Miracle-dz/UniFAR-FTUs huggingface dataset:
Are aligned neural networks adversarially aligned?

Large language models are now tuned to align with the goals of their creators, namely to be"helpful and harmless. "These models should respond helpfully to user questions, but refuse to answer requests that could cause harm. However, adversarial users can construct inputs which circumvent attempts at alignment. In this work, we study to what extent these models remain aligned, even when interacting with an adversarial user who constructs worst-case inputs (adversarial examples). These inputs are designed to cause the model to emit harmful content that would otherwise be prohibited. We show that existing NLP-based optimization attacks are insufficiently powerful to reliably attack aligned text models: even when current NLP-based attacks fail, we can find adversarial inputs with brute force. As a result, the failure of current attacks should not be seen as proof that aligned text models remain aligned under adversarial inputs. However the recent trend in large-scale ML models is multimodal models that allow users to provide images that influence the text that is generated. We show these models can be easily attacked, i.e., induced to perform arbitrary un-aligned behavior through adversarial perturbation of the input image. We conjecture that improved NLP attacks may demonstrate this same level of adversarial control over text-only models.
Domain Wall Dynamics in Classical Spin Chains: Free Propagation, Subdiffusive Spreading, and Soliton Emission.

The nonequilibrium dynamics of domain wall initial states in a classical anisotropic Heisenberg chain exhibits a striking coexistence of apparently linear and nonlinear behaviors: the propagation and spreading of the domain wall can be captured quantitatively by linear, i.e., noninteracting, spin wave theory absent its usual justifications; while, simultaneously, for a wide range of easy-plane anisotropies, emission can take the place of stable solitons-a process and objects intrinsically associated with interactions and nonlinearities. The easy-axis domain wall only has transient dynamics, the isotropic one broadens diffusively, while the easy-plane one yields a pair of ballistically counterpropagating domain walls which, unusually, broaden subdiffusively, their width scaling as t^{1/3}.
Early Planet Formation in Embedded Disks (eDisk). II. Limited Dust Settling and Prominent Snow Surfaces in the Edge-on Class I Disk IRAS 04302+2247

While dust disks around optically visible, Class II protostars are found to be vertically thin, when and how dust settles to the midplane are unclear. As part of the Atacama Large Millimeter/submillimeter Array large program, Early Planet Formation in Embedded Disks, we analyze the edge-on, embedded, Class I protostar IRAS 04302+2247, also nicknamed the “Butterfly Star. ” With a resolution of 0. ″05 (8 au), the 1.3 mm continuum shows an asymmetry along the minor axis that is evidence of an optically thick and geometrically thick disk viewed nearly edge-on. There is no evidence of rings and gaps, which could be due to the lack of radial substructure or the highly inclined and optically thick view. With 0. ″1 (16 au) resolution, we resolve the 2D snow surfaces, i.e., the boundary region between freeze-out and sublimation, for 12CO J = 2–1, 13CO J = 2–1, C18O J = 2–1, H 2CO J = 30,3–20,2, and SO J = 65–54, and constrain the CO midplane snow line to ∼130 au. We find Keplerian rotation around a protostar of 1.6 ± 0.4 M ⊙ using C18O. Through forward ray-tracing using RADMC-3D, we find that the dust scale height is ∼6 au at a radius of 100 au from the central star and is comparable to the gas pressure scale height. The results suggest that the dust of this Class I source has yet to vertically settle significantly.
SIMMF: Semantics-aware Interactive Multiagent Motion Forecasting for Autonomous Vehicle Driving

