Miracle-dz/UniFAR-FTUs huggingface dataset:
Multilayered Atomic Relaxation in van der Waals Heterostructures

When two-dimensional van der Waals materials are stacked to build heterostructures, moir\'e patterns emerge from twisted interfaces or from mismatch in lattice constant of individual layers. Relaxation of the atomic positions is a direct, generic consequence of the moir\'e pattern, with many implications for the physical properties. Moir\'e driven atomic relaxation may be naively thought to be restricted to the interfacial layers and thus irrelevant for multi-layered heterostructures. However, we provide experimental evidence for the importance of the three dimensional nature of the relaxation in two types of van der Waals heterostructures: First, in multi-layer graphene twisted on graphite at small twist angles ($\theta\approx0.14^\circ$) we observe propagation of relaxation domains even beyond 18 graphene layers. Second, we show how for multi-layer PdTe$_2$ on Bi$_2$Se$_3$ the moir\'e lattice constant depends on the number of PdTe$_2$ layers. Motivated by the experimental findings, we developed a continuum approach to model multi-layered relaxation processes based on the generalized stacking fault energy functional given by ab-initio simulations. Leveraging the continuum property of the approach enables us to access large scale regimes and achieve agreement with our experimental data for both systems. Furthermore it is well known that the electronic structure of graphene sensitively depends on local lattice deformations. Therefore we study the impact of multi-layered relaxation on the local density of states of the twisted graphitic system. We identify measurable implications for the system, experimentally accessible by scanning tunneling microscopy. Our multi-layered relaxation approach is not restricted to the discussed systems, and can be used to uncover the impact of an interfacial defect on various layered systems of interest.
ProcTHOR: Large-Scale Embodied AI Using Procedural Generation

Massive datasets and high-capacity models have driven many recent advancements in computer vision and natural language understanding. This work presents a platform to enable similar success stories in Embodied AI. We propose ProcTHOR, a framework for procedural generation of Embodied AI environments. ProcTHOR enables us to sample arbitrarily large datasets of diverse, interactive, customizable, and performant virtual environments to train and evaluate embodied agents across navigation, interaction, and manipulation tasks. We demonstrate the power and potential of ProcTHOR via a sample of 10,000 generated houses and a simple neural model. Models trained using only RGB images on ProcTHOR, with no explicit mapping and no human task supervision produce state-of-the-art results across 6 embodied AI benchmarks for navigation, rearrangement, and arm manipulation, including the presently running Habitat 2022, AI2-THOR Rearrangement 2022, and RoboTHOR challenges. We also demonstrate strong 0-shot results on these benchmarks, via pre-training on ProcTHOR with no fine-tuning on the downstream benchmark, often beating previous state-of-the-art systems that access the downstream training data.
FreeKD: Free-direction Knowledge Distillation for Graph Neural Networks

Knowledge distillation (KD) has demonstrated its effectiveness to boost the performance of graph neural networks (GNNs), where its goal is to distill knowledge from a deeper teacher GNN into a shallower student GNN. However, it is actually difficult to train a satisfactory teacher GNN due to the well-known over-parametrized and over-smoothing issues, leading to invalid knowledge transfer in practical applications. In this paper, we propose the first Free-direction Knowledge Distillation framework via Reinforcement learning for GNNs, called FreeKD, which is no longer required to provide a deeper well-optimized teacher GNN. The core idea of our work is to collaboratively build two shallower GNNs in an effort to exchange knowledge between them via reinforcement learning in a hierarchical way. As we observe that one typical GNN model often has better and worse performances at different nodes during training, we devise a dynamic and free-direction knowledge transfer strategy that consists of two levels of actions: 1) node-level action determines the directions of knowledge transfer between the corresponding nodes of two networks; and then 2) structure-level action determines which of the local structures generated by the node-level actions to be propagated. In essence, our FreeKD is a general and principled framework which can be naturally compatible with GNNs of different architectures. Extensive experiments on five benchmark datasets demonstrate our FreeKD outperforms two base GNNs in a large margin, and shows its efficacy to various GNNs. More surprisingly, our FreeKD has comparable or even better performance than traditional KD algorithms that distill knowledge from a deeper and stronger teacher GNN.
DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models

