Miracle-dz/UniFAR-FTUs huggingface dataset:
RGB-X Classification for Electronics Sorting

Effectively disassembling and recovering materials from waste electrical and electronic equipment (WEEE) is a critical step in moving global supply chains from carbon-intensive, mined materials to recycled and renewable ones. Conventional recycling processes rely on shredding and sorting waste streams, but for WEEE, which is comprised of numerous dissimilar materials, we explore targeted disassembly of numerous objects for improved material recovery. Many WEEE objects share many key features and therefore can look quite similar, but their material composition and internal component layout can vary, and thus it is critical to have an accurate classifier for subsequent disassembly steps for accurate material separation and recovery. This work introduces RGB-X, a multi-modal image classification approach, that utilizes key features from external RGB images with those generated from X-ray images to accurately classify electronic objects. More specifically, this work develops Iterative Class Activation Mapping (iCAM), a novel network architecture that explicitly focuses on the finer-details in the multi-modal feature maps that are needed for accurate electronic object classification. In order to train a classifier, electronic objects lack large and well annotated X-ray datasets due to expense and need of expert guidance. To overcome this issue, we present a novel way of creating a synthetic dataset using domain randomization applied to the X-ray domain. The combined RGB-X approach gives us an accuracy of 98.6% on 10 generations of modern smartphones, which is greater than their individual accuracies of 89.1% (RGB) and 97.9% (X-ray) independently. We provide experimental results133Experimental work done at Biorobotics Lab, Robotics Institute, Carnegie Mellon University to corroborate our results.
Attacking the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examples

Spiking neural networks (SNNs) have attracted much attention for their high energy efficiency and for recent advances in their classification performance. However, unlike traditional deep learning approaches, the analysis and study of the robustness of SNNs to adversarial examples remain relatively underdeveloped. In this work, we focus on advancing the adversarial attack side of SNNs and make three major contributions. First, we show that successful white-box adversarial attacks on SNNs are highly dependent on the underlying surrogate gradient technique, even in the case of adversarially trained SNNs. Second, using the best surrogate gradient technique, we analyze the transferability of adversarial attacks on SNNs and other state-of-the-art architectures like Vision Transformers (ViTs) and Big Transfer Convolutional Neural Networks (CNNs). We demonstrate that the adversarial examples created by non-SNN architectures are not misclassified often by SNNs. Third, due to the lack of an ubiquitous white-box attack that is effective across both the SNN and CNN/ViT domains, we develop a new white-box attack, the Auto Self-Attention Gradient Attack (Auto-SAGA). Our novel attack generates adversarial examples capable of fooling both SNN and non-SNN models simultaneously. Auto-SAGA is as much as $91.1\%$ more effective on SNN/ViT model ensembles and provides a $3\times$ boost in attack effectiveness on adversarially trained SNN ensembles compared to conventional white-box attacks like Auto-PGD. Our experiments and analyses are broad and rigorous covering three datasets (CIFAR-10, CIFAR-100 and ImageNet), five different white-box attacks and nineteen classifier models (seven for each CIFAR dataset and five models for ImageNet).
Deep Learning-Based Automatic Diagnosis System for Developmental Dysplasia of the Hip