Autonomous vehicles require motion forecasting of their surrounding multiagents (pedestrians and vehicles) to make optimal decisions for navigation. The existing methods focus on techniques to utilize the positions and velocities of these agents and fail to capture semantic information from the scene. Moreover, to mitigate the increase in computational complexity associated with the number of agents in the scene, some works leverage Euclidean distance to prune far-away agents. However, distance-based metric alone is insufficient to select relevant agents and accurately perform their predictions. To resolve these issues, we propose the Semantics-aware Interactive Multiagent Motion Forecasting (SIMMF) method to capture semantics along with spatial information and optimally select relevant agents for motion prediction. Specifically, we achieve this by implementing a semantic-aware selection of relevant agents from the scene and passing them through an attention mechanism to extract global encodings. These encodings along with agents' local information, are passed through an encoder to obtain time-dependent latent variables for a motion policy predicting the future trajectories. Our results show that the proposed approach outperforms state-of-the-art baselines and provides more accurate and scene-consistent predictions.
Learning to Modulate pre-trained Models in RL

Reinforcement Learning (RL) has been successful in various domains like robotics, game playing, and simulation. While RL agents have shown impressive capabilities in their specific tasks, they insufficiently adapt to new tasks. In supervised learning, this adaptation problem is addressed by large-scale pre-training followed by fine-tuning to new down-stream tasks. Recently, pre-training on multiple tasks has been gaining traction in RL. However, fine-tuning a pre-trained model often suffers from catastrophic forgetting. That is, the performance on the pre-training tasks deteriorates when fine-tuning on new tasks. To investigate the catastrophic forgetting phenomenon, we first jointly pre-train a model on datasets from two benchmark suites, namely Meta-World and DMControl. Then, we evaluate and compare a variety of fine-tuning methods prevalent in natural language processing, both in terms of performance on new tasks, and how well performance on pre-training tasks is retained. Our study shows that with most fine-tuning approaches, the performance on pre-training tasks deteriorates significantly. Therefore, we propose a novel method, Learning-to-Modulate (L2M), that avoids the degradation of learned skills by modulating the information flow of the frozen pre-trained model via a learnable modulation pool. Our method achieves state-of-the-art performance on the Continual-World benchmark, while retaining performance on the pre-training tasks. Finally, to aid future research in this area, we release a dataset encompassing 50 Meta-World and 16 DMControl tasks.
LongCoder: A Long-Range Pre-trained Language Model for Code Completion

In this paper, we introduce a new task for code completion that focuses on handling long code input and propose a sparse Transformer model, called LongCoder, to address this task. LongCoder employs a sliding window mechanism for self-attention and introduces two types of globally accessible tokens - bridge tokens and memory tokens - to improve performance and efficiency. Bridge tokens are inserted throughout the input sequence to aggregate local information and facilitate global interaction, while memory tokens are included to highlight important statements that may be invoked later and need to be memorized, such as package imports and definitions of classes, functions, or structures. We conduct experiments on a newly constructed dataset that contains longer code context and the publicly available CodeXGLUE benchmark. Experimental results demonstrate that LongCoder achieves superior performance on code completion tasks compared to previous models while maintaining comparable efficiency in terms of computational resources during inference. All the codes and data are available at https://github.com/microsoft/CodeBERT.
Gravitational Wave Detection with Relative Astrometry using Roman's Galactic Bulge Time Domain Survey

Gravitational waves (GWs) are a new avenue of observing our Universe. So far, we have seen them in the ~10-100 Hz range, and there are hints that we might soon detect them in the nanohertz regime. Multiple efforts are underway to access GWs across the frequency spectrum; however, parts of the frequency space are currently not covered by any planned or future observatories. Photometric surveys can bridge the microhertz gap in the spectrum between LISA and Pulsar Timing Arrays (PTAs) through relative astrometric measurements. Similar to PTA measurements, these astrometric measurements rely on the correlated spacetime distortions produced by gravitational waves at Earth, which induce coherent, apparent stellar position changes on the sky. To detect microhertz GWs with an imaging survey, a combination of high relative astrometric precision, a large number of observed stars, and a high cadence of exposures are needed. Roman's proposed core community survey, the Galactic Bulge Time Domain Survey (RGBTDS), would have all of these components. RGBTDS would be sensitive to GWs with frequencies ranging from $7.7\times 10^{-8}$ Hz to $5.6\times 10^{-4}$ Hz, which opens up a unique GW observing window for supermassive black hole binaries and their waveform evolution. We note that small changes to the survey could enhance Roman's sensitivity to GWs, making it possible to observe the GW background signal that PTAs have recently hinted at with an SNR $\sim$ 70.
Evaluation of OpenAI Codex for HPC Parallel Programming Models Kernel Generation

