arXiv subjects

Veit Elser

Publications and source records attributed to Veit Elser.

At least 19 recordsLinked to original sources

arXiv2026

Machine-learnable Sets

In this study we present a formal definition of large discrete sets having, informally, three properties: their elements are easily recognized, easily generated, and the latter tasks are easily learned from examples. The formalism is specialized to sets of binary strings and a definition of "machine-learnability" based on the existence of a bounded-complexity Boolean autoencoder that fixes the elements of the set. We present experiments where the autoencoders are implemented by nets of Boolean threshold functions. Machine-learnability is demonstrated for Rorschach patterns (that may have reversed contrast in the mirrored half), and considerably "wilder" sets whose elements are only approximately fixed by admissible autoencoders. In the second case we demonstrate a simple iteration that evolves wild sets to make them properly machine-learnable.

cs.LG↗

arXiv2026

Melting temperature shifts from quantum fluctuations in generalized Wigner crystals

It is generally believed that quantum fluctuations collaborate with thermal fluctuations, effectively reducing transition temperatures (e.g. for melting of charge order). We show that this is not always the case and that the interplay between quantum and thermal fluctuations can be competitive. We find excellent motivation for addressing this thanks to the discovery of correlated insulating "generalized Wigner crystal" (GWC) states in hetero-bilayer transition metal dichalcogenide (WS$_2$/WSe$_2$) moir\'e systems [Y. Xu, et al., Nature 587, 214-218 (2020)]. We account for the impact of quantum effects on the melting temperature of GWCs, carrying out finite temperature Lanczos calculations on an extended Hubbard model on the triangular lattice (both with a double-gate screened potential, and the nearest neighbor model) for multiple electron densities. We show that quantum effects capture the shift relative to the classical estimates, which in some cases are more than 50 percent off from the experimental values. Then building on these numerical findings, we provide a qualitative picture that clarifies that while quantum melting of GWC (by increasing the bandwidth) naturally softens the ground state order parameter, it does not always decrease the melting temperature; conversely it can increase it. To do so we employ a finite temperature perturbation theory, treating the kinetic energy perturbatively on top of a classical Wigner crystal. Our predictions should be observable in future experiments where the bandwidth can be tuned.

cond-mat.str-el↗

arXiv2026

Learning with Boolean threshold functions

We develop a method for training neural networks on Boolean data in which the values at all nodes are strictly $\pm 1$, and the resulting models are typically equivalent to networks whose nonzero weights are also $\pm 1$. The method replaces loss minimization with a nonconvex constraint formulation. Each node implements a Boolean threshold function (BTF), and training is expressed through a divide-and-concur decomposition into two complementary constraints: one enforces local BTF consistency between inputs, weights, and output; the other imposes architectural concurrence, equating neuron outputs with downstream inputs and enforcing weight equality across training-data instantiations of the network. The reflect-reflect-relax (RRR) projection algorithm is used to reconcile these constraints. Each BTF constraint includes a lower bound on the margin. When this bound is sufficiently large, the learned representations are provably sparse and equivalent to networks composed of simple logical gates with $\pm 1$ weights. Across a range of tasks -- including multiplier-circuit discovery, binary autoencoding, logic-network inference, and cellular automata learning -- the method achieves exact solutions or strong generalization in regimes where standard gradient-based methods struggle. These results demonstrate that projection-based constraint satisfaction provides a viable and conceptually distinct foundation for learning in discrete neural systems, with implications for interpretability and efficient inference.

cs.LG↗

arXiv2025

Resolving Tangling in Multi-Conformer Refinement via Iterative Projections

The advent of advanced crystallographic techniques has shifted structural biology from static, single-conformer models toward probing protein dynamics. Extracting cooperative motions from temporally and spatially averaged electron density maps requires both high-resolution data and refinement algorithms capable of handling conformational heterogeneity. However, current refinement protocols often fail due to the tangling phenomenon, in which conformational states become improperly intertwined during optimization. Here, we present an automated refinement methodology based on iterative projections within the divide-and-concur framework. This approach enables seamless integration of geometric constraints with experimental density constraints derived from observed scattering amplitudes. By allowing each atom to satisfy density constraints independently, we show that this framework effectively circumvents tangling artifacts and achieves robust refinement performance, even for models initialized with R-factors as high as 12%. Just as iterative projections revolutionized phase retrieval in crystallography, we demonstrate that they can also address the optimization challenges in multi-conformational refinement. This work establishes a computational foundation for advancing crystallographic methodologies to resolve conformational heterogeneity and ultimately capture protein dynamics at atomic resolution.

