Personalized DP-SGD using Sampling Mechanisms

Personalized privacy becomes critical in deep learning for Trustworthy AI. While Differentially Private Stochastic Gradient Descent (DP-SGD) is widely used in deep learning methods supporting privacy, it provides the same level of privacy to all individuals, which may lead to overprotection and low utility. In practice, different users may require different privacy levels, and the model can be improved by using more information about the users with lower privacy requirements. There are also recent works on differential privacy of individuals when using DP-SGD, but they are mostly about individual privacy accounting and do not focus on satisfying different privacy levels. We thus extend DP-SGD to support a recent privacy notion called ($\Phi$,$\Delta$)-Personalized Differential Privacy (($\Phi$,$\Delta$)-PDP), which extends an existing PDP concept called $\Phi$-PDP. Our algorithm uses a multi-round personalized sampling mechanism and embeds it within the DP-SGD iterations. Experiments on real datasets show that our algorithm outperforms DP-SGD and simple combinations of DP-SGD with existing PDP mechanisms in terms of model performance and efficiency due to its embedded sampling mechanism.Comment: 10 pages, 5 figure

Inspector Gadget: A Data Programming-based Labeling System for Industrial Images

As machine learning for images becomes democratized in the Software 2.0 era, one of the serious bottlenecks is securing enough labeled data for training. This problem is especially critical in a manufacturing setting where smart factories rely on machine learning for product quality control by analyzing industrial images. Such images are typically large and may only need to be partially analyzed where only a small portion is problematic (e.g., identifying defects on a surface). Since manual labeling these images is expensive, weak supervision is an attractive alternative where the idea is to generate weak labels that are not perfect, but can be produced at scale. Data programming is a recent paradigm in this category where it uses human knowledge in the form of labeling functions and combines them into a generative model. Data programming has been successful in applications based on text or structured data and can also be applied to images usually if one can find a way to convert them into structured data. In this work, we expand the horizon of data programming by directly applying it to images without this conversion, which is a common scenario for industrial applications. We propose Inspector Gadget, an image labeling system that combines crowdsourcing, data augmentation, and data programming to produce weak labels at scale for image classification. We perform experiments on real industrial image datasets and show that Inspector Gadget obtains better performance than other weak-labeling techniques: Snuba, GOGGLES, and self-learning baselines using convolutional neural networks (CNNs) without pre-training.Comment: 10 pages, 11 figure

Double Glomus Tumors Originating in the Submandibular and Parotid Regions

Glomus tumors are rare neoplasms that originate from the glomus bodies, an arteriovenous anastomosis with a specialized vascular structure. The most common site for these tumors is the subungal region of the fingers. Occasionally, glomus tumors are found in the middle ear, trachea, nasal cavities, stomach, and lungs. The occurrence in the parotid regions is very rare. While multiple glomus tumors in the whole body are thought to represent only 10% of all cases, instances of multiple tumors in the neck have not yet been reported in the literature. We report a case of double glomus tumors in the submandibular and parotid regions

Redactor: A Data-Centric and Individualized Defense against Inference Attacks

Information leakage is becoming a critical problem as various information becomes publicly available by mistake, and machine learning models train on that data to provide services. As a result, one's private information could easily be memorized by such trained models. Unfortunately, deleting information is out of the question as the data is already exposed to the Web or third-party platforms. Moreover, we cannot necessarily control the labeling process and the model trainings by other parties either. In this setting, we study the problem of targeted disinformation generation where the goal is to dilute the data and thus make a model safer and more robust against inference attacks on a specific target (e.g., a person's profile) by only inserting new data. Our method finds the closest points to the target in the input space that will be labeled as a different class. Since we cannot control the labeling process, we instead conservatively estimate the labels probabilistically by combining decision boundaries of multiple classifiers using data programming techniques. Our experiments show that a probabilistic decision boundary can be a good proxy for labelers, and that our approach is effective in defending against inference attacks and can scale to large data

Remarks on the Pocklington and Padró-Sáez Cube Root Algorithm in Fq\mathbb F_q

We clarify and generalize a cube root algorithm in $\mathbb F_q$ proposed by Pocklington, and later rediscovered by Padró and Sáez. We correct some mistakes in the result of Padró and Sáez and give a full generalization of their result. We also give the comparison of the implementation of our proposed algorithm with two most popular cube root algorithms, namely the Adleman-Manders-Miller algorithm and the Cipolla-Lehmer algorithm. To the authors\u27 knowledge, our comparison is the first one which compares three fundamental algorithms together

Multi-donor random terpolymers based on benzodithiophene and dithienosilole segments with different monomer compositions for high-performance polymer solar cells