As the first-line diagnostic imaging modality, radiography plays an essential role in the early detection of developmental dysplasia of the hip (DDH). Clinically, the diagnosis of DDH relies on manual measurements and subjective evaluation of different anatomical features from pelvic radiographs. This process is inefficient and error-prone and requires years of clinical experience. In this study, we propose a deep learning-based system that automatically detects 14 keypoints from a radiograph, measures three anatomical angles (center-edge, T\"onnis, and Sharp angles), and classifies DDH hips as grades I-IV based on the Crowe criteria. Moreover, a novel data-driven scoring system is proposed to quantitatively integrate the information from the three angles for DDH diagnosis. The proposed keypoint detection model achieved a mean (95% confidence interval [CI]) average precision of 0.807 (0.804-0.810). The mean (95% CI) intraclass correlation coefficients between the center-edge, Tonnis, and Sharp angles measured by the proposed model and the ground-truth were 0.957 (0.952-0.962), 0.947 (0.941-0.953), and 0.953 (0.947-0.960), respectively, which were significantly higher than those of experienced orthopedic surgeons (p<0.0001). In addition, the mean (95% CI) test diagnostic agreement (Cohen's kappa) obtained using the proposed scoring system was 0.84 (0.83-0.85), which was significantly higher than those obtained from diagnostic criteria for individual angle (0.76 [0.75-0.77]) and orthopedists (0.71 [0.63-0.79]). To the best of our knowledge, this is the first study for objective DDH diagnosis by leveraging deep learning keypoint detection and integrating different anatomical measurements, which can provide reliable and explainable support for clinical decision-making.
Training Scale-Invariant Neural Networks on the Sphere Can Happen in Three Regimes

A fundamental property of deep learning normalization techniques, such as batch normalization, is making the pre-normalization parameters scale invariant. The intrinsic domain of such parameters is the unit sphere, and therefore their gradient optimization dynamics can be represented via spherical optimization with varying effective learning rate (ELR), which was studied previously. However, the varying ELR may obscure certain characteristics of the intrinsic loss landscape structure. In this work, we investigate the properties of training scale-invariant neural networks directly on the sphere using a fixed ELR. We discover three regimes of such training depending on the ELR value: convergence, chaotic equilibrium, and divergence. We study these regimes in detail both on a theoretical examination of a toy example and on a thorough empirical analysis of real scale-invariant deep learning models. Each regime has unique features and reflects specific properties of the intrinsic loss landscape, some of which have strong parallels with previous research on both regular and scale-invariant neural networks training. Finally, we demonstrate how the discovered regimes are reflected in conventional training of normalized networks and how they can be leveraged to achieve better optima.
THE DISTRIBUTION OF THE COKERNEL OF A POLYNOMIAL PUSH-FORWARD OF A RANDOM Zp-MATRIX WITH A FIXED RESIDUE CLASS MODULO p

Given a prime p , we compute the distribution of the cokernel of a polynomial push-forward of an n ˆ n Haar-random matrix over the ring Z p of p -adic integers with a ﬁxed residue class modulo p for every positive integer n . More precisely, given an n ˆ n matrix ¯ X over the ﬁnite ﬁeld F p of p elements and a monic polynomial P p t q P Z p r t s whose image in F p r t s modulo p is irreducible, we compute the distribution of the cokernel cok p P p X qq of P p X q for an n ˆ n Haar-random matrix X over Z p whose image modulo p is ¯ X . Surprisingly, our result gives a closed formula for every ﬁxed n , rather than an asymptotic result when n goes to inﬁnity. This proves inﬁnitely many new cases of a conjecture of Cheong, Huang, and Kaplan, which generalizes a result of Friedman and Washington.
High-velocity Stars in SDSS/APOGEE DR17

We report 23 stars having Galactocentric velocities larger than 450 km s−1 in the final data release of the APOGEE survey. This sample was generated using space velocities derived by complementing the high-quality radial velocities from the APOGEE project in Sloan Digital Sky Survey’s Data Release 17 (DR17) with distances and proper motions from Gaia early Data Release 3 (eDR3). We analyze the observed kinematics and derived dynamics of these stars, considering different potential models for the Galaxy. We find that three stars could be unbound depending on the adopted potential, but in general all of the stars show typical kinematics of halo stars. The APOGEE DR17 spectroscopic results and Gaia eDR3 photometry are used to assess the stellar parameters and chemical properties of the stars. All of the stars belong to the red giant branch, and, in general, they follow the abundance pattern of typical halo stars. There are a few exceptions that would deserve further analysis through high-resolution spectroscopy. In particular, we identify a high-velocity Carbon-Enhanced Metal-Poor star, with a Galactocentric velocity of 482 km s−1. We do not confirm any hypervelocity star in the sample, but this result is very sensitive to the adopted distances and less sensitive to the Galactic potential.
Dyadic Interaction Assessment from Free-living Audio for Depression Severity Assessment