q-bio.QM↗

arXiv2024

Itinerant Ferromagnetism from One-Dimensional Mobility

We propose a universal kinetic mechanism for a half-metallic ferromagnet -- a metallic state with full spin polarization -- arising from strong on-site Coulomb repulsions between particles that exhibit constrained one-dimensional (1D) dynamics. We illustrate the mechanism in the context of a solvable model on a Lieb lattice in which doped electrons have 1D mobility. Such 1D motion is shown to induce only multi-spin ring exchanges of even parity, which mediate ferromagnetism and result in a unique half-metallic ground state. In contrast to the Nagaoka mechanism of ferromagnetism, this result pertains to any doped electron density in the {\it thermodynamic} limit. We explore various microscopic routes to such (approximate) 1D dynamics, highlighting two examples: doped holes in the strong-coupling limit of the Emery model and vacancies in a two-dimensional Wigner crystal. Finally, we demonstrate an intriguing exact equivalence between the bosonic and fermionic versions of these models, which implies a novel mechanism for the conjectured Bose metallic phase.

cond-mat.str-el↗

arXiv2023

How densely can spheres be packed with moderate effort in high dimensions?

We generate non-lattice packings of spheres in up to 22 dimensions using the geometrical constraint satisfaction algorithm RRR. Our aggregated data suggest that it is easy to double the density of Ball's lower bound, and more tentatively, that the exponential decay rate of the density can be improved relative to Minkowski's longstanding 1/2.

math.MG↗

arXiv2023

A logical word embedding for learning grammar

We introduce the logical grammar emdebbing (LGE), a model inspired by pregroup grammars and categorial grammars to enable unsupervised inference of lexical categories and syntactic rules from a corpus of text. LGE produces comprehensible output summarizing its inferences, has a completely transparent process for producing novel sentences, and can learn from as few as a hundred sentences.

cs.CL↗

arXiv2023

A transparent approach to data representation

We use a binary attribute representation (BAR) model to describe a data set of Netflix viewers' ratings of movies. We classify the viewers with discrete bits rather than continuous parameters, which makes the representation compact and transparent. The attributes are easy to interpret, and we need far fewer attributes than similar methods do to achieve the same level of error. We also take advantage of the nonuniform distribution of ratings among the movies in the data set to train on a small selection of movies without compromising performance on the rest of the movies.

cs.LG↗

arXiv2022

Learning grammar with a divide-and-concur neural network

We implement a divide-and-concur iterative projection approach to context-free grammar inference. Unlike most state-of-the-art models of natural language processing, our method requires a relatively small number of discrete parameters, making the inferred grammar directly interpretable -- one can read off from a solution how to construct grammatically valid sentences. Another advantage of our approach is the ability to infer meaningful grammatical rules from just a few sentences, compared to the hundreds of gigabytes of training data many other models employ. We demonstrate several ways of applying our approach: classifying words and inferring a grammar from scratch, taking an existing grammar and refining its categories and rules, and taking an existing grammar and expanding its lexicon as it encounters new words in new data.

cs.CL↗

arXiv2021

Avoiding Traps in Nonconvex Problems

Iterative projection methods may become trapped at non-solutions when the constraint sets are nonconvex. Two kinds of parameters are available to help avoid this behavior and this study gives examples of both. The first kind of parameter, called a hyperparameter, includes any kind of parameter that appears in the definition of the iteration rule itself. The second kind comprises metric parameters in the definition of the constraint sets, a feature that arises when the problem to be solved has two or more kinds of variables. Through examples we show the importance of properly tuning both kinds of parameters and offer heuristic interpretations of the observed behavior.

