Miracle-dz/UniFAR-FTUs huggingface dataset:
Benefit of deep learning with non-convex noisy gradient descent: Provable excess risk bound and superiority to kernel methods

Establishing a theoretical analysis that explains why deep learning can outperform shallow learning such as kernel methods is one of the biggest issues in the deep learning literature. Towards answering this question, we evaluate excess risk of a deep learning estimator trained by a noisy gradient descent with ridge regularization on a mildly overparameterized neural network, and discuss its superiority to a class of linear estimators that includes neural tangent kernel approach, random feature model, other kernel methods, $k$-NN estimator and so on. We consider a teacher-student regression model, and eventually show that any linear estimator can be outperformed by deep learning in a sense of the minimax optimal rate especially for a high dimension setting. The obtained excess bounds are so-called fast learning rate which is faster than $O(1/\sqrt{n})$ that is obtained by usual Rademacher complexity analysis. This discrepancy is induced by the non-convex geometry of the model and the noisy gradient descent used for neural network training provably reaches a near global optimal solution even though the loss landscape is highly non-convex. Although the noisy gradient descent does not employ any explicit or implicit sparsity inducing regularization, it shows a preferable generalization performance that dominates linear estimators.
Mechanical System Inspired Microscopic Traffic Model: Modeling, Analysis, and Validation

In this paper, we develop a mechanical system inspired microscopic traffic model to characterize the longitudinal interaction among a chain of vehicles. In particular, we propose a mass-spring-damper-clutch based car-following (CF) model that can naturally capture the car-following behavior of a rational driver. Specifically, the spring and damper can well characterize the driver’s tendency to maintain the same speed as the vehicle ahead while keeping a (speed-dependent) desired spacing. It is also capable of characterizing the impact of the following vehicle on the preceding vehicle, which is generally neglected in existing models. A new string stability criterion is defined for the considered multi-vehicle dynamics, and stability analysis is performed on the system parameters and time delays. An efficient online parameter identification algorithm, sequential recursive least squares with inverse QR decomposition (SRLS-IQR), is developed to estimate the driving-related model parameters. These real-time estimated parameters can be employed in advanced longitudinal control systems to enable accurate prediction of vehicle trajectories for improved safety and fuel efficiency. The proposed model and the parameter identification algorithm are validated on NGSIM, a naturalistic driving dataset, as well as our own connected vehicle driving data. Promising performance is demonstrated.
The Selection of Fluid Catalytic Cracking Catalysts and Additives: The Significance of Attrition Studies

Fluid catalytic cracking (FCC) is an important secondary conversion process in refinery that converts heavier gas oils and residues to valuable petroleum products. In the present and future crude oil availability scenario demand for FCC process that increases the refinery margins is growing. Continuous attempts are being made to improve FCC margin through process optimization, selection and use of improved catalysts and additives as well as hardware modifications. Refiners often face challenging task of judiciously selecting and switching to improved catalysts and additives for maximizing profits without violating hardware constraints. It is very difficult to select catalyst or additives based on vendor's claims and testing the catalyst directly in the plant is also risky business. It is important to match the plant performance closely to understand unit constraints and exploit the new catalyst capabilities to full extent. Detailed study with respect to catalyst deactivation, microactivity studies, complete characterization, and prediction of performance on the commercial scale, as well as economic evaluation, are key steps in catalyst or additive selection. Catalyst attrition is key issue now days due to heavy losses observed in the plant and frequent shut downs of the CO boiler units. In riser and regenerator reactor of FCC process, catalyst undergoes attrition due to various reasons such as inherent characteristics of catalyst/additive or due to process environment. Poor attrition of either catalyst/additive result into catalyst loss, fouling, and plugging of down stream units. In this communication the attrition behavior of different catalysts and additives and combined effect of base fluid catalytic cracking catalyst along with different additives in various proportions have been evaluated using an ASTM method in laboratory. Attrition behavior of different FCC additives such as CO combustion promoter, Octane and LPG booster and gasoline sulfur reduction additives were measured in isolation as well as by mixing with base catalyst in different proportion. Certain when studied alone exhibited higher attrition when compared with others. It is observed that even though absolute attrition strength of certain additives such as gasoline sulfur is poor, when used along with base catalyst in desired proportion the loss due to attrition was minimized. This study in combination with VGO cracking activity results helps to select suitable catalyst/additive for enhancing FCC process profits.
1,3-Bis(4-methyl­benz­yl)benzimidazolium chloride monohydrate