Psychomotor retardation in depression has been associated with speech timing changes from dyadic clinical interviews. In this work, we investigate speech timing features from free-living dyadic interactions. Apart from the possibility of continuous monitoring to complement clinical visits, a study in free-living conditions would also allow inferring sociability features such as dyadic interaction frequency implicated in depression. We adapted a speaker count estimator as a dyadic interaction detector with a specificity of 89.5% and a sensitivity of 86.1% in the DIHARD dataset. Using the detector, we obtained speech timing features from the detected dyadic interactions in multi-day audio recordings of 32 participants comprised of 13 healthy individuals, 11 individuals with depression, and 8 individuals with psychotic disorders. The dyadic interaction frequency increased with depression severity in participants with no or mild depression, indicating a potential diagnostic marker of depression onset. However, the dyadic interaction frequency decreased with increasing depression severity for participants with moderate or severe depression. In terms of speech timing features, the response time had a significant positive correlation with depression severity. Our work shows the potential of dyadic interaction analysis from audio recordings of free-living to obtain markers of depression severity.
Irreducible highest weight representations of the quantum algebra $U_h(A_\infty)$

A class of highest weight irreducible representations of the algebra $U_h(A_\infty)$, the quantum analogue of the completion and central extension $A_\infty$ of the Lie algebra $gl_\infty$, is constructed. It is considerably larger than the class of representations known so far. Within each module a basis is introduced and the transformation relations of the basis under the action of the Chevalley generators are explicitly given. The verification of the quantum algebra relations is shown to reduce to a set of nontrivial $q$-number identities. All representations are restricted in the terminology of S. Levendorskii and Y. Soibelman (Commun. Math. Phys. {\bf 140}, 399-414 (1991)).
A discrete‐time indirect adaptive multiple‐model actuator failure compensation scheme

A new discrete‐time actuator failure compensation control scheme is developed, using a multiple‐model adaptive control approach which has the capacity to achieve faster and more accurate compensation of failure uncertainties. An individual adaptive system, for each possible failure pattern in a failure pattern set of interest for compensation, is designed using an indirect model reference adaptive control scheme for actuator failure compensation. A multiple‐model control switching mechanism for discrete‐time systems is set up by finding the minimal performance index to select the most appropriate control law. The performance indices are based on the adaptive estimation errors of individual parameterized systems with actuator failures. Simulation results from an aircraft flight control system example are presented to show the desired closed‐loop system stability and tracking performance despite the presence of uncertain actuator failures. Copyright © 2014 John Wiley & Sons, Ltd.
AstroSat/UVIT Cluster Photometry in the Northern Disk of M31

The Andromeda galaxy (M31) is an object of ongoing study with the Ultraviolet Imaging Telescope (UVIT) on AstroSat. UVIT far-UV (FUV) and near-UV (NUV) photometry is carried out here for a set of 239 clusters in the NE disk and bulge of M31 that overlap with the HST/PHAT survey. Padova stellar models were applied to derive ages, masses, metallicities, and extinctions for 170 clusters. The ages show a narrow peak at ∼4 Myr and a broad peak around 100 Myr. log(Z/Z ⊙) values are mostly between −0.3 and +0.3. The 7 clusters in the bulge have a low metallicity and high mass. Most clusters are in the spiral arms and have metallicities in the range noted above. The youngest clusters mostly have high metallicity and are concentrated along the brightest parts of the spiral arms. The UVIT FUV and NUV data are sensitive to young stars and detect a new metal-rich peak in star formation in the disk at age ∼4 Myr.
Generative Adversarial Super-Resolution at the Edge with Knowledge Distillation

