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We use algebraic methods in statistical mechanics to represent a multi-parameter class of polynomials in severable variables as partition functions of a new family of solvable lattice models. The class of polynomials, defined by A.N. Kirillov, is derived from the largest class of divided difference operators satisfying the braid relations of Cartan type $A$. It includes as specializations Schubert, Grothendieck, and dual-Grothendieck polynomials among others. In particular, our results prove positivity conjectures of Kirillov for the subfamily of Hecke--Grothendieck polynomials, while the larger family is shown to exhibit rare instances of negative coefficients.

Logical clocks are a fundamental tool to establish causal ordering of events in a distributed system. They have been applied in weakly consistent storage systems, causally ordered broadcast, distributed snapshots, deadlock detection, and distributed system debugging. However, prior logical clock constructs fail to work in an open network with Byzantine participants. In this work, we present Chrono, a novel logical clock system that targets such challenging environment. We first redefine causality properties among distributed processes under the Byzantine failure model. To enforce these properties, Chrono defines a new validator abstraction for building fault-tolerant logical clocks. Furthermore, our validator abstraction is customizable: Chrono includes multiple backend implementations for the abstraction, each with different security-performance trade-offs. We have applied Chrono to build two decentralized applications, a mutual exclusive service and a weakly consistent key-value store. Chrono adds only marginal overhead compared to systems that tolerate no Byzantine faults. It also out-performs state-of-the-art BFT total order protocols by significant margins.

After last year's successful BabyLM Challenge, the competition will be hosted again in 2024/2025. The overarching goals of the challenge remain the same; however, some of the competition rules will be different. The big changes for this year's competition are as follows: First, we replace the loose track with a paper track, which allows (for example) non-model-based submissions, novel cognitively-inspired benchmarks, or analysis techniques. Second, we are relaxing the rules around pretraining data, and will now allow participants to construct their own datasets provided they stay within the 100M-word or 10M-word budget. Third, we introduce a multimodal vision-and-language track, and will release a corpus of 50% text-only and 50% image-text multimodal data as a starting point for LM model training. The purpose of this CfP is to provide rules for this year's challenge, explain these rule changes and their rationale in greater detail, give a timeline of this year's competition, and provide answers to frequently asked questions from last year's challenge.

Humans extract useful abstractions of the world from noisy sensory data. Serial reproduction allows us to study how people construe the world through a paradigm similar to the game of telephone, where one person observes a stimulus and reproduces it for the next to form a chain of reproductions. Past serial reproduction experiments typically employ a single sensory modality, but humans often communicate abstractions of the world to each other through language. To investigate the effect language on the formation of abstractions, we implement a novel multimodal serial reproduction framework by asking people who receive a visual stimulus to reproduce it in a linguistic format, and vice versa. We ran unimodal and multimodal chains with both humans and GPT-4 and find that adding language as a modality has a larger effect on human reproductions than GPT-4's. This suggests human visual and linguistic representations are more dissociable than those of GPT-4.

The impact of randomness on model training is poorly understood. How do differences in data order and initialization actually manifest in the model, such that some training runs outperform others or converge faster? Furthermore, how can we interpret the resulting training dynamics and the phase transitions that characterize different trajectories? To understand the effect of randomness on the dynamics and outcomes of neural network training, we train models multiple times with different random seeds and compute a variety of metrics throughout training, such as the $L_2$ norm, mean, and variance of the neural network's weights. We then fit a hidden Markov model (HMM) over the resulting sequences of metrics. The HMM represents training as a stochastic process of transitions between latent states, providing an intuitive overview of significant changes during training. Using our method, we produce a low-dimensional, discrete representation of training dynamics on grokking tasks, image classification, and masked language modeling. We use the HMM representation to study phase transitions and identify latent "detour" states that slow down convergence., Comment: Accepted at TMLR 2023. Updated Jan 19, 2024 with erratum

The strongly correlated material FeSi exhibits several unusual thermal, magnetic, and structural properties under varying pressure-temperature (P-T) conditions. It is a potential thermoelectric alloy and a materials of several geological implications as a possible constituent at the Earth's core mantle boundary (CMB). The phase transition behavior and lattice dynamics of FeSi under different P-T conditions remain elusive. A previous theoretical work predicted a pressure induced B20-B2 transition at ambient temperature, yet the transition is only observed at high P-T conditions in the experiments. Furthermore, the closing of the electronic gap due to a dramatic renormalization of the electronic structure and phonon anomalies has been reported based on density function calculations. In this study we have performed high pressure powder diffraction and Nuclear Resonant Inelastic X-ray Scattering (NRIXS) measurements up to 120 GPa to understand the phase stability and the lattice dynamics. Our study shows evidence for a nonhydrostatic stress induced B20-B2 transition in FeSi around 36 GPa for the first time. The Fe partial phonon density of states (PDOS) and thermal parameters were derived from NRIXS up to 120 GPa with the density function theoretical (DFT) calculations. These calculations further predict and are consistent with pressure-induced metallization and band gap closing around 12 GPa., Comment: 8 pages, 6 figures and supplementary information