The cation in the title compound, C23H23N2+·Cl−·H2O, features a benzimidazole ring connected to two 4-methyl­benzyl rings via methyl­ene bridges. The crystal structure is stabilized by C—H⋯O, C—H⋯Cl and O—H⋯Cl hydrogen-bonding inter­actions, leading to a three-dimensional framework.
Study of Catalytic Hydrogenation and Methanol Addition to α-Methylene-γ-Lactone of Eremanthine Derivatives

The sesquiterpene lactones guaia-1(10),11(13)-dieno-4α-hydroxy,9α-acetyl-15-iodine-12,6α-lactone (2), guaia-1(10),4(15),11(13)-trieno-9α-hydroxy-12,6α-lactone (3), (11S)-guaia-4(15),10(14)-dieno-9α-hydroxy-13-methoxy-12,6α-lactone (4), (11S)-guai-1(10)-eno-4α,9α-dihydroxy-13-methoxy-12,6α-lactone (5), and guaia-1(10),11(13)-dieno-4α,9α-dihydroxy-15-iodine-12,6α-lactone (6) were previously obtained starting from the natural product eremanthine (1). In this paper we report the catalytic hydrogenation reactions of allylic derivatives 2–5 and the methanol addition to α-methylene-γ-lactone of the iodohydrin 6.
A Peat Moss-Based Technology for Mitigating Residues of the Explosives TNT, RDX, and HMX in Soil

There is increased interest in how to balance military preparedness and environmental protection at Department of Defense (DoD) facilities. This research evaluated a peat moss-based technology to enhance the adsorption and biodegradation of explosive residues at military testing and training ranges. The evaluation was performed using 30-cm-long soil columns operated under unsaturated flow conditions. The treatment materials were placed at the soil surface, and soil contaminated with 2,4,6-trinitrotoluene (TNT), hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX), and octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX) was spread over the surface. Simulated rainfall initiated dissolution and leaching of the explosive compounds, which was monitored at several depths within the columns. Peat moss plus soybean oil reduced the soluble concentrations of TNT, RDX and HMX detected at 10 cm depth by 100%, 60%, and 40%, respectively, compared to the no-treatment control column. Peat moss alone reduced TNT and HMX concentrations at 10 cm depth relative to the control, but exhibited higher soluble RDX concentrations by the end of the experiment. Concentrations of HMX and RDX were also reduced at 30 cm depth by the peat moss plus soybean oil treatments relative to those observed in the control column. These preliminary results demonstrate proof-of-concept of a low cost technology for reducing the contamination of groundwater with explosives at military test and training ranges.
Small clusters of ii-vi materials

Los materiales II-VI tiene muchas aplicaciones hoy en dia gracias a sus propiedades semiconductoras. Una de estas aplicaciones es el desarrollo de celulas solares, debido a que el salto de energia de estos materiales es adecuado para captar la luz solar. Sin embargo, las propiedades del sistema cristalino no tienen por que ser las mismas que las propiedades de los clusters del mismo material. Debido a ello hemos decidido estudiar las propiedades de estos clusters II-VI en cuanto a las geometrias que caracterizan los minimos globales y las propiedades opticas de estas estructuras. Hemos observado asi que las estructuras de clusters de tamano pequeno son de tipo anillo, mientras que las estructuras de clusters de tamano mayores son de tipo esferoide tridimensional. En el caso de los clusters ZniOi la transicion ocurre para i=7, y en el caso de los clusters ZniXi, siendo Z=S, Se, Te, la transicion ocurre para i=5. Por otro lado se han calculado las energias de cohesion para cada sistema, teniendo en cuenta extrapolaciones teoricas a partir de los resultados de los clusters tridimensionales. Los resultados obtenidos ofrecen un acuerdo remarcable con los datos experimentales. A partir de estas estructuras se caracterizaron las energias de excitacion electronicas. En el caso de los clusters ZniOi se obtuvieron los saltos de energia mayores, mientras que para el ZniTei las menores. Asi mismo se observo que las energias de excitacion de las estructuras anillo eran mayores que la de los esferoides. Basandonos en estos datos podemos afirmar que los esferoides de ZniTei podrian llegar a tener una gran aplicacion en el desarrollo de celulas solares.
Prognostic and Predictive Value of a 15 Transcription Factors (TFs) Panel for Hepatocellular Carcinoma