We introduce DoWhy-GCM , an extension of the DoWhy Python library, that leverages graphical causal models. Unlike existing causality libraries, which mainly focus on effect estimation questions, with DoWhy-GCM , users can ask a wide range of additional causal questions, such as identifying the “root causes” of outliers and distributional changes, causal structure learning, attributing causal inﬂuences, and diagnosis of causal structures. To this end, DoWhy-GCM users ﬁrst model cause-effect relations between variables in a system under study through a graphical causal model, ﬁt the causal mechanisms of variables next, and then ask the causal question. All these steps take only a few lines of code in DoWhy-GCM . The library is available at https://github.com/py-why/dowhy .
On Strategic Measures and Optimality Properties in Discrete-Time Stochastic Control with Universally Measurable Policies

This paper concerns discrete-time infinite-horizon stochastic control systems with Borel state and action spaces and universally measurable policies. We study optimization problems on strategic measures induced by the policies in these systems. The results are then applied to risk-neutral and risk-sensitive Markov decision processes to establish the measurability of the optimal value functions and the existence of universally measurable, randomized or nonrandomized, ϵ-optimal policies, for a variety of average cost criteria and risk criteria. We also extend our analysis to a class of minimax control problems and establish similar optimality results under the axiom of analytic determinacy. Funding: This work was supported by grants from DeepMind, the Alberta Machine Intelligence Institute (AMII), and Alberta Innovates-Technology Futures (AITF).
The Modality Focusing Hypothesis: On the Blink of Multimodal Knowledge Distillation

Multimodal knowledge distillation (KD) extends traditional knowledge distillation to the area of multimodal learning. One common practice is to adopt a well-performed multimodal network as the teacher in the hope that it can transfer its full knowledge to a unimodal student for performance improvement. In this paper, we investigate the efﬁcacy of multimodal KD. We begin by providing two failure cases of it and demonstrate that KD is not a universal cure in multimodal knowledge transfer. We present the modality Venn diagram to understand modality relationships and the modality focusing hypothesis revealing the decisive factor in the efﬁcacy of multimodal KD. Experimental results on 6 multimodal datasets help justify our hypothesis, diagnose failure cases, and point directions to improve distillation performance.
Counting Markov Equivalent Directed Acyclic Graphs Consistent with Background Knowledge

A polynomial-time exact algorithm for counting the number of directed acyclic graphs in a Markov equivalence class was recently given by Wien\"obst, Bannach, and Li\'skiewicz (AAAI 2021). In this paper, we consider the more general problem of counting the number of directed acyclic graphs in a Markov equivalence class when the directions of some of the edges are also fixed (this setting arises, for example, when interventional data is partially available). This problem has been shown in earlier work to be complexity-theoretically hard. In contrast, we show that the problem is nevertheless tractable in an interesting class of instances, by establishing that it is ``fixed-parameter tractable''. In particular, our counting algorithm runs in time that is bounded by a polynomial in the size of the graph, where the degree of the polynomial does \emph{not} depend upon the number of additional edges provided as input.
A Detailed Temperature Map of the Archetypal Protostellar Shocks in L1157

We present sensitive NH3 (1,1)–(7,7) line images from the Karl G. Jansky Very Large Array toward successive shocks, which are associated with the blueshifted outflow lobe driven by the compact protobinary system L1157. Within a projection distance of 0.1 pc, our observations not only trace the quiescent and cold gas in the flattened envelope, but also illustrate the complex physical and chemical processes that take place where the high-velocity jet impinges on its surrounding medium. Specifically, the NH3 ortho-to-para ratio is enhanced by a factor of 2–2.5 along the jet path, where the velocity offset between the line peak and the blueshifted wing reaches values as high as 10 km s−1; it also shows a strong spatial correlation with the NH3 column density, which is enhanced to >1016 cm−2 toward the shock cavities. At a linear resolution of 1500 au, our refined temperature map from the seven NH3 lines shows a gradient from the warm B0 eastern cavity wall (>120 K) to the cool cavity B1 and the earlier shock B2 (<80 K), indicating shock heating.
On the Message Passing Efficiency of Polar and Low-Density Parity-Check Decoders