Solid-liquid phase transitions are fundamental physical processes, but atomically-resolved microscopy has yet to capture both the solid and liquid dynamics for such a transition. We have developed a new technique for controlling the melting and freezing of 2D molecular layers on a graphene field-effect transistor (FET) that allows us to image phase transition dynamics via atomically-resolved scanning tunneling microscopy. Back-gate voltages applied to a F4TCNQ-decorated graphene FET induce reversible transitions between a charge-neutral solid phase and a negatively charged liquid phase. Nonequilibrium molecular melting dynamics are visualized by rapidly heating the graphene surface with electrical current and imaging the resulting evolution toward new equilibrium states. An analytical model has been developed that explains the observed equilibrium mixed-state phases based on spectroscopic measurement of both solid and liquid molecular energy levels. Observed non-equilibrium melting dynamics are consistent with Monte Carlo simulations.

Video-based dialog task is a challenging multimodal learning task that has received increasing attention over the past few years with state-of-the-art obtaining new performance records. This progress is largely powered by the adaptation of the more powerful transformer-based language encoders. Despite this progress, existing approaches do not effectively utilize visual features to help solve tasks. Recent studies show that state-of-the-art models are biased toward textual information rather than visual cues. In order to better leverage the available visual information, this study proposes a new framework that combines 3D-CNN network and transformer-based networks into a single visual encoder to extract more robust semantic representations from videos. The visual encoder is jointly trained end-to-end with other input modalities such as text and audio. Experiments on the AVSD task show significant improvement over baselines in both generative and retrieval tasks.

The design of man-made objects is dominated by computer aided design (CAD) tools. Assisting design with data-driven machine learning methods is hampered by lack of labeled data in CAD's native format; the parametric boundary representation (B-Rep). Several data sets of mechanical parts in B-Rep format have recently been released for machine learning research. However, large scale databases are largely unlabeled, and labeled datasets are small. Additionally, task specific label sets are rare, and costly to annotate. This work proposes to leverage unlabeled CAD geometry on supervised learning tasks. We learn a novel, hybrid implicit/explicit surface representation for B-Rep geometry, and show that this pre-training significantly improves few-shot learning performance and also achieves state-of-the-art performance on several existing B-Rep benchmarks.

Strong inductive biases give humans the ability to quickly learn to perform a variety of tasks. Although meta-learning is a method to endow neural networks with useful inductive biases, agents trained by meta-learning may sometimes acquire very different strategies from humans. We show that co-training these agents on predicting representations from natural language task descriptions and programs induced to generate such tasks guides them toward more human-like inductive biases. Human-generated language descriptions and program induction models that add new learned primitives both contain abstract concepts that can compress description length. Co-training on these representations result in more human-like behavior in downstream meta-reinforcement learning agents than less abstract controls (synthetic language descriptions, program induction without learned primitives), suggesting that the abstraction supported by these representations is key., Comment: In Proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS 2022), winner of Outstanding Paper Award

While safe reinforcement learning (RL) holds great promise for many practical applications like robotics or autonomous cars, current approaches require specifying constraints in mathematical form. Such specifications demand domain expertise, limiting the adoption of safe RL. In this paper, we propose learning to interpret natural language constraints for safe RL. To this end, we first introduce HazardWorld, a new multi-task benchmark that requires an agent to optimize reward while not violating constraints specified in free-form text. We then develop an agent with a modular architecture that can interpret and adhere to such textual constraints while learning new tasks. Our model consists of (1) a constraint interpreter that encodes textual constraints into spatial and temporal representations of forbidden states, and (2) a policy network that uses these representations to produce a policy achieving minimal constraint violations during training. Across different domains in HazardWorld, we show that our method achieves higher rewards (up to11x) and fewer constraint violations (by 1.8x) compared to existing approaches. However, in terms of absolute performance, HazardWorld still poses significant challenges for agents to learn efficiently, motivating the need for future work., Comment: The first two authors contributed equally

We report on the impact of magnetoelastic coupling on the magnetocaloric properties of LaFe$_{11.4}$Si$_{1.6}$H$_{1.6}$ in terms of the vibrational density of states, which we determined with $^{57}$Fe nuclear resonant inelastic X-ray scattering measurements and with density-functional-theory based first-principles calculations in the ferromagnetic low-temperature and paramagnetic high-temperature phase. In experiments and calculations, we observe pronounced differences in the shape of the Fe-partial VDOS between non-hydrogenated and hydrogenated samples. This shows that hydrogen does not only shift the temperature of the first-order phase transition, but also affects the elastic response of the Fe-subsystem significantly. In turn, the anomalous redshift of the Fe VDOS, observed by going to the low-volume PM phase, survives hydrogenation. As a consequence, the change in the Fe specific vibrational entropy $\Delta S_\mathrm{lat}$ across the phase transition has the same sign as the magnetic and electronic contribution. DFT calculations show that the same mechanism, which is a consequence of the itinerant electron metamagnetism associated with the Fe subsystem, is effective in both the hydrogenated and he hydrogen-free compounds. Although reduced by 50 % as compared to the hydrogen-free system, the measured change $\Delta S_\mathrm{lat}$ of 3.2\pm1.9 J/kgK across the FM to PM transition contributes with 35 % significantly and cooperatively to the total isothermal entropy change $\Delta S_\mathrm{iso}$. Hydrogenation is observed to induce an overall blueshift of the Fe-VDOS with respect to the H-free compound; this effect, together with the enhanced Debye temperature observed, is a fingerprint of the hardening of the Fe sublattice by hydrogen incorporation. In addition, the mean Debye velocity of sound of LaFe$_{11.4}$Si$_{1.6}$H$_{1.6}$ was determined from the NRIXS and the DFT data., Comment: 13 pages 9 figures