Purpose Hepatocellular carcinoma (HCC) is one of the most devastating diseases worldwide. Limited performance of clinicopathologic parameters as prognostic factors underscores more accurate and effective biomarkers for high-confidence prognosis that guide decision-making for optimal treatment of HCC. The aim of the present study was to establish a novel panel to improve prognosis prediction of HCC patients, with a particular interest in transcription factors (TFs). Materials and Methods A TF-related prognosis model of liver cancer with data from ICGC-LIRP-JI cohort successively were processed by univariate and multivariate Cox regression analysis. Then, for evaluating the prognostic prediction value of the model, receiver operating characteristic (ROC) curve and survival analysis were performed both with internal data from the International Cancer Genome Consortium (ICGC) and external data from The Cancer Genome Atlas (TCGA). Furthermore, we verified the expression of three genes in HCC cell lines by Western blot and qPCR and protein expression level by IHC in liver cancer patients’ sample. Finally, we constructed a TF clinical characteristics nomogram to furtherly predict liver cancer patient survival probability with TCGA cohort. Results By Cox regression analysis, a panel of 15 TFs (ZNF331, MYCN, AHRR, LEF1, ZNF780A, POU1F1, DLX5, ZNF775, PLSCR1, FOXK1, TAL2, ZNF558, SOX9, TCFL5, GSC) was identified to present with powerful predictive performance for overall survival of HCC patients based on internal ICGC cohort and external TCGA cohort. A nomogram that integrates these factors was established, allowing efficient prediction of survival probabilities and displaying higher clinical utility. Conclusion The 15-TF panel is an independent prognostic factor for HCC, and 15 TF-based nomogram might provide implication an effective approach for HCC patient management and treatment.
Serratus anterior plane block versus intercostal nerve block for postoperative analgesic effect after video-assisted thoracoscopic lobectomy

Abstract Background: Serratus anterior plane block (SAPB) and intercostal nerve block (ICNB) are attractive options for multimodal analgesia in patients undergoing thoracoscopic surgery, but which block is superior remains unclear. Objective: The present study aimed to assess the effect of SAPB versus ICNB on reducing postoperative pain after video-assisted thoracoscopic surgery (VATS) for lobectomy. Methods: This prospective, randomized, active-intervention-controlled, subject-assessor-blinded, single-center, parallel-group trial allocated 18- to 80-year-old patients with American Society of Anesthesiologists status I to III to receive either SAPB or ICNB in a 1:1 ratio. The primary outcome was numerical pain rating scale (NRS) scores during the first 24 hours postoperatively. The secondary outcomes were postoperative cumulative doses of ketorolac and fentanyl, and the occurrence of postoperative adverse effects. Results: Among the 57 patients assessed for eligibility, 50 were randomly assigned in a 1:1 ratio. Due to conversion to open thoracotomy and patient-controlled analgesia pump failure, 4 patients were excluded and 46 were analyzed. The intergroup differences in NRS scores were not statistically significant at any time point. Cumulative consumptions of fentanyl and ketorolac were also not significantly different. No perioperative adverse events occurred. Postoperative complications were also absent, except for nausea (2/23 patients [8.7% in both groups]). Conclusion: Results of the present study do not clarify whether SAPB or ICNB has a superior effect on reducing pain after VATS, thereby suggesting they may exert similar analgesic effects.
Generative Adversarial User Privacy in Lossy Single-Server Information Retrieval

We propose to extend the concept of private information retrieval by allowing for distortion in the retrieval process and relaxing the perfect privacy requirement at the same time. In particular, we study the trade-off between download rate, distortion, and user privacy leakage, and show that in the limit of large file sizes this trade-off can be captured via a novel information-theoretical formulation for datasets with a known distribution. Moreover, for scenarios where the statistics of the dataset is unknown, we propose a new deep learning framework by leveraging a generative adversarial network approach, which allows the user to learn efficient schemes from the data itself. We evaluate the performance of the scheme on a synthetic Gaussian dataset as well as on the MNIST, CIFAR-10, and LSUN datasets. For the MNIST, CIFAR-10, and LSUN datasets, the data-driven approach significantly outperforms a nonlearning-based scheme which combines source coding with the download of multiple files.
When Do Curricula Work?