math.OC↗

arXiv2021

Charge-order-enhanced capacitance in semiconductor moir\'e superlattices

Van der Waals moir\'e materials have emerged as a highly controllable platform to study the electronic correlation phenomena. In particular, robust correlated insulating states have recently been discovered at both integer and fractional filling factors of the semiconductor moir\'e systems. Here, we reveal the thermodynamic properties of these states by measuring the gate capacitance of MoSe2/WS2 moir\'e superlattices. We observe a series of incompressible states for filling factor 0 - 8 and anomalously large capacitance (nearly 60% above the device's geometrical capacitance) in the intervening compressible regions. The anomalously large capacitance is most pronounced at small filling factor, below the melting temperature of the charge-ordered states, and for small sample-gate separation. It is a manifestation of the device-geometry-dependent Coulomb interaction between electrons and phase mixing of the charge-ordered states. We have further extracted the thermodynamic gap of the correlated insulating states and the entropy of the capacitive device. The results not only establish capacitance as a powerful probe of the correlated states in semiconductor moir\'e systems, but also demonstrate control of the extended Coulomb interaction in these materials via sample-gate coupling.

cond-mat.str-el↗

arXiv2020

Reconstructing cellular automata rules from observations at nonconsecutive times

Recent experiments by Springer and Kenyon have shown that a deep neural network can be trained to predict the action of $t$ steps of Conway's Game of Life automaton given millions of examples of this action on random initial states. However, training was never completely successful for $t>1$, and even when successful, a reconstruction of the elementary rule ($t=1$) from $t>1$ data is not within the scope of what the neural network can deliver. We describe an alternative network-like method, based on constraint projections, where this is possible. From a single data item this method perfectly reconstructs not just the automaton rule but also the states in the time steps it did not see. For a unique reconstruction, the size of the initial state need only be large enough that it and the $t-1$ states it evolves into contain all possible automaton input patterns. We demonstrate the method on 1D binary cellular automata that take inputs from $n$ adjacent cells. The unknown rules in our experiments are not restricted to simple rules derived from a few linear functions on the inputs (as in Game of Life), but include all $2^{2^n}$ possible rules on $n$ inputs. Our results extend to $n=6$, for which exhaustive rule-search is not feasible. By relaxing translational symmetry in space and also time, our method is attractive as a platform for the learning of binary data, since the discreteness of the variables does not pose the same challenge it does for gradient-based methods.

nlin.CG↗

arXiv2020

Abundance of correlated insulating states at fractional fillings of WSe$_{2}$/WS$_{2}$ moir\'e superlattices

Quantum particles on a lattice with competing long-range interactions are ubiquitous in physics. Transition metal oxides, layered molecular crystals and trapped ion arrays are a few examples out of many. In the strongly interacting regime, these systems often exhibit a rich variety of quantum many-body ground states that challenge theory. The emergence of transition metal dichalcogenide moir\'e heterostructures provides a highly controllable platform to study long-range electronic correlations. Here we report an observation of nearly two-dozen correlated insulating states at fractional fillings of a WSe$_{2}$/WS$_{2}$ moir\'e heterostructure. The discovery is enabled by a new optical sensing technique that is built on the sensitivity to dielectric environment of the exciton excited states in single-layer semiconductor WSe$_{2}$. The cascade of insulating states exhibits an energy ordering which is nearly symmetric about filling factor of half electron (or hole) per superlattice site. We propose a series of charge-ordered states at commensurate filling fractions that range from generalized Wigner crystals to charge density waves. Our study lays the groundwork for utilizing moir\'e superlattices to simulate a wealth of quantum many-body problems that are described by the two-dimensional t-V model or spin models with long-range charge-charge and exchange interactions.

cond-mat.str-el↗

arXiv2020

Chemistry of the spin-1/2 kagome Heisenberg antiferromagnet

We believe that a necessary first step in understanding the ground state properties of the spin-${\scriptstyle\frac{1}{2}}$ kagome Heisenberg antiferromagnet is a better understanding of this model's very large number of low energy singlet states. A description of the low energy states that is both accurate and amenable for numerical work may ultimately prove to have greater value than knowing only what these properties are, in particular when these turn on the delicate balance of many small energies. We demonstrate how this program would be implemented using the basis of spin-singlet dimerized states, though other bases that have been proposed may serve the same purpose. The quality of a basis is evaluated by its participation in all the low energy singlets, not just the ground state. From an experimental perspective, and again in light of the small energy scales involved, methods that can deliver all the low energy states promise more robust predictions than methods that only refine a fraction of these states.

