Miracle-dz/UniFAR-FTUs huggingface dataset:
Adversarial Classification: Necessary conditions and geometric flows

We study a version of adversarial classification where an adversary is empowered to corrupt data inputs up to some distance $\varepsilon$, using tools from variational analysis. In particular, we describe necessary conditions associated with the optimal classifier subject to such an adversary. Using the necessary conditions, we derive a geometric evolution equation which can be used to track the change in classification boundaries as $\varepsilon$ varies. This evolution equation may be described as an uncoupled system of differential equations in one dimension, or as a mean curvature type equation in higher dimension. In one dimension we rigorously prove that one can use the initial value problem starting from $\varepsilon=0$, which is simply the Bayes classifier, in order to solve for the global minimizer of the adversarial problem. Numerical examples illustrating these ideas are also presented.
Orthofibrations and monoidal adjunctions.

We study various types of fibrations over a product of two $\infty$-categories, and show how they can be dualised over one of the two factors via an explicit construction in terms of spans. Among other things, we use this to prove that given an adjunction between monoidal $\infty$-categories, there is an equivalence between lax monoidal structures on the right adjoint and oplax monoidal structures on the left adjoint functor.
Modeling Functional Similarity in Source Code With Graph-Based Siamese Networks

Code clones are duplicate code fragments that share (nearly) similar syntax or semantics. Code clone detection plays an important role in software maintenance, code refactoring, and reuse. A substantial amount of research has been conducted in the past to detect clones. A majority of these approaches use lexical and syntactic information to detect clones. However, only a few of them target semantic clones. Recently, motivated by the success of deep learning models in other fields, including natural language processing and computer vision, researchers have attempted to adopt deep learning techniques to detect code clones. These approaches use lexical information (tokens) and(or) syntactic structures like abstract syntax trees (ASTs) to detect code clones. However, they do not make sufficient use of the available structural and semantic information, hence limiting their capabilities. This paper addresses the problem of semantic code clone detection using program dependency graphs and geometric neural networks, leveraging the structured syntactic and semantic information. We have developed a prototype tool Holmes, based on our novel approach and empirically evaluated it on popular code clone benchmarks. Our results show that Holmes performs considerably better than the other state-of-the-art tool, TBCCD. We also assessed Holmes on unseen projects and performed cross dataset experiments to evaluate the generalizability of Holmes. Our results affirm that Holmes outperforms TBCCD since most of the pairs that Holmes detected were either undetected or suboptimally reported by TBCCD.
Steering a thermally activated micromotor with a nearby isothermal wall

Abstract Selective heating of a microparticle surface had been observed to cause its autonomous movement in a fluid medium due to self-generated temperature gradients. Here, we theoretically investigate the response of such an auto-thermophoretic particle near an isothermal planar wall. We derive an exact solution of the energy equation and employ the Lorentz reciprocal theorem to obtain the translational and rotational swimming velocities in the creeping-flow limit. We report fixed points for vertical movement of the micromotor for its specific orientations relative to the wall. The critical wall gap for fixed points shows unique non-monotonic dependence on the metallic coating coverage on the particle. Also, the micromotor trajectories can be switched either from wall-bound sliding or stationary state to escape from the near-wall zone by tuning the particle and the surrounding fluid pair thermal conductivity contrast. The scenario holds several exclusive distinguishing features from the otherwise extensively studied self-diffusiophoresis phenomenon near an inert wall, despite obvious analogies in the respective constitutive laws relating the fluxes with the gradients of the concerned forcing parameters. The most contrasting locomotion is the ability of a self-thermophoretic micromotor with a large heated cap to migrate towards the wall even if it is initially directed away from the wall. During the stationary states of swimming, the cold portion on the micromotor surface faces away from the wall under all conditions. Such unique aspects hold the potential of being harnessed in practice towards achieving intricate control over the autonomous motion of microparticles in thermally regulated fluidic environments.
Reinforcement learning with distance-based incentive/penalty (DIP) updates for highly constrained industrial control systems