We evaluate AI-assisted generative capabilities on fundamental numerical kernels in high-performance computing (HPC), including AXPY, GEMV, GEMM, SpMV, Jacobi Stencil, and CG. We test the generated kernel codes for a variety of language-supported programming models, including (1) C++ (e.g., OpenMP [including offload], OpenACC, Kokkos, SyCL, CUDA, and HIP), (2) Fortran (e.g., OpenMP [including offload] and OpenACC), (3) Python (e.g., numpy, Numba, cuPy, and pyCUDA), and (4) Julia (e.g., Threads, CUDA.jl, AMDGPU.jl, and KernelAbstractions.jl). We use the GitHub Copilot capabilities powered by the GPT-based OpenAI Codex available in Visual Studio Code as of April 2023 to generate a vast amount of implementations given simple + + prompt variants. To quantify and compare the results, we propose a proficiency metric around the initial 10 suggestions given for each prompt. Results suggest that the OpenAI Codex outputs for C++ correlate with the adoption and maturity of programming models. For example, OpenMP and CUDA score really high, whereas HIP is still lacking. We found that prompts from either a targeted language such as Fortran or the more general-purpose Python can benefit from adding code keywords, while Julia prompts perform acceptably well for its mature programming models (e.g., Threads and CUDA.jl). We expect for these benchmarks to provide a point of reference for each programming model’s community. Overall, understanding the convergence of large language models, AI, and HPC is crucial due to its rapidly evolving nature and how it is redefining human-computer interactions.
Measured Albedo in the Wild: Filling the Gap in Intrinsics Evaluation

Intrinsic image decomposition and inverse rendering are long-standing problems in computer vision. To evaluate albedo recovery, most algorithms report their quantitative performance with a mean Weighted Human Disagreement Rate (WHDR) metric on the IIW dataset. However, WHDR focuses only on relative albedo values and often fails to capture overall quality of the albedo. In order to comprehensively evaluate albedo, we collect a new dataset, Measured Albedo in the Wild (MAW), and propose three new metrics that complement WHDR: intensity, chromaticity and texture metrics. We show that existing algorithms often improve WHDR metric but perform poorly on other metrics. We then finetune different algorithms on our MAW dataset to significantly improve the quality of the reconstructed albedo both quantitatively and qualitatively. Since the proposed intensity, chromaticity, and texture metrics and the WHDR are all complementary we further introduce a relative performance measure that captures average performance. By analysing existing algorithms we show that there is significant room for improvement. Our dataset and evaluation metrics will enable researchers to develop algorithms that improve albedo reconstruction.
S-TLLR: STDP-inspired Temporal Local Learning Rule for Spiking Neural Networks

Spiking Neural Networks (SNNs) are biologically plausible models that have been identified as potentially apt for deploying energy-efficient intelligence at the edge, particularly for sequential learning tasks. However, training of SNNs poses significant challenges due to the necessity for precise temporal and spatial credit assignment. Back-propagation through time (BPTT) algorithm, whilst the most widely used method for addressing these issues, incurs a high computational cost due to its temporal dependency. In this work, we propose S-TLLR, a novel three-factor temporal local learning rule inspired by the Spike-Timing Dependent Plasticity (STDP) mechanism, aimed at training deep SNNs on event-based learning tasks. Furthermore, S-TLLR is designed to have low memory and time complexities, which are independent of the number of time steps, rendering it suitable for online learning on low-power edge devices. To demonstrate the scalability of our proposed method, we have conducted extensive evaluations on event-based datasets spanning a wide range of applications, such as image and gesture recognition, audio classification, and optical flow estimation. In all the experiments, S-TLLR achieved high accuracy, comparable to BPTT, with a reduction in memory between $5-50\times$ and multiply-accumulate (MAC) operations between $1.3-6.6\times$.
Catastrophe Risk in a Stochastic Multi-Population Mortality Model

