Miracle-dz/UniFAR-FTUs huggingface dataset:
PerfSAGE: Generalized Inference Performance Predictor for Arbitrary Deep Learning Models on Edge Devices

The ability to accurately predict deep neural network (DNN) inference performance metrics, such as latency, power, and memory footprint, for an arbitrary DNN on a target hardware platform is essential to the design of DNN based models. This ability is critical for the (manual or automatic) design, optimization, and deployment of practical DNNs for a specific hardware deployment platform. Unfortunately, these metrics are slow to evaluate using simulators (where available) and typically require measurement on the target hardware. This work describes PerfSAGE, a novel graph neural network (GNN) that predicts inference latency, energy, and memory footprint on an arbitrary DNN TFlite graph (TFL, 2017). In contrast, previously published performance predictors can only predict latency and are restricted to pre-defined construction rules or search spaces. This paper also describes the EdgeDLPerf dataset of 134,912 DNNs randomly sampled from four task search spaces and annotated with inference performance metrics from three edge hardware platforms. Using this dataset, we train PerfSAGE and provide experimental results that demonstrate state-of-the-art prediction accuracy with a Mean Absolute Percentage Error of<5% across all targets and model search spaces. These results: (1) Outperform previous state-of-art GNN-based predictors (Dudziak et al., 2020), (2) Accurately predict performance on accelerators (a shortfall of non-GNN-based predictors (Zhang et al., 2021)), and (3) Demonstrate predictions on arbitrary input graphs without modifications to the feature extractor.
Gatherplot: A Non-Overlapping Scatterplot

Scatterplots are a common tool for exploring multidimensional datasets, especially in the form of scatterplot matrices (SPLOMs). However, scatterplots suffer from overplotting when categorical variables are mapped to one or two axes, or the same continuous variable is used for both axes. Previous methods such as histograms or violin plots use aggregation, which makes brushing and linking difficult. To address this, we propose gatherplots, an extension of scatterplots to manage the overplotting problem. Gatherplots are a form of unit visualization, which avoid aggregation and maintain the identity of individual objects to ease visual perception. In gatherplots, every visual mark that maps to the same position coalesces to form a packed entity, thereby making it easier to see the overview of data groupings. The size and aspect ratio of marks can also be changed dynamically to make it easier to compare the composition of different groups. In the case of a categorical variable vs. a categorical variable, we propose a heuristic to decide bin sizes for optimal space usage. To validate our work, we conducted a crowdsourced user study that shows that gatherplots enable people to assess data distribution more quickly and more correctly than when using jittered scatterplots.
Learning Efficient Abstract Planning Models that Choose What to Predict

An effective approach to solving long-horizon tasks in robotics domains with continuous state and action spaces is bilevel planning, wherein a high-level search over an abstraction of an environment is used to guide low-level decision-making. Recent work has shown how to enable such bilevel planning by learning abstract models in the form of symbolic operators and neural samplers. In this work, we show that existing symbolic operator learning approaches fall short in many robotics domains where a robot's actions tend to cause a large number of irrelevant changes in the abstract state. This is primarily because they attempt to learn operators that exactly predict all observed changes in the abstract state. To overcome this issue, we propose to learn operators that 'choose what to predict' by only modelling changes necessary for abstract planning to achieve specified goals. Experimentally, we show that our approach learns operators that lead to efficient planning across 10 different hybrid robotics domains, including 4 from the challenging BEHAVIOR-100 benchmark, while generalizing to novel initial states, goals, and objects.
Smoothed Online Learning for Prediction in Piecewise Affine Systems

The problem of piecewise affine (PWA) regression and planning is of foundational importance to the study of online learning, control, and robotics, where it provides a theoretically and empirically tractable setting to study systems undergoing sharp changes in the dynamics. Unfortunately, due to the discontinuities that arise when crossing into different ``pieces,'' learning in general sequential settings is impossible and practical algorithms are forced to resort to heuristic approaches. This paper builds on the recently developed smoothed online learning framework and provides the first algorithms for prediction and simulation in PWA systems whose regret is polynomial in all relevant problem parameters under a weak smoothness assumption; moreover, our algorithms are efficient in the number of calls to an optimization oracle. We further apply our results to the problems of one-step prediction and multi-step simulation regret in piecewise affine dynamical systems, where the learner is tasked with simulating trajectories and regret is measured in terms of the Wasserstein distance between simulated and true data. Along the way, we develop several technical tools of more general interest.
Interface-resolved study of magnetism in MgO/FeCoB/MgO trilayers using x-ray standing wave techniques