Typical reinforcement learning (RL) methods show limited applicability for real-world industrial control problems because industrial systems involve various constraints and simultaneously require continuous and discrete control. To overcome these challenges, we devise a novel RL algorithm that enables an agent to handle a highly constrained action space. This algorithm has two main features. First, we devise two distance-based Q-value update schemes, incentive update and penalty update, in a distance-based incentive/penalty update technique to enable the agent to decide discrete and continuous actions in the feasible region and to update the value of these types of actions. Second, we propose a method for defining the penalty cost as a shadow price-weighted penalty. This approach affords two advantages compared to previous methods to efficiently induce the agent to not select an infeasible action. We apply our algorithm to an industrial control problem, microgrid system operation, and the experimental results demonstrate its superiority.
PLOP: Learning without Forgetting for Continual Semantic Segmentation

Deep learning approaches are nowadays ubiquitously used to tackle computer vision tasks such as semantic segmentation, requiring large datasets and substantial computational power. Continual learning for semantic segmentation (CSS) is an emerging trend that consists in updating an old model by sequentially adding new classes. However, continual learning methods are usually prone to catastrophic forgetting. This issue is further aggravated in CSS where, at each step, old classes from previous iterations are collapsed into the background. In this paper, we propose Local POD, a multi-scale pooling distillation scheme that preserves long- and short-range spatial relationships at feature level. Furthermore, we design an entropy-based pseudo-labelling of the background w.r. t. classes predicted by the old model to deal with background shift and avoid catastrophic forgetting of the old classes. Our approach, called PLOP, significantly outperforms state-of-the-art methods in existing CSS scenarios, as well as in newly proposed challenging benchmarks 1.
Learnable Boundary Guided Adversarial Training

Previous adversarial training raises model robustness under the compromise of accuracy on natural data. In this paper, we reduce natural accuracy degradation. We use the model logits from one clean model to guide learning of another one robust model, taking into consideration that logits from the well trained clean model embed the most discriminative features of natural data, e.g., generalizable classifier boundary. Our solution is to constrain logits from the robust model that takes adversarial examples as input and makes it similar to those from the clean model fed with corresponding natural data. It lets the robust model inherit the classifier boundary of the clean model. Moreover, we observe such boundary guidance can not only preserve high natural accuracy but also benefit model robustness, which gives new insights and facilitates progress for the adversarial community. Finally, extensive experiments on CIFAR-10, CIFAR-100, and Tiny ImageNet testify to the effectiveness of our method. We achieve new state-of-the-art robustness on CIFAR-100 without additional real or synthetic data with auto-attack benchmark 1. Our code is available at https://github.com/dvlab-research/LBGAT.
HAWQV3: Dyadic Neural Network Quantization

Quantization is one of the key techniques used to make Neural Networks (NNs) faster and more energy efficient. However, current low precision quantization algorithms often have the hidden cost of conversion back and forth from floating point to quantized integer values. This hidden cost limits the latency improvement realized by quantizing NNs. To address this, we present HAWQV3, a novel dyadic quantization framework. The contributions of HAWQV3 are the following. (i) The entire inference process consists of only integer multiplication, addition, and bit shifting in INT4/8 mixed precision, without any floating point operations/casting or even integer division. (ii) We pose the mixed-precision quantization as an integer linear programming problem, where the bit precision setting is computed to minimize model perturbation, while observing application specific constraints on memory footprint, latency, and BOPS. (iii) To verify our approach, we develop the first open source 4-bit mixed-precision quantization in TVM, and we directly deploy the quantized models to T4 GPUs using only the Turing Tensor Cores. We observe an average speed up of $1.45\times$ for uniform 4-bit, as compared to uniform 8-bit, precision for ResNet50. (iv) We extensively test the proposed dyadic quantization approach on multiple different NNs, including ResNet18/50 and InceptionV3, for various model compression levels with/without mixed precision. For instance, we achieve an accuracy of $78.50\%$ with dyadic INT8 quantization, which is more than $4\%$ higher than prior integer-only work for InceptionV3. Furthermore, we show that mixed-precision INT4/8 quantization can be used to achieve higher speed ups, as compared to INT8 inference, with minimal impact on accuracy. For example, for ResNet50 we can reduce INT8 latency by $23\%$ with mixed precision and still achieve $76.73\%$ accuracy.
Exploring the multimodal information from video content using deep learning features of appearance, audio and action for video recommendation