This study focuses on the efficiency of message-passing-based decoding algorithms for polar and low-density parity-check (LDPC) codes. Both successive cancellation (SC) and belief propagation (BP) decoding algorithms are studied under the message-passing framework. Counter-intuitively, SC decoding demonstrates the highest decoding efficiency although it was considered a weak decoder regarding error-correction performance. We analyze the complexity-performance tradeoff to dynamically track the decoding efficiency, where the complexity is measured by the number of messages passed (NMP), and the performance is measured by the statistical distance to the maximum a posteriori (MAP) estimate. This study offers new insight into the contribution of each message passing in decoding, and compares various decoding algorithms on a message-by-message level.
A Wide and Deep Exploration of Radio Galaxies with Subaru HSC (WERGS). VII. Redshift Evolution of Radio Galaxy Environments at z = 0.3–1.4

We examine the redshift evolution of density environments around 2163 radio galaxies with the stellar masses of ∼109–1012 M ⊙ between redshifts of z = 0.3–1.4, based on the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) and Faint Images of the Radio Sky at Twenty cm. We use the k-nearest neighbor method to measure the local galaxy number density around our radio galaxy sample. We find that the overdensities of the radio galaxies are weakly but significantly anticorrelated with redshift. This is consistent with the known result that the relative abundance of less-massive radio galaxies increases with redshift, because less-massive radio galaxies reside in relatively low-density regions. Massive radio galaxies with stellar masses of M * > 1011 M ⊙ are found in high density environments compared with the control sample galaxies with radio nondetection and matched stellar mass. Less-massive radio galaxies with M * < 1011 M ⊙ reside in average density environments. The fraction of the radio galaxies associated with the neighbors within a typical major merger scale, <70 kpc, is higher than (comparable to) that of the control galaxies at M * > 1011 M ⊙ (M * < 1011 M ⊙). We also find that the local densities around the radio galaxies are anticorrelated with the radio luminosities and black hole mass accretion rates at a fixed stellar mass. These findings suggest that massive radio galaxies have matured through galaxy mergers in the past, and have supermassive black holes whose mass accretion almost ceased at z > 1.4, while less-massive radio galaxies undergo active accretion just at this epoch, as they have avoided such merger events.
Spiking Neural Networks for Frame-based and Event-based Single Object Localization

Spiking neural networks have shown much promise as an energy-efficient alternative to artificial neural networks. However, understanding the impacts of sensor noises and input encodings on the network activity and performance remains difficult with common neuromorphic vision baselines like classification. Therefore, we propose a spiking neural network approach for single object localization trained using surrogate gradient descent, for frame- and event-based sensors. We compare our method with similar artificial neural networks and show that our model has competitive/better performance in accuracy, robustness against various corruptions, and has lower energy consumption. Moreover, we study the impact of neural coding schemes for static images in accuracy, robustness, and energy efficiency. Our observations differ importantly from previous studies on bio-plausible learning rules, which helps in the design of surrogate gradient trained architectures, and offers insight to design priorities in future neuromorphic technologies in terms of noise characteristics and data encoding methods.
The 30 Doradus Molecular Cloud at 0.4 pc Resolution with the Atacama Large Millimeter/submillimeter Array: Physical Properties and the Boundedness of CO-emitting Structures

