We show using diagramtic arguments that in some (but not all) cases, the temperature dependent part of chiral vortical effect coefficient is independent coupling constant. An interpretation this result terms quantization effective 3 dimensional Chern-Simons theory also given. In language 3D, dimensionally reduced theory, value related to formula ∑ = 1 ∞ n − 1/12 . presence dynamical gauge fields, CVE protected from renormalization, even large N limit.

We introduce Continual Learning via Neural Pruning (CLNP), a new method aimed at lifelong learning in fixed capacity models based on neuronal model sparsification. In this method, subsequent tasks are trained using the inactive neurons and filters of sparsified network cause zero deterioration to performance previous tasks. order deal with possible compromise between sparsity performance, we formalize incorporate concept graceful forgetting: idea that it is preferable suffer small amount...

ABSTRACT We present AstroCLIP, a single, versatile model that can embed both galaxy images and spectra into shared, physically meaningful latent space. These embeddings then be used – without any fine-tuning for variety of downstream tasks including (1) accurate in-modality cross-modality semantic similarity search, (2) photometric redshift estimation, (3) property estimation from spectra, (4) morphology classification. Our approach to implementing AstroCLIP consists two parts. First, we...

We show that matching anomalies under large gauge transformations and diffeomorphisms can explain the appearance non-renormalization of couplings in effective field theory. focus on thermal theory, where we argue certain unusual Chern-Simons is a consequence global anomalies. As an example, mixed anomaly four dimensions fixes chiral vortical effect coefficient (up to overall additive factor). This experimentally measurable prediction from anomaly. For situations, propose simpler method for...

Fractional quantum Hall liquids exhibit a rich set of excitations, the lowest energy which are magnetorotons with dispersion minima at finite momentum. We propose theory on plateaux near half filling, namely, filling fractions ν=N/(2N+1) large N. The involves an infinite number bosonic fields arising from bosonizing fluctuations shape composite Fermi surface. At zero momentum there O(N) neutral each carrying well-defined spin that runs integer values 2,3,…. mixing modes nonzero q leads to...

We study fractional quantum Hall states at filling fractions in the Jain sequences using framework of composite Dirac fermions. Synthesizing previous work, we write down an effective field theory consistent with all symmetry requirements, including Galilean invariance and particle-hole symmetry. Employing a Fermi liquid description, demonstrate appearance Girvin--Macdonlald--Platzman algebra compute dispersion relation neutral excitations various response functions. Our results satisfy...

We introduce multiple physics pretraining (MPP), an autoregressive task-agnostic approach for physical surrogate modeling. MPP involves training large models to predict the dynamics of heterogeneous systems simultaneously by learning features that are broadly useful across diverse tasks. In order learn effectively in this setting, we a shared embedding and normalization strategy projects fields into single space. validate efficacy our on both downstream tasks over broad fluid...

We consider gapped fractional quantum Hall states on the lowest Landau level when Coulomb energy is much smaller than cyclotron energy. introduce two spectral densities, ρ T (ω) and $$ {\overline{\rho}}_T\left(\omega \right) , which are proportional to probabilities of absorption circularly polarized gravitons by system. prove three sum rules relating these densities with shift \mathcal{S} q 4 coefficient static structure factor S 4, high-frequency shear modulus ground state μ ∞, precisely...

Motivated by the observation of fractional quantum Hall effect in graphene, we consider effective field theory relativistic states. We find that, beside Chern-Simons term, action also contains a term topological nature, which couples electromagnetic with topologically conserved current 2 + 1 dimensional fluid. In contrast to new involves spacetime metric nontrivial way. extract predictions for linear and gravitational responses. For states at zeroth Landau level, additional holomorphic...

We investigate the relationship between Hall viscosity, spin density and response to geometric torsion. For most general effective action for relativistic gapped systems, presence of non-universal terms implies that there is no torsion viscosity. also consider free non-relativistic microscopic actions again verify existence analogous couplings. Explicit examples demonstrate unrelated both viscosity density. argue theories must have vanishing in Lorentz invariant vacuums.