cond-mat.str-el↗

arXiv2020

Parity non-conservation in a condensed matter system

The nuclear spin of a He$^3$ quasiparticle dissolved in superfluid He$^4$ sees an apparent magnetic field proportional to the Fermi coupling constant, the superfluid condensate density, and the electron current at the He$^3$ nucleus. Whereas the direction of the current must be parallel to the quasiparticle momentum, calculating its magnitude presents an interesting theoretical challenge because it vanishes in the Born-Oppenheimer approximation. We find the effect is too small to be observed and present our results in the hope others will be inspired to look for similar effects in other systems.

cond-mat.other↗

arXiv2019

Learning Without Loss

We explore a new approach for training neural networks where all loss functions are replaced by hard constraints. The same approach is very successful in phase retrieval, where signals are reconstructed from magnitude constraints and general characteristics (sparsity, support, etc.). Instead of taking gradient steps, the optimizer in the constraint based approach, called relaxed-reflect-reflect (RRR), derives its steps from projections to local constraints. In neural networks one such projection makes the minimal modification to the inputs $x$, the associated weights $w$, and the pre-activation value $y$ at each neuron, to satisfy the equation $x\cdot w=y$. These projections, along with a host of other local projections (constraining pre- and post-activations, etc.) can be partitioned into two sets such that all the projections in each set can be applied concurrently, across the network and across all data in the training batch. This partitioning into two sets is analogous to the situation in phase retrieval and the setting for which the general purpose RRR optimizer was designed. Owing to the novelty of the method, this paper also serves as a self-contained tutorial. Starting with a single-layer network that performs non-negative matrix factorization, and concluding with a generative model comprising an autoencoder and classifier, all applications and their implementations by projections are described in complete detail. Although the new approach has the potential to extend the scope of neural networks (e.g. by defining activation not through functions but constraint sets), most of the featured models are standard to allow comparison with stochastic gradient descent.

cs.LG↗

arXiv2018

Monotone Learning with Rectified Wire Networks

We introduce a new neural network model, together with a tractable and monotone online learning algorithm. Our model describes feed-forward networks for classification, with one output node for each class. The only nonlinear operation is rectification using a ReLU function with a bias. However, there is a rectifier on every edge rather than at the nodes of the network. There are also weights, but these are positive, static, and associated with the nodes. Our "rectified wire" networks are able to represent arbitrary Boolean functions. Only the bias parameters, on the edges of the network, are learned. Another departure in our approach, from standard neural networks, is that the loss function is replaced by a constraint. This constraint is simply that the value of the output node associated with the correct class should be zero. Our model has the property that the exact norm-minimizing parameter update, required to correctly classify a training item, is the solution to a quadratic program that can be computed with a few passes through the network. We demonstrate a training algorithm using this update, called sequential deactivation (SDA), on MNIST and some synthetic datasets. Upon adopting a natural choice for the nodal weights, SDA has no hyperparameters other than those describing the network structure. Our experiments explore behavior with respect to network size and depth in a family of sparse expander networks.

cs.LG↗

arXiv2018

Deep sub-{\AA}ngstrom imaging of 2D materials with a high dynamic range detector

Aberration-corrected optics have made electron microscopy at atomic-resolution a widespread and often essential tool for nanocharacterization. Image resolution is dominated by beam energy and the numerical aperture of the lens ({\alpha}), with state-of-the-art reaching ~0.47 {\AA} at 300 keV. Two-dimensional materials are imaged at lower beam energies to avoid knock-on damage, limiting spatial resolution to ~1 {\AA}. Here, by combining a new electron microscope pixel array detector with the dynamic range to record the complete distribution of transmitted electrons and full-field ptychography to recover phase information from the full phase space, we increased the spatial resolution well beyond the traditional lens limitations. At 80 keV beam energy, our ptychographic reconstructions significantly improved image contrast of single-atom defects in MoS2, reaching an information limit close to 5{\alpha}, corresponding to a 0.39 {\AA} Abbe resolution, at the same dose and imaging conditions where conventional imaging modes reach only 0.98 {\AA}.

cond-mat.mtrl-sci↗