Predict-then-Calibrate: A New Perspective of Robust Contextual LP

Contextual optimization, also known as predict-then-optimize or prescriptive analytics, considers an optimization problem with the presence of covariates (context or side information). The goal is to learn a prediction model (from the training data) that predicts the objective function from the covariates, and then in the test phase, solve the optimization problem with the covariates but without the observation of the objective function. In this paper, we consider a risk-sensitive version of the problem and propose a generic algorithm design paradigm called predict-then-calibrate. The idea is to first develop a prediction model without concern for the downstream risk profile or robustness guarantee, and then utilize calibration (or recalibration) methods to quantify the uncertainty of the prediction. While the existing methods suffer from either a restricted choice of the prediction model or strong assumptions on the underlying data, we show the disentangling of the prediction model and the calibration/uncertainty quantification has several advantages. First, it imposes no restriction on the prediction model and thus fully unleashes the potential of off-the-shelf machine learning methods. Second, the derivation of the risk and robustness guarantee can be made independent of the choice of the prediction model through a data-splitting idea. Third, our paradigm of predict-then-calibrate applies to both (risk-sensitive) robust and (risk-neutral) distributionally robust optimization (DRO) formulations. Theoretically, it gives new generalization bounds for the contextual LP problem and sheds light on the existing results of DRO for contextual LP. Numerical experiments further reinforce the advantage of the predict-then-calibrate paradigm in that an improvement on either the prediction model or the calibration model will lead to a better final performance.Comment: 30 pages, 8 figure

Acid Sphingomyelinase Regulates the Localization and Trafficking of Palmitoylated Proteins

In human, loss of Acid Sphingomeylinase (ASM/SMPD1) causes Niemann-Pick Disease, type A. ASM hydrolyzes sphingomyelins to produce ceramides but protein targets of ASM remain largely unclear. ... See full text for complete abstract

Mobile Charging as a Service: A Reservation-Based Approach

This paper aims to design an intelligent mobile charging control mechanism for Electric Vehicles (EVs), by promoting charging reservations (including service start time, expected charging time, and charging location, etc.). EV mobile charging could be implemented as an alternative recharging solution, wherein charge replenishment is provided by economically mobile plug-in chargers, capable of providing on-site charging services. With intelligent charging management, readily available mobile chargers are predictable and could be efficiently scheduled towards EVs with charging demand, based on updated context collected from across the charging network. The context can include critical information relating to charging sessions as well as charging demand, etc. Further with reservations introduced, accurate estimations on charging demand for a future moment are achievable, and correspondingly, optimal mobile chargersselection can be obtained. Therefore, charging demands across the network can be efficiently and effectively satisfied, with the support of intelligent system-level decisions. In order to evaluate critical performance attributes, we further carry out extensive simulation experiments with practical concerns to verify our insights observed from the theoretical analysis. Results show great performance gains by promoting the reservation-based mobile charger-selection, especially for mobile chargers equipped with suffice power capacity

The application of artificial intelligence in glaucoma diagnosis and prediction

Artificial intelligence is a multidisciplinary and collaborative science, the ability of deep learning for image feature extraction and processing gives it a unique advantage in dealing with problems in ophthalmology. The deep learning system can assist ophthalmologists in diagnosing characteristic fundus lesions in glaucoma, such as retinal nerve fiber layer defects, optic nerve head damage, optic disc hemorrhage, etc. Early detection of these lesions can help delay structural damage, protect visual function, and reduce visual field damage. The development of deep learning led to the emergence of deep convolutional neural networks, which are pushing the integration of artificial intelligence with testing devices such as visual field meters, fundus imaging and optical coherence tomography to drive more rapid advances in clinical glaucoma diagnosis and prediction techniques. This article details advances in artificial intelligence combined with visual field, fundus photography, and optical coherence tomography in the field of glaucoma diagnosis and prediction, some of which are familiar and some not widely known. Then it further explores the challenges at this stage and the prospects for future clinical applications. In the future, the deep cooperation between artificial intelligence and medical technology will make the datasets and clinical application rules more standardized, and glaucoma diagnosis and prediction tools will be simplified in a single direction, which will benefit multiple ethnic groups

Analyzing the Hardware-Software Implications of Multi-modal DNN Workloads using MMBench

The explosive growth of various types of big data and advances in AI technologies have catalyzed a new type of applications called multi-modal DNNs. Multi-modal DNNs are capable of interpreting and reasoning about information from multiple modalities, making them more applicable to real-world AI scenarios. In recent research, multi-modal DNNs have outperformed the best uni-modal DNN in a wide range of applications from traditional multimedia to emerging autonomous systems. However, despite their importance and superiority, very limited research attention has been devoted to understand the characteristics of multi-modal DNNs and their implications on current computing software/hardware platforms. To facilitate research and advance the understanding of these multi-modal DNN workloads, we first present MMbench, an open-source benchmark suite consisting of a set of real-world multi-modal DNN workloads with relevant performance metrics for evaluation. Then we use MMbench to conduct an in-depth analysis on the characteristics of multi-modal DNNs. We study their implications on application and programming framework, operating and scheduling system, as well as execution hardware. Finally, we conduct a case study and extend our benchmark to edge devices. We hope that our work can provide guidance for future software/hardware design and optimization to underpin multi-modal DNNs on both cloud and edge computing platforms