We synthesized and characterized a series of new random terpolymers which are composed of two electron-rich segments, thienyl-substituted benzo[1,2-b:4,5-b’]dithiophene (BDT) and dithieno[3,2-b:2’,3’-d]silole (DTS), and one electron- deficient segment, thieno[3,4-c]pyrrole-4,6-dione (TPD), with different ratio of DTS segment to BDT segment (25%, 50%, and 75% DTS). The compositional effect of the random terpolymers on physicochemical properties and photovoltaic performances were studied. The different compositions of BDT and DTS segments in the conjugated backbone of the terpolymers had a crucial effect on the electrochemical properties of the random terpolymers. PTPD-BDT75-DTS25 (P1) with monomeric composition of BDT and DTS (75:25) segments showed excellent light harvesting ability, high charge carrier mobility, and low-lying HOMO energy level. Moreover, the photovoltaic performance of the random terpolymer based BHJ PSCs was strongly influenced by the composition of BDT and DTS segments in the conjugated backbones of the terpolymers. The inverted BHJ PSCs based on the PTPD-BDT75- DTS25 exhibited a Voc of 0.94 V, a Jsc of 10.83 mA/cm2, and a FF of 56.52%, leading to a high PCE of 5.78%.[Figure not available: see fulltext.] © 2017 The Polymer Society of Korea and Springer Science+Business Media B.V., part of Springer Nature1

MicroRNA Expression Variation in Female Dog (<i>Canis familiaris</i>) Reproductive Organs with Age and Presence of Uteropathy

While aging is associated with microRNA (miRNA) expression, little is known about its role in the aging of dog reproductive organs. We examined miRNA expression in ovaries, oviducts, and uteri from young and old dogs and dogs with uteropathy to elucidate miRNA’s role in aging. The ovaries, oviducts, and uteri of 18 dogs (Canis familiaris)—young (8.5 ± 1.9 months old), old (78.2 ± 29.0 months old), and those with uteropathy (104.4 ± 15.1 months old)—were collected for miRNA expression examination. Total RNA samples were extracted, reverse-transcribed to cDNA, and real-time PCR analysis was also performed. In ovaries, miR-708 and miR-151 levels were significantly higher in old dogs than in young dogs, and only let-7a, let-7b, let-7c, miR125b, and miR26a were significantly upregulated in dogs with uteropathy. In the oviducts and uteri of old dogs, miR-140, miR-30d, miR-23a, miR-10a, miR-125a, miR-221, and miR-29a were upregulated. Realtime quantitative PCR revealed that targeted mRNA was similarly regulated to miRNA. These results suggest that miRNAs of reproductive organs in dogs may be biological markers for aging and reproductive diseases and could be used for mediating aging

Mitigative Effects of PFF-A Isolated from <i>Ecklonia cava</i> on Pigmentation in a Zebrafish Model and Melanogenesis in B16F10 Cells

Melanin synthesis is a defense mechanism that prevents skin damage, but excessive accumulation of melanin occurs in the skin in various reactions such as pigmentation, lentigines, and freckles. Although anti-melanogenic effects have been demonstrated for various naturally occurring marine products that inhibit and control tyrosinase activity, most studies have not been extended to in vivo applications. Phlorofucofuroeckol-A (PFF-A, 12.5–100 µM) isolated from Ecklonia cava has previously been shown to have tyrosinase-mitigative effects in B16F10 cells, but it has not been evaluated in an in vivo model, and its underlying mechanism for anti-melanogenic effects has not been studied. In the present study, we evaluated the safety and efficacy of PFF-A for anti-melanogenic effects in an in vivo model. We selected low doses of PFF-A (1.5–15 nM) and investigated their mitigative effects on pigmentation stimulated by α-MSH in vivo and their related-mechanism in an in vitro model. The findings suggest that low-dose PFF-A derived from E. cava suppresses pigmentation in vivo and melanogenesis in vitro. Therefore, this study presents the possibility that PFF-A could be utilized as a new anti-melanogenic agent in the cosmeceutical industries

Boosting the Performance of Photomultiplication‐Type Organic Photodiodes by Embedding CsPbBr3 Perovskite Nanocrystals

Abstract In this study, it is demonstrated that CsPbBr3 perovskite nanocrystals (NCs) can enhance the overall performances of photomultiplication‐type organic photodiodes (PM‐OPDs). The proposed approach enables the ionic‐polarizable CsPbBr3 NCs to be evenly distributed throughout the depletion region of Schottky junction interface, allowing the entire trapped electrons within the depletion region to be stabilized, in contrast to previously reported interface‐limited strategies. The optimized CsPbBr3‐NC‐embedded poly(3‐hexylthiophene‐diyl)‐based PM‐OPDs exhibit exceptionally high external quantum efficiency, specific detectivity, and gain–bandwidth product of 2,840,000%, 3.97 × 1015 Jones, and 2.14 × 107 Hz, respectively. 2D grazing‐incidence X–ray diffraction analyses and drift–diffusion simulations combined with temperature‐dependent J–V characteristic analyses are conducted to investigate the physics behind the success of CsPbBr3‐NC‐embedded PM‐OPDs. The results show that the electrostatic interactions generated by the ionic polarization of NCs effectively stabilize the trapped electrons throughout the entire volume of the photoactive layer, thereby successfully increasing the effective energy depth of the trap states and allowing efficient PM mechanisms. This study demonstrates how a hybrid‐photoactive‐layer approach can further enhance PM‐OPD when the functionality of inorganic inclusions meets the requirements of the target device