Following the popularisation of media streaming, a number of video streaming services are continuously buying new video content to mine the potential profit from them. As such, the newly added content has to be handled well to be recommended to suitable users. In this paper, we address the new item cold-start problem by exploring the potential of various deep learning features to provide video recommendations. The deep learning features investigated include features that capture the visual-appearance, audio and motion information from video content. We also explore different fusion methods to evaluate how well these feature modalities can be combined to fully exploit the complementary information captured by them. Experiments on a real-world video dataset for movie recommendations show that deep learning features outperform hand-crafted features. In particular, recommendations generated with deep learning audio features and action-centric deep learning features are superior to MFCC and state-of-the-art iDT features. In addition, the combination of various deep learning features with hand-crafted features and textual metadata yields significant improvement in recommendations compared to combining only the former.
Predictors of Opioid Prescription Among a Sample of Patients with Acute Musculoskeletal Pain at a Tertiary Care Hospital in Saudi Arabia

Background Musculoskeletal pain is one of the most complex and debilitating types of pain. Although different pharmacologic treatments are available, very few studies have explored the predictors for opioid analgesics prescription to manage this type of pain. Objective The aim of this study was to explore the predictors for opioid prescription in patients with acute musculoskeletal pain in Saudi Arabia. Methods This was a single-center, retrospective chart review of adult patients (≥18 yrs.) with an acute nociceptive musculoskeletal pain at a university-affiliated medical center in Riyadh, Saudi Arabia. Cancer patients and those with chronic neuropathic pain were excluded. Patients’ age, gender, number of comorbidities, duration of pain management, number of clinic visits for pain, and Numeric Pain Rating Scale (NPRS) scores at rest and with normal activities were collected. Multiple logistic regression was conducted to examine the relationship between the type of musculoskeletal pain and the prescription of opioid analgesics controlling for NPRS score on activity, age, gender, number of comorbidities, duration of pain treatment, and number of clinic visits for pain. Results The mean age of the 227 patients, who met the inclusion criteria, was 39 years and 68% of them were male. Sixty-three percent of the patients were prescribed opioid analgesics, and 61% of them had shoulder pain, 29% had back pain, and 10% had lower extremity pain (eg, hip, thigh, lower leg, knee, ankle, and foot pain). Tramadol was the most commonly prescribed opioid analgesic (82%), followed by codeine (13%). Ninety-seven percent of patients who were prescribed non-opioid analgesics had shoulder pain. Patients with shoulder pain had lower odds of receiving opioid analgesics (OR=0.019, P<0.0001, 95% CI=0.004–0.081) in comparison to their counterparts who had lower extremity or back pains. Moreover, the higher the pain score on activity was, the higher odds of receiving opioid analgesics (OR=1.317, P<0.0001, 95% CI=1.029–1.685). Conclusion Future studies should explore the impact of different opioid prescribing policies to improve the quality of patient care and reduce the unnecessary prescribing of opioids for patients with non-cancer musculoskeletal pain.
Direct Transcription for Dynamic Optimization: A Tutorial with a Case Study on Dual-Patient Ventilation During the COVID-19 Pandemic