Inspired by human learning, researchers have proposed ordering examples during training based on their difficulty. Both curriculum learning, exposing a network to easier examples early in training, and anti-curriculum learning, showing the most difficult examples first, have been suggested as improvements to the standard i.i. d. training. In this work, we set out to investigate the relative benefits of ordered learning. We first investigate the \emph{implicit curricula} resulting from architectural and optimization bias and find that samples are learned in a highly consistent order. Next, to quantify the benefit of \emph{explicit curricula}, we conduct extensive experiments over thousands of orderings spanning three kinds of learning: curriculum, anti-curriculum, and random-curriculum -- in which the size of the training dataset is dynamically increased over time, but the examples are randomly ordered. We find that for standard benchmark datasets, curricula have only marginal benefits, and that randomly ordered samples perform as well or better than curricula and anti-curricula, suggesting that any benefit is entirely due to the dynamic training set size. Inspired by common use cases of curriculum learning in practice, we investigate the role of limited training time budget and noisy data in the success of curriculum learning. Our experiments demonstrate that curriculum, but not anti-curriculum can indeed improve the performance either with limited training time budget or in existence of noisy data.
Parallel Training of Deep Networks with Local Updates

Deep learning models trained on large data sets have been widely successful in both vision and language domains. As state-of-the-art deep learning architectures have continued to grow in parameter count so have the compute budgets and times required to train them, increasing the need for compute-efficient methods that parallelize training. Two common approaches to parallelize the training of deep networks have been data and model parallelism. While useful, data and model parallelism suffer from diminishing returns in terms of compute efficiency for large batch sizes. In this paper, we investigate how to continue scaling compute efficiently beyond the point of diminishing returns for large batches through local parallelism, a framework which parallelizes training of individual layers in deep networks by replacing global backpropagation with truncated layer-wise backpropagation. Local parallelism enables fully asynchronous layer-wise parallelism with a low memory footprint, and requires little communication overhead compared with model parallelism. We show results in both vision and language domains across a diverse set of architectures, and find that local parallelism is particularly effective in the high-compute regime.
Uniform Central Limit Theorem for self normalized sums in high dimensions

In this article, we are interested in the normal approximation of the self-normalized random vector $\Big(\frac{\sum_{i=1}^{n}X_{i1}}{\sqrt{\sum_{i=1}^{n}X_{i1}^2}},\dots,\frac{\sum_{i=1}^{n}X_{ip}}{\sqrt{\sum_{i=1}^{n}X_{ip}^2}}\Big)$ in $\mathcal{R}^p$ uniformly over the class of hyper-rectangles $\mathcal{A}^{re}=\{\prod_{j=1}^{p}[a_j,b_j]\cap\mathcal{R}:-\infty\leq a_j\leq b_j\leq \infty, j=1,\ldots,p\}$, where $X_1,\dots,X_n$ are non-degenerate independent $p-$dimensional random vectors with each having independent and identically distributed (iid) components. We investigate the optimal cut-off rate of $\log p$ in the uniform central limit theorem (UCLT) under variety of moment conditions. When $X_{ij}$'s have $(2+\delta)$th absolute moment for some $0< \delta\leq 1$, the optimal rate of $\log p$ is $o\big(n^{\delta/(2+\delta)}\big)$. When $X_{ij}$'s are independent and identically distributed (iid) across $(i,j)$, even $(2+\delta)$th absolute moment of $X_{11}$ is not needed. Only under the condition that $X_{11}$ is in the domain of attraction of the normal distribution, the growth rate of $\log p$ can be made to be $o(\eta_n)$ for some $\eta_n\rightarrow 0$ as $n\rightarrow \infty$. We also establish that the rate of $\log p$ can be pushed to $\log p =o(n^{1/2})$ if we assume the existence of fourth moment of $X_{ij}$'s. By an example, it is shown however that the rate of growth of $\log p$ can not further be improved from $n^{1/2}$ as a power of $n$. As an application, we found respective versions of the high dimensional UCLT for component-wise Student's t-statistic. An important aspect of the these UCLTs is that it does not require the existence of some exponential moments even when dimension $p$ grows exponentially with some power of $n$, as opposed to the UCLT of normalized sums. Only the existence of some absolute moment of order $\in [2,4]$ is sufficient.
New Determination of Fundamental Properties of Palomar 5 Using Deep DESI Imaging Data