We present results of a wide-field (approximately 60 × 90 pc) Atacama Large Millimeter/submillimeter Array mosaic of CO(2–1) and 13CO(2–1) emission from the molecular cloud associated with the 30 Doradus star-forming region in the Large Magellanic Cloud (LMC). Three main emission complexes, including two forming a bow-tie-shaped structure extending northeast and southwest from the central R136 cluster, are resolved into complex filamentary networks. Consistent with previous studies, we find that the central region of the cloud has higher line widths at a fixed size relative to the rest of the molecular cloud and to other LMC clouds, indicating an enhanced level of turbulent motions. However, there is no clear trend in gravitational boundedness (as measured by the virial parameter) with distance from R136. Structures observed in 13CO are spatially coincident with filaments and are close to a state of virial equilibrium. In contrast, 12CO structures vary greatly in virialization, with low CO surface brightness structures outside of the main filamentary network being predominantly unbound. The low surface brightness structures constitute ∼10% of the measured CO luminosity; they may be shredded remnants of previously star-forming gas clumps, or alternatively the CO-emitting parts of more massive, CO-dark structures.
Learning Enhanced Representations for Tabular Data via Neighborhood Propagation

Prediction over tabular data is an essential and fundamental problem in many important downstream tasks. However, existing methods either take a data instance of the table independently as input or do not fully utilize the multi-rows features and labels to directly change and enhance the target data representations. In this paper, we propose to 1) construct a hypergraph from relevant data instance retrieval to model the cross-row and cross-column patterns of those instances, and 2) perform message Propagation to Enhance the target data instance representation for Tabular prediction tasks. Specifically, our specially-designed message propagation step benefits from 1) fusion of label and features during propagation, and 2) locality-aware high-order feature interactions. Experiments on two important tabular data prediction tasks validate the superiority of the proposed PET model against other baselines. Additionally, we demonstrate the effectiveness of the model components and the feature enhancement ability of PET via various ablation studies and visualizations. The code is included in https://github.com/KounianhuaDu/PET.
When quantum corrections alter the predictions of classical field theory for scalar field dark matter

We investigate the timescale on which quantum corrections alter the predictions of classical ﬁeld theory for scalar ﬁeld dark matter. This is accomplished by including second order terms in the evolution proportional to the covariance of the ﬁeld operators. When this covariance is no longer small compared to the mean ﬁeld value, we say that the system has reached the “quantum breaktime” and the predictions of classical ﬁeld theory will begin to diﬀer from those of the full quantum theory. While holding the classical ﬁeld theory evolution ﬁxed, we determine the change of the quantum breaktime as total occupation number is increased. This provides a novel numerical estimation of the breaktime based at high occupations n tot and mode number N = 256. We study the collapse of a sinusoidal overdensity in a single spatial dimension. We ﬁnd that the breaktime scales as log( n tot ) prior to shell crossing and then then as a powerlaw following the collapse. If we assume that the collapsing phase is representative of halos undergoing nonlinear growth, this implies that the quantum breaktime of typical systems may be as large as ∼ 30 of dynamical times even at occupations of n tot ∼ 10 100 .
Exploring evolution-aware & -free protein language models as protein function predictors

Large-scale Protein Language Models (PLMs) have improved performance in protein prediction tasks, ranging from 3D structure prediction to various function predictions. In particular, AlphaFold, a ground-breaking AI system, could potentially reshape structural biology. However, the utility of the PLM module in AlphaFold, Evoformer, has not been explored beyond structure prediction. In this paper, we investigate the representation ability of three popular PLMs: ESM-1b (single sequence), MSA-Transformer (multiple sequence alignment) and Evoformer (structural), with a special focus on Evoformer. Specifically, we aim to answer the following key questions: (i) Does the Evoformer trained as part of AlphaFold produce representations amenable to predicting protein function? (ii) If yes, can Evoformer replace ESM-1b and MSA-Transformer? (ii) How much do these PLMs rely on evolution-related protein data? In this regard, are they complementary to each other? We compare these models by empirical study along with new insights and conclusions. All code and datasets for reproducibility are available at https://github.com/elttaes/Revisiting-PLMs.
Comparing the hardness of MAX 2-SAT problem instances for quantum and classical algorithms