A variety of optimal control, estimation, system identification and design problems can be formulated as functional optimization problems with differential equality and inequality constraints. Since these problems are infinitedimensional and often do not have a known analytical solution, one has to resort to numerical methods to compute an approximate solution. This paper uses a unifying notation to outline some of the techniques used in the transcription step of simultaneous direct methods (which discretize-then-optimize) for solving continuous-time dynamic optimization problems. We focus on collocation, integrated residual and Runge-Kutta schemes. These transcription methods are then applied to a simulation case study to answer a question that arose during the COVID-19 pandemic, namely: If there are not enough ventilators, is it possible to ventilate more than one patient on a single ventilator? The results suggest that it is possible, in principle, to estimate individual patient parameters sufficiently accurately, using a relatively small number of flow rate measurements, without needing to disconnect a patient from the system or needing more than one flow rate sensor. We also show that it is possible to ensure that two different patients can indeed receive their desired tidal volume, by modifying the resistance experienced by the air flow to each patient and controlling the ventilator pressure.
Transfer Learning for Oral Cancer Detection using Microscopic Images

Oral cancer has more than 83% survival rate if detected in its early stages, however, only 29% of cases are currently detected early. Deep learning techniques can detect patterns of oral cancer cells and can aid in its early detection. In this work, we present the first results of neural networks for oral cancer detection using microscopic images. We compare numerous state-of-the-art models via transfer learning approach and collect and release an augmented dataset of high-quality microscopic images of oral cancer. We present a comprehensive study of different models and report their performance on this type of data. Overall, we obtain a 10-15% absolute improvement with transfer learning methods compared to a simple Convolutional Neural Network baseline. Ablation studies show the added benefit of data augmentation techniques with finetuning for this task.
End-to-End Differentiable 6DoF Object Pose Estimation with Local and Global Constraints

Inferring the 6DoF pose of an object from a single RGB image is an important but challenging task, especially under heavy occlusion. While recent approaches improve upon the two stage approaches by training an end-to-end pipeline, they do not leverage local and global constraints. In this paper, we propose pairwise feature extraction to integrate local constraints, and triplet regularization to integrate global constraints for improved 6DoF object pose estimation. Coupled with better augmentation, our approach achieves state of the art results on the challenging Occlusion Linemod dataset, with a 9% improvement over the previous state of the art, and achieves competitive results on the Linemod dataset.
Vertical Phase Mixing across the Galactic Disk

By combining the Large Sky Area Multi-object Fiber Spectroscopic Telescope and Gaia data, we investigate the vertical phase mixing across the Galactic disk. Our results confirm the existence of phase space snail shells (or phase spirals) from 6–12 kpc. We find that grouping stars by the guiding radius (R g ), instead of the present radius (R), further enhances the snail shell signal in the following aspects: (1) clarity of the snail shell shape is increased; (2) more wraps of the snail shell can be seen; (3) the phase spaces are less affected by the lack of stars closer to the disk midplane due to extinction; and (4) the phase space snail shell is amplified in greater radial ranges. Compared to the R-based snail shell, the quantitatively measured shapes are similar, except that the R g -based snail shells show more wraps with a better contrast. These lines of evidence lead to the conclusion that the guiding radius (angular momentum) is a fundamental parameter tracing the phase space snail shell across the Galactic disk. Results of our test particle simulations with impulse approximation verify that particles grouped according to R g reveal well-defined and sharper snail shell features. By comparing the radial profiles of the snail shell pitch angle between the observation and simulation, the external perturbation can be constrained to ∼500−700 Myr ago. For future vertical phase mixing study, it is recommended to use the guiding radius with additional constraints on orbital hotness (ellipticity) to improve the clarity of the phase snail.
The Luminous Red Nova variety: AT 2020hat and AT 2020kog.