This paper presents an approach to incorporate mortality shocks into mortality projections produced by a stochastic multi-population mortality model. The proposed model combines a decreasing stochastic mortality trend with a regime-switching mechanism that captures age-specific mortality shocks over a lengthy calibration period. The result is a flexible and powerful toolbox that actuaries and risk managers can tailor to their specific needs, risk appetite, or supervisory requirements. We illustrate the proposed mortality model with a case study on projecting Dutch mortality rates. Our findings show that the proposed model generates wider prediction intervals for the mortality rates compared to state-of-the-art stochastic mortality models. The width of these prediction intervals depends on the frequency and severity of the mortality shocks calibrated with the regime-switching model. Furthermore, we compare the solvency capital requirement (SCR) for mortality, longevity and catastrophe risk generated by our toolbox with the SCR under the Solvency II standard model.
Automated Identication of Atrial Fibrillation from Single-lead ECGs Using Multi-branching ResNet

Atrial fibrillation (AF) is the most common cardiac arrhythmia, which is clinically identified with irregular and rapid heartbeat rhythm. AF puts a patient at risk of forming blood clots, which can eventually lead to heart failure, stroke, or even sudden death. It is of critical importance to develop an advanced analytical model that can effectively interpret the electrocardiography (ECG) signals and provide decision support for accurate AF diagnostics. In this paper, we propose an innovative deep-learning method for automated AF identification from single-lead ECGs. We first engage the continuous wavelet transform (CWT) to extract time-frequency features from ECG signals. Then, we develop a convolutional neural network (CNN) structure that incorporates ResNet for effective network training and multi-branching architectures for addressing the imbalanced data issue to process the 2D time-frequency features for AF classification. We evaluate the proposed methodology using two real-world ECG databases. The experimental results show a superior performance of our method compared with traditional deep learning models.
FAIRER: Fairness as Decision Rationale Alignment

Deep neural networks (DNNs) have made significant progress, but often suffer from fairness issues, as deep models typically show distinct accuracy differences among certain subgroups (e.g., males and females). Existing research addresses this critical issue by employing fairness-aware loss functions to constrain the last-layer outputs and directly regularize DNNs. Although the fairness of DNNs is improved, it is unclear how the trained network makes a fair prediction, which limits future fairness improvements. In this paper, we investigate fairness from the perspective of decision rationale and define the parameter parity score to characterize the fair decision process of networks by analyzing neuron influence in various subgroups. Extensive empirical studies show that the unfair issue could arise from the unaligned decision rationales of subgroups. Existing fairness regularization terms fail to achieve decision rationale alignment because they only constrain last-layer outputs while ignoring intermediate neuron alignment. To address the issue, we formulate the fairness as a new task, i.e., decision rationale alignment that requires DNNs' neurons to have consistent responses on subgroups at both intermediate processes and the final prediction. To make this idea practical during optimization, we relax the naive objective function and propose gradient-guided parity alignment, which encourages gradient-weighted consistency of neurons across subgroups. Extensive experiments on a variety of datasets show that our method can significantly enhance fairness while sustaining a high level of accuracy and outperforming other approaches by a wide margin.
AutoGraph: Predicting Lane Graphs From Traffic Observations