The legacy imaging surveys for the Dark Energy Spectroscopic Instrument project provides multiple-color photometric data, which are about 2 mag deeper than those from the SDSS. In this study, we redetermine the fundamental properties for an old halo globular cluster of Palomar 5 based on these new imaging data, including structure parameters, stellar population parameters, and luminosity and mass functions. These characteristics, together with its tidal tails, are key for dynamical studies of the cluster and constraining the mass model of the Milky Way. By fitting the King model to the radial surface density profile of Palomar 5, we derive the core radius of , tidal radius of , and concentration parameter of c = 0.78 ± 0.04. We apply a Bayesian analysis method to derive the stellar population properties and get an age of 11.508 ± 0.027 Gyr, metallicity of [Fe/H] = −1.798 ± 0.014, reddening of E(B − V) = 0.0552 ± 0.0005, and distance modulus of . The main-sequence luminosity and mass functions for both the cluster center and tidal tails are investigated. The luminosity and mass functions at different distances from the cluster center suggest that there is obvious spatial mass segregation. Many faint low-mass stars have been evaporated at the cluster center, and the tidal tails are enhanced by low-mass stars. Both the concentration and relaxation times suggest that Palomar 5 is a totally relaxed system.
VideoMix: Rethinking Data Augmentation for Video Classification

State-of-the-art video action classifiers often suffer from overfitting. They tend to be biased towards specific objects and scene cues, rather than the foreground action content, leading to sub-optimal generalization performances. Recent data augmentation strategies have been reported to address the overfitting problems in static image classifiers. Despite the effectiveness on the static image classifiers, data augmentation has rarely been studied for videos. For the first time in the field, we systematically analyze the efficacy of various data augmentation strategies on the video classification task. We then propose a powerful augmentation strategy VideoMix. VideoMix creates a new training video by inserting a video cuboid into another video. The ground truth labels are mixed proportionally to the number of voxels from each video. We show that VideoMix lets a model learn beyond the object and scene biases and extract more robust cues for action recognition. VideoMix consistently outperforms other augmentation baselines on Kinetics and the challenging Something-Something-V2 benchmarks. It also improves the weakly-supervised action localization performance on THUMOS'14. VideoMix pretrained models exhibit improved accuracies on the video detection task (AVA).
Explainable Artificial Intelligence: How Subsets of the Training Data Affect a Prediction

There is an increasing interest in and demand for interpretations and explanations of machine learning models and predictions in various application areas. In this paper, we consider data-driven models which are already developed, implemented and trained. Our goal is to interpret the models and explain and understand their predictions. Since the predictions made by data-driven models rely heavily on the data used for training, we believe explanations should convey information about how the training data affects the predictions. To do this, we propose a novel methodology which we call Shapley values for training data subset importance. The Shapley value concept originates from coalitional game theory, developed to fairly distribute the payout among a set of cooperating players. We extend this to subset importance, where a prediction is explained by treating the subsets of the training data as players in a game where the predictions are the payouts. We describe and illustrate how the proposed method can be useful and demonstrate its capabilities on several examples. We show how the proposed explanations can be used to reveal biasedness in models and erroneous training data. Furthermore, we demonstrate that when predictions are accurately explained in a known situation, then explanations of predictions by simple models correspond to the intuitive explanations. We argue that the explanations enable us to perceive more of the inner workings of the algorithms, and illustrate how models producing similar predictions can be based on very different parts of the training data. Finally, we show how we can use Shapley values for subset importance to enhance our training data acquisition, and by this reducing prediction error.