LiSum: Open Source Software License Summarization with Multi-Task Learning

Open source software (OSS) licenses regulate the conditions under which users can reuse, modify, and distribute the software legally. However, there exist various OSS licenses in the community, written in a formal language, which are typically long and complicated to understand. In this paper, we conducted a 661-participants online survey to investigate the perspectives and practices of developers towards OSS licenses. The user study revealed an indeed need for an automated tool to facilitate license understanding. Motivated by the user study and the fast growth of licenses in the community, we propose the first study towards automated license summarization. Specifically, we released the first high quality text summarization dataset and designed two tasks, i.e., license text summarization (LTS), aiming at generating a relatively short summary for an arbitrary license, and license term classification (LTC), focusing on the attitude inference towards a predefined set of key license terms (e.g., Distribute). Aiming at the two tasks, we present LiSum, a multi-task learning method to help developers overcome the obstacles of understanding OSS licenses. Comprehensive experiments demonstrated that the proposed jointly training objective boosted the performance on both tasks, surpassing state-of-the-art baselines with gains of at least 5 points w.r.t. F1 scores of four summarization metrics and achieving 95.13% micro average F1 score for classification simultaneously. We released all the datasets, the replication package, and the questionnaires for the community

Synthesis of electroneutralized amphiphilic copolymers with peptide dendrons for intramuscular gene delivery

Intramuscular gene delivery materials are of great importance in plasmid-based gene therapy system, but there is limited information so far on how to design and synthesize them. A previous study showed that the peptide dendron-based triblock copolymer with its components arranged in a reversed biomembrane architecture could significantly increase intramuscular gene delivery and expression. Herein, we wonder whether copolymers with biomembrane-mimicking arrangement may have similar function on intramuscular gene delivery. Meanwhile, it is of great significance to uncover the influence of electric charge and molecular structure on the function of the copolymers. To address the issues, amphiphilic triblock copolymers arranged in hydrophilic-hydrophobic-hydrophilic structure were constructed despite the paradoxical characteristics and difficulties in synthesizing such hydrophilic but electroneutral molecules. The as-prepared two copolymers, dendronG2(l-lysine-OH)-poly propylene glycol2k(PPG2k)-dendronG2(l-lysine-OH) (rL2PL2) and dendronG3(l-lysine-OH)-PPG2k-dendronG3(l-lysine-OH) (rL3PL3), were in similar structure but had different hydrophilic components and surface charges, thus leading to different capabilities in gene delivery and expression in skeletal muscle. rL2PL2 was more efficient than Pluronic L64 and rL3PL3 when mediating luciferase, β-galactosidase, and fluorescent protein expressions. Furthermore, rL2PL2-mediated growth-hormone-releasing hormone expression could significantly induce mouse body weight increase in the first 21 days after injection. In addition, both rL2PL2 and rL3PL3 showed good in vivo biosafety in local and systemic administration. Altogether, rL2PL2-mediated gene expression in skeletal muscle exhibited applicable potential for gene therapy. The study revealed that the molecular structure and electric charge were critical factors governing the function of the copolymers for intramuscular gene delivery. It can be concluded that, combined with the previous study, both structural arrangements either reverse or similar to the biomembrane are effective in designing such copolymers. It also provides an innovative way in designing and synthesizing new electroneutralized triblock copolymers, which could be used safely and efficiently for intramuscular gene delivery

Volatility of Secondary Organic Aerosol from β-Caryophyllene Ozonolysis over a Wide Tropospheric Temperature Range

We investigated secondary organic aerosol (SOA) from β-caryophyllene oxidation generated over a wide tropospheric temperature range (213–313 K) from ozonolysis. Positive matrix factorization (PMF) was used to deconvolute the desorption data (thermograms) of SOA products detected by a chemical ionization mass spectrometer (FIGAERO-CIMS). A nonmonotonic dependence of particle volatility (saturation concentration at 298 K, C298K*) on formation temperature (213–313 K) was observed, primarily due to temperature-dependent formation pathways of β-caryophyllene oxidation products. The PMF analysis grouped detected ions into 11 compound groups (factors) with characteristic volatility. These compound groups act as indicators for the underlying SOA formation mechanisms. Their different temperature responses revealed that the relevant chemical pathways (e.g., autoxidation, oligomer formation, and isomer formation) had distinct optimal temperatures between 213 and 313 K, significantly beyond the effect of temperature-dependent partitioning. Furthermore, PMF-resolved volatility groups were compared with volatility basis set (VBS) distributions based on different vapor pressure estimation methods. The variation of the volatilities predicted by different methods is affected by highly oxygenated molecules, isomers, and thermal decomposition of oligomers with long carbon chains. This work distinguishes multiple isomers and identifies compound groups of varying volatilities, providing new insights into the temperature-dependent formation mechanisms of β-caryophyllene-derived SOA particles