Single-Image Super-Resolution can support robotic tasks in environments where a reliable visual stream is required to monitor the mission, handle teleoperation or study relevant visual details. In this work, we propose an efficient Generative Adversarial Network model for real-time Super-Resolution, called EdgeSRGAN (code available at https://github.com/PIC4SeR/EdgeSRGAN). We adopt a tailored architecture of the original SRGAN and model quantization to boost the execution on CPU and Edge TPU devices, achieving up to 200 fps inference. We further optimize our model by distilling its knowledge to a smaller version of the network and obtain remarkable improvements compared to the standard training approach. Our experiments show that our fast and lightweight model preserves considerably satisfying image quality compared to heavier state-of-the-art models. Finally, we conduct experiments on image transmission with bandwidth degradation to highlight the advantages of the proposed system for mobile robotic applications.
Reward Delay Attacks on Deep Reinforcement Learning

Most reinforcement learning algorithms implicitly assume strong synchrony. We present novel attacks targeting Q-learning that exploit a vulnerability entailed by this assumption by delaying the reward signal for a limited time period. We consider two types of attack goals: targeted attacks, which aim to cause a target policy to be learned, and untargeted attacks, which simply aim to induce a policy with a low reward. We evaluate the efficacy of the proposed attacks through a series of experiments. Our first observation is that reward-delay attacks are extremely effective when the goal is simply to minimize reward. Indeed, we find that even naive baseline reward-delay attacks are also highly successful in minimizing the reward. Targeted attacks, on the other hand, are more challenging, although we nevertheless demonstrate that the proposed approaches remain highly effective at achieving the attacker's targets. In addition, we introduce a second threat model that captures a minimal mitigation that ensures that rewards cannot be used out of sequence. We find that this mitigation remains insufficient to ensure robustness to attacks that delay, but preserve the order, of rewards.
Laboratory Observation of Para-H2C4N

The rotational spectrum of para-H2C4N (I(H)= 0) in the Ka = 1 rotational level has been observed in supersonic expansion at low temperature by Fourier transform microwave spectroscopy. The C2v structure of H2C4N has been confirmed by an analysis of both the present Ka = 1 transitions and the Ka = 0 transitions from the previous ortho-H2C4N (I(H)= 1) study. Rest transition frequencies of para-H2C4N are now available for radio-astronomical searching.
Sensitivity of non-Hermitian systems

Understanding the extreme sensitivity of the eigenvalues of non-Hermitian Hamiltonians to the boundary conditions is of great importance when analyzing non-Hermitian systems, as it appears generically and is intimately connected to the skin eﬀect and the breakdown of the conventional bulk boundary correspondence. Here we describe a method to ﬁnd the eigenvalues of one-dimensional one-band models with arbitrary boundary conditions. We use this method on several systems to ﬁnd analytical expressions for the eigenvalues, which give us conditions on the parameter values in the system for when we can expect the spectrum to be insensitive to a change in boundary conditions. By stacking one-dimensional chains, we use the derived results to ﬁnd corresponding conditions for insensitivity for some two-dimensional systems with periodic boundary conditions in one direction. This would be hard by using other methods to detect skin eﬀect, such as the winding of the determinant of the Bloch Hamiltonian. Finally, we use these results to make predictions about the (dis)appearance of the skin eﬀect in purely two-dimensional systems with open boundary conditions in both directions.
Do better ImageNet classifiers assess perceptual similarity better?