Interfaces in the MgO-FeCoB-MgO trilayer have been studied with grazing incident nuclear resonance scattering (GINRS) using the x-ray standing waves (XSW) technique. High depth selectivity of the present method allows one to measure magnetism and structure at the two interfaces of FeCoB, namely, FeCoB-on-MgO and MgO-on-FeCoB, independently, yielding an intriguing result that both interfaces are not symmetric. A high-density layer with an increased magnetic hyperfine field at the FeCoB-on-MgO interface suggests different growth mechanisms at the two interfaces. The azimuthal angle-dependent magneto-optic Kerr effect measurements reveal the presence of unusual uniaxial magnetic anisotropy (UMA) in the trilayer. An in-situ temperature-dependent study discovered that this UMA systematically reduces with temperature. The trilayer becomes isotropic at 450C with an order-of-magnitude increase in coercivity. The asymmetry at the interfaces is, in turn, explained by boron diffusion from the FeCoB interface layer into the nearby MgO layer. Stress-induced UMA is observed in the boron-deficient FeCoB layer, superimposed with the bulk FeCoB layer, and found to be responsible for unusual UMA. The temperature-dependent variation in the UMA and coercivity can be understood in terms of variations in the internal stresses and coupling between FeCoB bulk and the interface layer.
Reconstructing a point set from a random subset of its pairwise distances

Let $V$ be a set of $n$ points on the real line. Suppose that each pairwise distance is known independently with probability $p$. How much of $V$ can be reconstructed up to isometry? We prove that $p = (\log n)/n$ is a sharp threshold for reconstructing all of $V$ which improves a result of Benjamini and Tzalik. This follows from a hitting time result for the random process where the pairwise distances are revealed one-by-one uniformly at random. We also show that $1/n$ is a weak threshold for reconstructing a linear proportion of $V$.
Pauli equation and charged spin-1/2 particle in a weak gravitational field

Using the nonrelativistic approximation in the curved-space Dirac equation, the analog of the Pauli equation is derived for a weak, but otherwise arbitrary, gravitational ﬁeld, in the presence of an electromagnetic ﬁeld. On top of that, are obtained the equations of motion for the massive spin-1 / 2 charged particle. In the two particular cases, we consider the previously explored backgrounds of the plane gravitational wave and the homogeneous static gravitational ﬁeld. Diﬀerent from the previous works which were employing either the exact or conventional Foldy-Wouthuysen transformations, here we perform calculations in the simplest way, in particular, aiming to clean up the existing discrepancies in the literature.
Monitoring of Pulse Intensity and Mode Changing for PSR B0329+54

We monitored PSR B0329+54 for four months using the Nanshan 25-m radio telescope at 1540 MHz. The observations reveal three mode changing events, that lasted for 37, 12 and 17 min respectively. The integrated flux densities for the different observations differ greatly from their average value. The modulation indices of individual observations vary in a wide range as well, indicating that the intrinsic flux density is largely modulated by the scintillation effect.
Text-To-4D Dynamic Scene Generation

We present MAV3D (Make-A-Video3D), a method for generating three-dimensional dynamic scenes from text descriptions. Our approach uses a 4D dynamic Neural Radiance Field (NeRF), which is optimized for scene appearance, density, and motion consistency by querying a Text-to-Video (T2V) diffusion-based model. The dynamic video output generated from the provided text can be viewed from any camera location and angle, and can be composited into any 3D environment. MAV3D does not require any 3D or 4D data and the T2V model is trained only on Text-Image pairs and unlabeled videos. We demonstrate the effectiveness of our approach using comprehensive quantitative and qualitative experiments and show an improvement over previously established internal baselines. To the best of our knowledge, our method is the first to generate 3D dynamic scenes given a text description.
ZiCo: Zero-shot NAS via Inverse Coefficient of Variation on Gradients