An algorithm for a particular problem may find some instances of the problem easier and others harder to solve, even for a fixed input size. We numerically analyse the relative hardness of MAX 2-SAT problem instances for various continuous-time quantum algorithms and a comparable classical algorithm. This has two motivations: to investigate whether small-sized problem instances, which are commonly used in numerical simulations of quantum algorithms for benchmarking purposes, are a good representation of larger instances in terms of their hardness to solve, and to determine the applicability of continuous-time quantum algorithms in a portfolio approach, where we take advantage of the variation in the hardness of instances between different algorithms by running them in parallel. We find that, while there are correlations in instance hardness between all of the algorithms considered, they appear weak enough that a portfolio approach would likely be desirable in practice. Our results also show a widening range of hardness of randomly generated instances as the problem size is increased, which demonstrates both the difference in the distribution of hardness at small sizes and the value of a portfolio approach that can reduce the number of extremely hard instances. We identify specific weaknesses of these quantum algorithms that can be overcome with a portfolio approach, such their inability to efficiently solve satisfiable instances (which is easy classically).
Automated testing of image captioning systems

Image captioning (IC) systems, which automatically generate a text description of the salient objects in an image (real or synthetic), have seen great progress over the past few years due to the development of deep neural networks. IC plays an indispensable role in human society, for example, labeling massive photos for scientific studies and assisting visually-impaired people in perceiving the world. However, even the top-notch IC systems, such as Microsoft Azure Cognitive Services and IBM Image Caption Generator, may return incorrect results, leading to the omission of important objects, deep misunderstanding, and threats to personal safety. To address this problem, we propose MetaIC, the first metamorphic testing approach to validate IC systems. Our core idea is that the object names should exhibit directional changes after object insertion. Specifically, MetaIC (1) extracts objects from existing images to construct an object corpus; (2) inserts an object into an image via novel object resizing and location tuning algorithms; and (3) reports image pairs whose captions do not exhibit differences in an expected way. In our evaluation, we use MetaIC to test one widely-adopted image captioning API and five state-of-the-art (SOTA) image captioning models. Using 1,000 seeds, MetaIC successfully reports 16,825 erroneous issues with high precision (84.9%-98.4%). There are three kinds of errors: misclassification, omission, and incorrect quantity. We visualize the errors reported by MetaIC, which shows that flexible overlapping setting facilitates IC testing by increasing and diversifying the reported errors. In addition, MetaIC can be further generalized to detect label errors in the training dataset, which has successfully detected 151 incorrect labels in MS COCO Caption, a standard dataset in image captioning.
LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Tasks

Fine-tuning pretrained language models (LMs) without making any architectural changes has become a norm for learning various language downstream tasks. However, for non-language downstream tasks, a common practice is to employ task-specific designs for input, output layers, and loss functions. For instance, it is possible to fine-tune an LM into an MNIST classifier by replacing the word embedding layer with an image patch embedding layer, the word token output layer with a 10-way output layer, and the word prediction loss with a 10-way classification loss, respectively. A natural question arises: Can LM fine-tuning solve non-language downstream tasks without changing the model architecture or loss function? To answer this, we propose Language-Interfaced Fine-Tuning (LIFT) and study its efficacy and limitations by conducting an extensive empirical study on a suite of non-language classification and regression tasks. LIFT does not make any changes to the model architecture or loss function, and it solely relies on the natural language interface, enabling"no-code machine learning with LMs. "We find that LIFT performs comparably well across a wide range of low-dimensional classification and regression tasks, matching the performances of the best baselines in many cases, especially for the classification tasks. We also report experimental results on the fundamental properties of LIFT, including inductive bias, robustness, and sample complexity. We also analyze the effect of pretraining on LIFT and a few properties/techniques specific to LIFT, e.g., context-aware learning via appropriate prompting, calibrated predictions, data generation, and two-stage fine-tuning. Our code is available at https://github.com/UW-Madison-Lee-Lab/LanguageInterfacedFineTuning.