We present the results of our monitoring campaigns of the Luminous Red Novae (LRNe) AT 2020hat in NGC 5068 and AT 2020kog in NGC 6106. The two objects were imaged (and detected) before their discovery by routine survey operations. They show a general trend of slow luminosity rise lasting at least a few months. The subsequent major LRN outbursts were extensively followed in photometry and spectroscopy. The light curves present an initial short-duration peak, followed by a redder plateau phase. AT 2020kog is a moderately luminous event peaking at ~7x10^40 erg/s, while AT 2020hat is almost one order of magnitude fainter than AT 2020kog, although still more luminous than V838 Mon. In analogy with other LRNe, the spectra of AT 2020kog change significantly with time. They resemble those of Type IIn supernovae at early phases, then they become similar to those of K-type stars during the plateau, and to M-type stars at very late phases. In contrast, AT 2020hat shows a redder continuum already at early epochs, and its spectrum shows the late appearance of molecular bands. A moderate-resolution spectrum of AT 2020hat taken at +37 d after maximum shows a forest of narrow P Cygni lines of metals with velocities of 180 km/s, along with an Halpha emission with a full-width at half-maximum velocity of 250 km/s. For AT 2020hat, a robust constraint on its quiescent progenitor is provided by archival images of the Hubble Space Telescope. The progenitor is clearly detected as a mid-K type star, with an absolute magnitude M_F606W=-3.33+-0.09 mag and a colour F606W-F814W=1.14+-0.05 mag, which are inconsistent with the expectations from a massive star that could later produce a core-collapse supernova. Although quite peculiar, the two objects well match the progenitor vs. light curve absolute magnitude correlations discussed in the literature.
Comparison of Some Salivary Characteristics in Iraqi Children with Early Childhood Caries (ECC) and Children without Early Childhood Caries

Background Early Childhood Caries (ECC) is a unique form of caries that develops in temporary dentition. It has a multifactorial infectious disease. Saliva is one of the most important factors, which has an important protective effect against tooth decay when its multiple characteristics and functions are normal. The study aimed to compare some salivary characteristics in children with ECC and children without ECC. Materials and Methods Case-control, cross-sectional observational study. The 77 pre-schoolers aged 37 to 72 months (12 with ECC, 26 with ECC-S, and 39 without ECC) examined and we collected the stimulated saliva. The pH microelectrode was used to determine pH and buffer capacity; a formula that involves volume, collection time, and specific saliva weight was used to test the salivary flow rate. The potentiometric and phosphate methods were used to determine fluoride through spectrophotometric, colorimetric absorption techniques. Results The results got to show that there are no statistically significant differences in pH, buffer capacity, salivary flow rate, and levels of fluoride and phosphate, in children with ECC, ECC-S, and without ECC. The risk factors, such as mother’s education, bottle use, brushing frequency, and previous dental care of the child are more important at the time of developing ECC than some salivary variables. Conclusion The risk factors, such as mother’s education, bottle use, brushing frequency, and previous dental care of the child are more important at the time of developing ECC than some salivary variables such as pH, buffer capacity, salivary flow rate, and levels of fluoride and phosphate.
Stochastic Optimization with Decision-Dependent Distributions

Stochastic optimization problems often involve data distributions that change in reaction to the decision variables. This is the case, for example, when members of the population respond to a deployed classifier by manipulating their features so as to improve the likelihood of being positively labeled. Recent works on performative prediction identify an intriguing solution concept for such problems: find the decision that is optimal with respect to the static distribution that the decision induces. Continuing this line of work, we show that, in the strongly convex setting, typical stochastic algorithms—originally designed for static problems—can be applied directly for finding such equilibria with little loss in efficiency. The reason is simple to explain: the main consequence of the distributional shift is that it corrupts algorithms with a bias that decays linearly with the distance to the solution. Using this perspective, we obtain convergence guarantees for popular algorithms, such as stochastic gradient, clipped gradient, prox-point, and dual averaging methods, along with their accelerated and proximal variants. In realistic applications, deployment of a decision rule is often much more expensive than sampling. We show how to modify the aforementioned algorithms so as to maintain their sample efficiency when performing only logarithmically many deployments.