Neural Architecture Search (NAS) is widely used to automatically obtain the neural network with the best performance among a large number of candidate architectures. To reduce the search time, zero-shot NAS aims at designing training-free proxies that can predict the test performance of a given architecture. However, as shown recently, none of the zero-shot proxies proposed to date can actually work consistently better than a naive proxy, namely, the number of network parameters (#Params). To improve this state of affairs, as the main theoretical contribution, we first reveal how some specific gradient properties across different samples impact the convergence rate and generalization capacity of neural networks. Based on this theoretical analysis, we propose a new zero-shot proxy, ZiCo, the first proxy that works consistently better than #Params. We demonstrate that ZiCo works better than State-Of-The-Art (SOTA) proxies on several popular NAS-Benchmarks (NASBench101, NATSBench-SSS/TSS, TransNASBench-101) for multiple applications (e.g., image classification/reconstruction and pixel-level prediction). Finally, we demonstrate that the optimal architectures found via ZiCo are as competitive as the ones found by one-shot and multi-shot NAS methods, but with much less search time. For example, ZiCo-based NAS can find optimal architectures with 78.1%, 79.4%, and 80.4% test accuracy under inference budgets of 450M, 600M, and 1000M FLOPs, respectively, on ImageNet within 0.4 GPU days. Our code is available at https://github.com/SLDGroup/ZiCo.
Towards Continual Egocentric Activity Recognition: A Multi-Modal Egocentric Activity Dataset for Continual Learning

With the rapid development of wearable cameras, it is now feasible to considerably increase the collection of egocentric video for first-person visual perception. However, the development is hindered by a shortage of multi-modal egocentric activity datasets. Furthermore, the catastrophic forgetting problem of multimodal continual activity learning, as a branch of continual learning, has not been thoroughly explored, which makes accumulating a larger collection of multi-modal activity data more urgent. To address this shortage, we propose a multi-modal egocentric activity dataset for continual activity learning named UESTC-MMEA-CL in this paper. The dataset is collected using our self-developed glasses with a first-person camera and wearable sensors, and it contains synchronized data of video, accelerometers, and gyroscopes for 32 types of daily activities performed by 10 participants who wore our glasses. Statistical analysis of the sensor data is given to show the auxiliary effects of activity recognition. We report the results of egocentric activity recognition of three modalities (RGB, acceleration, and gyroscope) separately and jointly on a base network architecture. We thoroughly evaluated four baseline methods with different multimodal combinations to explore the catastrophic forgetting in continual learning on UESTC-MMEA-CL. We hope that the UESTC-MMEA-CL dataset can act as a facilitator for future studies on continual learning for first-person activity recognition in wearable applications. You can download preliminary data from https://ivipclab.github. io/publication_uestc-mmea-cl/mmea-cl. The data is currently used to solve the problems of multimodal continual learning of activities.
Generalized multifractality in two-dimensional disordered systems of chiral symmetry classes

We study generalized multifractality that characterizes eigenstate fluctuations and correlations in disordered systems of chiral symmetry classes AIII, BDI, and CII. By using the non-linear sigmamodel field theory, we construct pure-scaling composite operators and eigenfunction observables that satisfy Abelian fusion rules. The observables are labelled by two multi-indices $\lambda$, $\lambda'$ referring to two sublattices, at variance with other symmetry classes, where a single multi-index $\lambda$ (that can be viewed as a generalized version of a Young diagram) is needed. Further, we analyze Weyl symmetries of multifractal exponents, which are also peculiar in chiral classes, in view of a distinct root system associated with the sigma-model symmetric space. The analytical results are supported and complemented by numerical simulations that are performed for a 2D lattice Hamiltonian of class AIII, both in the metallic phase and at the Anderson transition. Both in the metallic phase and at the transition, the numerically obtained exponents satisfy Weyl symmetry relations, confirming that the sigma-model is the right theory of the problem. Furthermore, in the metallic phase, we observe the generalized parabolicity (proportionality to eigenvalues of the quadratic Casimir operator), as expected in the one-loop approximation. On the other hand, the generalized parabolicity is strongly violated at the metal-insulator transition, implying violation of local conformal invariance.
DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature

The increasing fluency and widespread usage of large language models (LLMs) highlight the desirability of corresponding tools aiding detection of LLM-generated text. In this paper, we identify a property of the structure of an LLM's probability function that is useful for such detection. Specifically, we demonstrate that text sampled from an LLM tends to occupy negative curvature regions of the model's log probability function. Leveraging this observation, we then define a new curvature-based criterion for judging if a passage is generated from a given LLM. This approach, which we call DetectGPT, does not require training a separate classifier, collecting a dataset of real or generated passages, or explicitly watermarking generated text. It uses only log probabilities computed by the model of interest and random perturbations of the passage from another generic pre-trained language model (e.g., T5). We find DetectGPT is more discriminative than existing zero-shot methods for model sample detection, notably improving detection of fake news articles generated by 20B parameter GPT-NeoX from 0.81 AUROC for the strongest zero-shot baseline to 0.95 AUROC for DetectGPT. See https://ericmitchell.ai/detectgpt for code, data, and other project information.
Graph Neural Networks can Recover the Hidden Features Solely from the Graph Structure

Graph Neural Networks (GNNs) are popular models for graph learning problems. GNNs show strong empirical performance in many practical tasks. However, the theoretical properties have not been completely elucidated. In this paper, we investigate whether GNNs can exploit the graph structure from the perspective of the expressive power of GNNs. In our analysis, we consider graph generation processes that are controlled by hidden (or latent) node features, which contain all information about the graph structure. A typical example of this framework is kNN graphs constructed from the hidden features. In our main results, we show that GNNs can recover the hidden node features from the input graph alone, even when all node features, including the hidden features themselves and any indirect hints, are unavailable. GNNs can further use the recovered node features for downstream tasks. These results show that GNNs can fully exploit the graph structure by themselves, and in effect, GNNs can use both the hidden and explicit node features for downstream tasks. In the experiments, we confirm the validity of our results by showing that GNNs can accurately recover the hidden features using a GNN architecture built based on our theoretical analysis.
Rewarded meta-pruning: Meta Learning with Rewards for Channel Pruning

Convolutional neural networks (CNNs) have gained recognition for their remarkable performance across various tasks. However, the sheer number of parameters and the computational demands pose challenges, particularly on edge devices with limited processing power. In response to these challenges, this paper presents a novel approach aimed at enhancing the efficiency of deep learning models. Our method introduces the concept of accuracy and efficiency coefficients, offering a fine-grained control mechanism to balance the trade-off between network accuracy and computational efficiency. At our core is the Rewarded Meta-Pruning algorithm, guiding neural network training to generate pruned model weight configurations. The selection of this pruned model is based on approximations of the final model’s parameters, and it is precisely controlled through a reward function. This reward function empowers us to tailor the optimization process, leading to more effective fine-tuning and improved model performance. Extensive experiments and evaluations underscore the superiority of our proposed method when compared to state-of-the-art techniques. We conducted rigorous pruning experiments on well-established architectures such as ResNet-50, MobileNetV1, and MobileNetV2. The results not only validate the efficacy of our approach but also highlight its potential to significantly advance the field of model compression and deployment on resource-constrained edge devices.
The Active Merging System ESO 202-G23 (Carafe Nebula)