Lane graph estimation is a long-standing problem in the context of autonomous driving. Previous works aimed at solving this problem by relying on large-scale, hand-annotated lane graphs, introducing a data bottleneck for training models to solve this task. To overcome this limitation, we propose to use the motion patterns of traffic participants as lane graph annotations. In our AutoGraph approach, we employ a pre-trained object tracker to collect the tracklets of traffic participants such as vehicles and trucks. Based on the location of these tracklets, we predict the successor lane graph from an initial position using overhead RGB images only, not requiring any human supervision. In a subsequent stage, we show how the individual successor predictions can be aggregated into a consistent lane graph. We demonstrate the efficacy of our approach on the UrbanLaneGraph dataset and perform extensive quantitative and qualitative evaluations, indicating that AutoGraph is on par with models trained on hand-annotated graph data.
Renormalization Group Analysis of the Anderson Model on Random Regular Graphs

We present a renormalization group analysis of the problem of the Anderson localization on a Random Regular Graph which generalizes the renormalization group of Abrahams, Anderson, Licciardello, and Ramakrishnan to infinite-dimensional graphs. The renormalization group equations necessarily involve two parameters (one being the changing connectivity of sub-trees), but we show that the one-parameter scaling hypothesis is recovered for sufficiently large system sizes for both eigenstates and spectrum observables. We also explain the non-monotonic behavior of dynamical and spectral quantities as a function of the system size for values of disorder close to the transition, by identifying two terms in the beta function of the running fractal dimension of different signs and functional dependence. Our theory provides the first coherent explanation for the unusual scaling behaviors observed in numerical data of the Anderson model and of Many-Body Localization.
Mean Field Theory in Deep Metric Learning

In this paper, we explore the application of mean field theory, a technique from statistical physics, to deep metric learning and address the high training complexity commonly associated with conventional metric learning loss functions. By adapting mean field theory for deep metric learning, we develop an approach to design classification-based loss functions from pair-based ones, which can be considered complementary to the proxy-based approach. Applying the mean field theory to two pair-based loss functions, we derive two new loss functions, MeanFieldContrastive and MeanFieldClassWiseMultiSimilarity losses, with reduced training complexity. We extensively evaluate these derived loss functions on three image-retrieval datasets and demonstrate that our loss functions outperform baseline methods in two out of the three datasets.
Smaller than a Breadbox: Scale and Natural Kinds

I propose a division of the literature on natural kinds into metaphysical worries, semantic worries, and methodological worries. I argue that the latter set of worries, which concern how classification influences scientific practices, should occupy centre stage in philosophy of science discussions about natural kinds. I apply this methodological framework to the problems of classifying chemical species and nanomaterials. I show that classification in nanoscience differs from classification in chemistry because the latter relies heavily on compositional identity, whereas the former must consider additional properties, namely, size, shape, and surface chemistry. I use this difference to argue for a scale-dependent theory of scientific classification. 1 Introduction 2 The Methodological Problem of Kinds 3 Chemical Kindhood: Reactivity, Microstructure, and the Structure–Property Paradigm 4 Scale-Dependence and Nanoscale Kinds 5 Conclusion 1 Introduction 2 The Methodological Problem of Kinds 3 Chemical Kindhood: Reactivity, Microstructure, and the Structure–Property Paradigm 4 Scale-Dependence and Nanoscale Kinds 5 Conclusion
THE EFFECTS OF ROAD CURVE GEOMETRY AND APPROACH DISTANCE ON JUDGEMENTS OF CURVE EXIT ANGLE

Two psychophysical laboratory experiments were performed to study the effect of road curve geometry and approach distance on judgements of road curve deflection angles. Subjects estimated the change in direction of a curve in the road, viewed in perspective, by setting a direction indicator to represent the exit direction of the bend. The curve angle, radius, direction and distance before the bend were systematically manipulated in the stimulus set. The effect of blurring the subjects vision was also evaluated. In contrast to earlier findings, curve exit direction tended to be over-estimated and radius effects were neutral or opposite to that expected. Increasing the curve angle and reducing the distance before the bend decreased estimation error. The amount of preview available and the inside perspective angle were not found to be directly related to errors of judgement. Right-hand bends were judged more accurately than left-hand bends and degraded visual acuity had little effect on the subject's estimates (a). The number of the covering abstract of the conference is TRIS No. 368448. (TRRL)