We introduce two methods for estimating the density matrix a quantum system: Quantum Maximum Likelihood and Variational Inference. In these methods, we construct variational family to model of mixed state. also flows, analog normalizing which can be used increase expressivity this family. The eigenstates eigenvalues interest are then derived by optimizing an appropriate loss function. approach is qualitatively different than traditional lattice techniques that rely on time dependence...

For a spacetime of odd dimensions endowed with unit vector field, we introduce new topological current that is identically conserved and whose charge equal to the Euler character even dimensional spacelike foliations. The existence this allows us Chern-Simons-type terms in effective field theories describing relativistic quantum Hall states (2 + 1) superfluids. Using theory, calculate various correlation functions identify transport coefficients. In case, provides natural generalization...

Cortical pyramidal neurons receive inputs from multiple distinct neural populations and integrate these in separate dendritic compartments. We explore the possibility that cortical microcircuits implement canonical correlation analysis (CCA), an unsupervised learning method projects onto a common subspace so as to maximize correlations between projections. To this end, we seek multichannel CCA algorithm can be implemented biologically plausible network. For biological plausibility, require...

Large Language Models have not yet been broadly adapted for the analysis of scientific datasets due in part to unique difficulties tokenizing numbers. We propose xVal, a numerical encoding scheme that represents any real number using just single token. xVal given by scaling dedicated embedding vector value. Combined with modified number-inference approach, this strategy renders model end-to-end continuous when considered as map from numbers input string those output string. This leads an...

This study derives an online algorithm implementable in a neural network with local updating rules to solve specific class of complex tasks establishing precise relationship between realistic synaptic learning and underlying computational principles that guide their design.

We present AstroCLIP, a single, versatile model that can embed both galaxy images and spectra into shared, physically meaningful latent space. These embeddings then be used - without any fine-tuning for variety of downstream tasks including (1) accurate in-modality cross-modality semantic similarity search, (2) photometric redshift estimation, (3) property estimation from spectra, (4) morphology classification. Our approach to implementing AstroCLIP consists two parts. First, we separately...

Predictive coding has emerged as an influential normative model of neural computation, with numerous extensions and applications. As such, much effort been put into mapping PC faithfully onto the cortex, but there are issues that remain unresolved or controversial. In particular, current implementations often involve separate value error neurons require symmetric forward backward weights across different brain regions. These features have not experimentally confirmed. this work, we show...

The backpropagation algorithm is an invaluable tool for training artificial neural networks; however, because of a weight sharing requirement, it does not provide plausible model brain function. Here, in the context two-layer network, we derive network which avoids this problem by requiring explicit error computation and backpropagation. Furthermore, our maps onto that bears remarkable resemblance to connectivity structure learning rules cortex. We find empirically performs comparably...

A major problem in motor control is understanding how the brain plans and executes proper movements face of delayed noisy stimuli. prominent framework for addressing such problems Optimal Feedback Control (OFC). OFC generates actions that optimize behaviorally relevant criteria by integrating sensory stimuli predictions an internal model using Kalman filter or its extensions. However, a satisfactory neural filtering lacking because existing proposals have following limitations: not...

Finding informative low-dimensional representations that can be computed efficiently in large datasets is an important problem data analysis. Recently, contrastive Principal Component Analysis (cPCA) was proposed as a more generalization of PCA takes advantage learning. However, the performance cPCA sensitive to hyper-parameter choice and there currently no online algorithm for implementing cPCA. Here, we introduce modified method, which denote <sup...

To generate actions in the face of physiological delays, brain must predict future. Here we explore how prediction may lie at core function by considering a neuron predicting future scalar time series input. Assuming that dynamics lag vector (a composed several consecutive elements series) are locally linear, Normal Mode Decomposition decomposes into independently evolving (eigen-)modes allowing for straightforward prediction. We propose learns top mode and projects its input onto associated...