We present the results of observations both in imaging and spectroscopy of the nearby active galaxy ESO 202-G23 (Carafe Nebula). The main result of this investigation is that the peculiar morphology of the galaxy, formerly attributed to the presence of an asymmetric bar, is produced by the merging of two galaxies both hosting an active nucleus. Our deep R image reveals unexpectedly the presence of a knot (G2), not visible in the B image, at 3.5 kpc on the northern of the nucleus (G1), classified in the literature as a Seyfert 2. A long-slit spectrum provides evidence that G1 is a Seyfert 1.5 nucleus and that G2 is also a nucleus, showing features typical of a LINER heavily reddened by dust. The presence of tails and plumes in the outermost parts of the galaxy and the double U shape of the rotation curve of the gas around G1 and G2 strongly support the hypothesis of a merger that occurred 106–109 years ago, as suggested by the comparison of the current (14 M⊙ yr-1) and the recent (24 M⊙ yr-1) star formation rates. The system ESO 202-G23 is an infrared bright source (LFIR/L⊙ = 2.18 × 1010) and the IRAS flux ratios suggest a predominantly thermal origin of the infrared emission. The analysis of the IRAS data allows us to obtain some important properties concerning both the gas and the dust: Td(warm) ∼ 36 K; Md(warm) = 4.1 × 106 M⊙; Td(cold) ∼ 21 K; Md(cold) = (6.9–8.3) × 107 M⊙; MH I = 8.6 × 109 M⊙; MH II = (2.3–4.3) × 109 M⊙; Mgas = (1.1–1.3) × 1010 M⊙. The derived mass-to-light ratio MH I/LB = 2.23 and the value of the parameter K = vmax/rmax, K(G1) = 201, K(G2) = 179, indicate that the precursor is not later than Sbc.
Fine-tunable near-critical Stranski-Krastanov growth of InAs/InP quantum dots

Emerging applications of self-assembled semiconductor quantum dot (QD)-based nonclassical light sources emitting in the telecom C-band (1530 to 1565 nm) present challenges in terms of controlled synthesis of their low-density ensembles, critical for device processing with an isolated QD. This work shows how to control the surface density and size of InAs/InP quantum dots over a wide range by tailoring the conditions of Stranski-Krastanow growth. We demonstrate that in the near-critical growth regime, the density of quantum dots can be tuned between $10^7$ and $10^{10} cm^{-2}$. Furthermore, employing both experimental and modeling approaches, we show that the size (and therefore the emission wavelength) of InAs nanoislands on InP can be controlled independently from their surface density. Finally, we demonstrate that our growth method gives low-density ensembles resulting in well-isolated QD-originated emission lines in the telecom C-band.
When sampling works in data-driven control: Informativity for stabilization in continuous time

This paper introduces a notion of data informativity for stabilization tailored to continuous-time signals and systems. We establish results comparable to those known for discrete-time systems with sampled data. We justify that additional assumptions on the properties of the noise signals are needed to understand when sampled versions of continuous-time signals are informative for stabilization, thereby introducing the notions of square Lipschitzness and total bounded variation. This allows us to connect the continuous and discrete domains, yielding sufficient conditions to synthesize a stabilizing controller for the true continuous-time system on the basis of sampled data. Simulations illustrate our results.
On the Riemann–Hilbert approach to asymptotics of tronquée solutions of Painlevé I

In this paper, we revisit large variable asymptotic expansions of tronquée solutions of the Painlevé I equation, obtained via the Riemann–Hilbert approach and the method of steepest descent. The explicit construction of an extra local parametrix around the recessive stationary point of the phase function, in terms of complementary error functions, makes it possible to give detailed information about exponential-type contributions beyond the standard Poincaré expansions for tronquée and tritronquée solutions.
Spatial and Temporal Scales of Boundary Layer Wind Predictability in Response to Small-Amplitude Land Surface Uncertainty

Abstract Predictability experiments with the Weather Research and Forecast (WRF) model as a proxy for the atmosphere are analyzed to quantify the spatial and temporal scales of boundary layer wind response to land surface perturbations. Soil moisture is chosen as the land surface variable subject to uncertainty because the atmosphere is known to be sensitive to its state. A range of experiments with spatially correlated, small-amplitude perturbations to soil moisture leads to results that show the dependence of predictability on atmospheric conditions. The primary conclusions are as follows: 1) atmospheric conditions, including static instability and the presence of deep convection, determine whether large errors and local loss of predictability are possible in response to soil moisture errors; 2) the scale of soil moisture uncertainty determines scales of PBL wind predictability when the atmosphere is resistant to upscale error transfer, but when the atmosphere is sensitive the scale and magnitude of soi.