Perceptual distances between images, as measured in the space of pre-trained deep features, have outperformed prior low-level, pixel-based metrics on assessing perceptual similarity. While the capabilities of older and less accurate models such as AlexNet and VGG to capture perceptual similarity are well known, modern and more accurate models are less studied. In this paper, we present a large-scale empirical study to assess how well ImageNet classifiers perform on perceptual similarity. First, we observe a inverse correlation between ImageNet accuracy and Perceptual Scores of modern networks such as ResNets, EfficientNets, and Vision Transformers: that is better classifiers achieve worse Perceptual Scores. Then, we examine the ImageNet accuracy/Perceptual Score relationship on varying the depth, width, number of training steps, weight decay, label smoothing, and dropout. Higher accuracy improves Perceptual Score up to a certain point, but we uncover a Pareto frontier between accuracies and Perceptual Score in the mid-to-high accuracy regime. We explore this relationship further using a number of plausible hypotheses such as distortion invariance, spatial frequency sensitivity, and alternative perceptual functions. Interestingly we discover shallow ResNets and ResNets trained for less than 5 epochs only on ImageNet, whose emergent Perceptual Score matches the prior best networks trained directly on supervised human perceptual judgements. The checkpoints for the models in our study are available at https://console.cloud. google.com/storage/browser/gresearch/perceptual_similarity.
Blessing of Class Diversity in Pre-training

This paper presents a new statistical analysis aiming to explain the recent superior achievements of the pre-training techniques in natural language processing (NLP). We prove that when the classes of the pre-training task (e.g., different words in the masked language model task) are sufficiently diverse, in the sense that the least singular value of the last linear layer in pre-training (denoted as $\tilde{\nu}$) is large, then pre-training can significantly improve the sample efficiency of downstream tasks. Specially, we show the transfer learning excess risk enjoys an $O\left(\frac{1}{\tilde{\nu} \sqrt{n}}\right)$ rate, in contrast to the $O\left(\frac{1}{\sqrt{m}}\right)$ rate in the standard supervised learning. Here, $n$ is the number of pre-training data and $m$ is the number of data in the downstream task, and typically $n \gg m$. Our proof relies on a vector-form Rademacher complexity chain rule for disassembling composite function classes and a modified self-concordance condition. These techniques can be of independent interest.
Predict+Optimize for Packing and Covering LPs with Unknown Parameters in Constraints

Predict+Optimize is a recently proposed framework which combines machine learning and constrained optimization, tackling optimization problems that contain parameters that are unknown at solving time. The goal is to predict the unknown parameters and use the estimates to solve for an estimated optimal solution to the optimization problem. However, all prior works have focused on the case where unknown parameters appear only in the optimization objective and not the constraints, for the simple reason that if the constraints were not known exactly, the estimated optimal solution might not even be feasible under the true parameters. The contributions of this paper are two-fold. First, we propose a novel and practically relevant framework for the Predict+Optimize setting, but with unknown parameters in both the objective and the constraints. We introduce the notion of a correction function, and an additional penalty term in the loss function, modelling practical scenarios where an estimated optimal solution can be modified into a feasible solution after the true parameters are revealed, but at an additional cost. Second, we propose a corresponding algorithmic approach for our framework, which handles all packing and covering linear programs. Our approach is inspired by the prior work of Mandi and Guns, though with crucial modifications and re-derivations for our very different setting. Experimentation demonstrates the superior empirical performance of our method over classical approaches.
An Empirical Test of the Theory of Crystallization in Stellar Interiors

Our theoretical calculations indicate that the massive pulsating DA white dwarf, BPM 37093, has a crystallized ion interior. Our estimates for the crystallized mass fraction depend on our assumption for the interior composition and range from 10% for pure carbon and low total mass to more than 90% for pure oxygen and high total mass; the mass of this white dwarf suggests on theoretical grounds that the core composition is unlikely to be lighter than oxygen. Our best estimate is that the crystallized mass fraction exceeds 90%. BPM 37093 is the first known example of a pulsating white dwarf star with a potentially crystalline interior. We point out that future observations of the pulsation properties of this object are essential for carrying out asteroseismological measurements of the crystallized mass fraction of its interior. Uncertainties in the nature and extent of crystallization and its associated effects are the largest source of uncertainty in calculating the ages of the coolest white dwarf stars—an important chronometer for the Galactic disk. In addition, crystallization plays an important role in our understanding of neutron star crusts. Investigations of the pulsation properties of this star promise to provide us with the first empirical tests of the theory of crystallization in stellar interiors, now more than 35 yr old.