We combine $^{57}$Fe M\"ossbauer spectroscopy and $^{57}$Fe nuclear resonant inelastic x-ray scattering (NRIXS) in nanoscale polycrystalline [bcc-$^{57}$Fe/MgO] multilayers with various Fe layer thicknesses and layer-resolved density-functional-theory (DFT) based first-principles calculations of a (001)-oriented [Fe(8 ML)/MgO(8 ML)](001) heterostructure to unravel the interface-related atomic vibrational properties of a multilayer system. In theory and experiment, we observe consistently enhanced hyperfine magnetic fields compared to bulk which are associated with the Fe/MgO interface layers. NRIXS and DFT both reveal a strong reduction of the longitudinal acoustic (LA) phonon peak in combination with an enhancement of the low-energy vibrational density of states (VDOS) suggesting that the presence of interfaces and the associated increase in the layer-resolved magnetic moments results in drastic changes in the Fe-partial VDOS. From the experimental and calculated VDOS, vibrational thermodynamic properties have been determined as a function of Fe thickness and are found to be in excellent agreement., Comment: 14 pages, 6 figures

BaFe$_2$S$_3$ is a quasi one-dimensional Mott insulator that orders antiferromagnetically below 117(5)\,K. The application of pressure induces a transition to a metallic state, and superconductivity emerges. The evolution of the magnetic behavior on increasing pressure has up to now been either studied indirectly by means of transport measurements, or by using local magnetic probes only in the low pressure region. Here, we investigate the magnetic properties of BaFe$_2$S$_3$ up to 9.9\,GPa by means of synchrotron $^{57}$Fe M\"ossbauer spectroscopy experiments, providing the first local magnetic phase diagram. The magnetic ordering temperature increases up to 185(5) K at 7.5\,GPa, and is fully suppressed at 9.9\,GPa. The low-temperature magnetic hyperfine field is continuously reduced from 12.9 to 10.3\,T between 1.4 and 9.1\,GPa, followed by a sudden drop to zero at 9.9\,GPa indicating a first-order phase transition. The pressure dependence of the magnetic order in BaFe$_2$S$_3$ can be qualitatively explained by a combination of a bandwidth-controlled insulator-metal transition as well as a pressure enhanced exchange interaction between Fe-atoms and Fe 3\textit{d}-S 3\textit{p} hybridization., Comment: 4 pages, 4 figures

Material properties depend sensitively on the atomic arrangements and atomic bonding, but these are notoriously difficult to measure in nanosized atomic clusters due to the small size of the objects and the challenge of obtaining bulk samples of identical clusters. Here we have combined the recent ability to make gram quantities of identical semiconductor quantum-dot nanoparticles with the ability to measure lattice dynamics on small sample quantities of hydrogenated materials using high energy resolution inelastic x-ray scattering (HERIX), to measure the size-dependence of the phonon density of states (PDOS) in CdSe quantum dots. The fact that we have atomically precise structural models for these nanoparticles allows the calculation of the PDOS using Density Functional Theory (DFT), providing both experimental and theoretical confirmations of the important role that the inertia of the surface capping species plays on determining the lattice dynamics., Comment: 9 pages, 6 figures

We investigated LaFeAsO single crystals by means of synchrotron M\"ossbauer spectroscopy under pressures up to 7.5 GPa and down to 13 K and provide a microscopic phase diagram. We found a continuous suppression of the magnetic hyperfine field with increasing pressure and it completely vanishes at $\sim$ 7.5 GPa which is in contrast to the behavior in polycrystalline samples where the magnetic order vanishes at $\sim$ 20 GPa. The different behavior of the polycrystalline samples might be due to As-vacancies. Our results are in qualitative agreement with density functional theory calculations where a reduction of the magnetic moment with increasing pressure was found. We found that among different samples at ambient pressure the magnetic phase transition temperature as well as the low-temperature magnetic hyperfine field decrease with increasing unit cell volume., Comment: 6 pages, 6 figures

We present a detailed investigation of the electronic properties of CeFeAsO under chemical (As by P substitution) and hydrostatic pressure by means of in-house and synchrotron M\"ossbauer spectroscopy. The Fe magnetism is suppressed due to both pressures and no magnetic order was observed above a P-substitution level of 40% or 5.2 GPa hydrostatic pressure. We compared both pressures and found that the isovalent As by P substitution change the crystallographic and electronic properties differently than hydrostatic pressure., Comment: supplement is included in the pdf file