reading complex.
Diamond Sampling for Approximate Maximum All-pairs Dot-product (MAD) Search

Given two sets of vectors, $A = \{{a_1}, \dots, {a_m}\}$ and $B=\{{b_1},\dots,{b_n}\}$, our problem is to find the top-$t$ dot products, i.e., the largest $|{a_i}\cdot{b_j}|$ among all possible pairs. This is a fundamental mathematical problem that appears in numerous data applications involving similarity search, link prediction, and collaborative filtering. We propose a sampling-based approach that avoids direct computation of all $mn$ dot products. We select diamonds (i.e., four-cycles) from the weighted tripartite representation of $A$ and $B$. The probability of selecting a diamond corresponding to pair $(i,j)$ is proportional to $({a_i}\cdot{b_j})^2$, amplifying the focus on the largest-magnitude entries. Experimental results indicate that diamond sampling is orders of magnitude faster than direct computation and requires far fewer samples than any competing approach. We also apply diamond sampling to the special case of maximum inner product search, and get significantly better results than the state-of-the-art hashing methods.
On Whether the Atemporal Conception of the World Is Also Amodal

The world as we encounter it is full of contingency, rife with danger and opportunity. Surrounding everything that does happen is a halo of unrealized possibilities. There is not only what does happen, but what could, would, or might have happened if things had been otherwise. The serenity prayer popularized in AA meetings tells us that practical wisdom is at least in part a matter of knowing how to recognize the difference between necessity and contingency.i And it is not just common sense that is steeped in modality. A good part of our scientific knowledge is explicitly modal knowledge. Our physical theories aren’t just interested in what actually happens. They mine the pattern of actual events for clues about their modal substructure, i.e., the laws and causes whose implications describe a wide array of purely hypothetical situations.