Detection of Double-Nuclei Galaxies in SDSS

It is now well established that galaxy interactions and mergers play a crucial role in the hierarchical growth of structure in our universe. Galaxy mergers can lead to the formation of elliptical galaxies and larger disk galaxies, as well as drive galaxy evolution through star formation and nuclear activity. During mergers, the nuclei of the individual galaxies come closer and finally form a double nuclei galaxy. Although mergers are common, the detection of double-nuclei galaxies (DNGs) is rare and fairly serendipitous. Their detection is very important as their properties can help us understand the formation of supermassive black hole (SMBH) binaries, dual active galactic nuclei (DAGN), and the associated feedback effects. There is thus a need for an automatic/systematic survey of data for the discovery of double nuclei galaxies. Using the Sloan digital sky survey (SDSS) as the target catalog, we have introduced a novel algorithm "Gothic" (Graph-bOosTed iterated HIll Climbing) that detects whether a given image of a galaxy has characteristic features of a DNG (ASCL entry 2707). We have tested the algorithm on a random sample of 100,000 galaxies from the Stripe 82 region in SDSS and obtained a maximum detection rate of 4.2% with a careful choice of the input catalog

Efficient ML Models for Practical Secure Inference

ML-as-a-service continues to grow, and so does the need for very strong privacy guarantees. Secure inference has emerged as a potential solution, wherein cryptographic primitives allow inference without revealing users' inputs to a model provider or model's weights to a user. For instance, the model provider could be a diagnostics company that has trained a state-of-the-art DenseNet-121 model for interpreting a chest X-ray and the user could be a patient at a hospital. While secure inference is in principle feasible for this setting, there are no existing techniques that make it practical at scale. The CrypTFlow2 framework provides a potential solution with its ability to automatically and correctly translate clear-text inference to secure inference for arbitrary models. However, the resultant secure inference from CrypTFlow2 is impractically expensive: Almost 3TB of communication is required to interpret a single X-ray on DenseNet-121. In this paper, we address this outstanding challenge of inefficiency of secure inference with three contributions. First, we show that the primary bottlenecks in secure inference are large linear layers which can be optimized with the choice of network backbone and the use of operators developed for efficient clear-text inference. This finding and emphasis deviates from many recent works which focus on optimizing non-linear activation layers when performing secure inference of smaller networks. Second, based on analysis of a bottle-necked convolution layer, we design a X-operator which is a more efficient drop-in replacement. Third, we show that the fast Winograd convolution algorithm further improves efficiency of secure inference. In combination, these three optimizations prove to be highly effective for the problem of X-ray interpretation trained on the CheXpert dataset.Comment: 10 pages include references, 4 figure

Secure Floating-Point Training

Secure 2-party computation (2PC) of floating-point arithmetic is improving in performance and recent work runs deep learning algorithms with it, while being as numerically precise as commonly used machine learning (ML) frameworks like PyTorch. We find that the existing 2PC libraries for floating-point support generic computations and lack specialized support for ML training. Hence, their latency and communication costs for compound operations (e.g., dot products) are high. We provide novel specialized 2PC protocols for compound operations and prove their precision using numerical analysis. Our implementation BEACON outperforms state-of-the-art libraries for 2PC of floating-point by over $6\times$

SecFloat: Accurate Floating-Point meets Secure 2-Party Computation

We build a library SecFloat for secure 2-party computation (2PC) of 32-bit single-precision floating-point operations and math functions. The existing functionalities used in cryptographic works are imprecise and the precise functionalities used in standard libraries are not crypto-friendly, i.e., they use operations that are cheap on CPUs but have exorbitant cost in 2PC. SecFloat bridges this gap with its novel crypto-friendly precise functionalities. Compared to the prior cryptographic libraries, SecFloat is up to six orders of magnitude more precise and up to two orders of magnitude more efficient. Furthermore, against a precise 2PC baseline, SecFloat is three orders of magnitude more efficient. The high precision of SecFloat leads to the first accurate implementation of secure inference. All prior works on secure inference of deep neural networks rely on ad hoc float-to-fixed converters. We evaluate a model where the fixed-point approximations used in privacy-preserving machine learning completely fail and floating-point is necessary. Thus, emphasizing the need for libraries like SecFloat

End-to-end Privacy Preserving Training and Inference for Air Pollution Forecasting with Data from Rival Fleets

Privacy-preserving machine learning (PPML) promises to train machine learning (ML) models by combining data spread across multiple data silos. Theoretically, secure multiparty computation (MPC) allows multiple data owners to train models on their joint data without revealing the data to each other. However, the prior implementations of this secure training using MPC have three limitations: they have only been evaluated on CNNs, and LSTMs have been ignored; fixed point approximations have affected training accuracies compared to training in floating point; and due to significant latency overheads of secure training via MPC, its relevance for practical tasks with streaming data remains unclear. The motivation of this work is to report our experience of addressing the practical problem of secure training and inference of models for urban sensing problems, e.g., traffic congestion estimation, or air pollution monitoring in large cities, where data can be contributed by rival fleet companies while balancing the privacy-accuracy trade-offs using MPC-based techniques. Our first contribution is to design a custom ML model for this task that can be efficiently trained with MPC within a desirable latency. In particular, we design a GCN-LSTM and securely train it on time-series sensor data for accurate forecasting, within 7 minutes per epoch. As our second contribution, we build an end-toend system of private training and inference that provably matches the training accuracy of cleartext ML training. This work is the first to securely train a model with LSTM cells. Third, this trained model is kept secret-shared between the fleet companies and allows clients to make sensitive queries to this model while carefully handling potentially invalid queries. Our custom protocols allow clients to query predictions from privately trained models in milliseconds, all the while maintaining accuracy and cryptographic securit

Détection de front d'onde dans l'infrarouge à l'aide de diffuseurs minces

Long wave infrared (LWIR) radiation between 7-14 µm allows passive imaging and is the fingerprint region for spectroscopy. Infrared (IR) imaging has wide-ranging applications in thermography, airborne & atmospheric sensing, fault detection, and non-invasive medical testing. However, existing cameras are only sensitive to the amplitude, but not to the phase of IR radiation. The technique of speckle imaging enables the detection of phase or intensity through complex scattering media - at visible and IR wavelengths. We have developed a novel broadband LWIR speckle imaging configuration for wavefront sensing, utilizing a thin diffusive medium and an uncooled microbolometric camera. Taking advantage of the large angular memory effect of the diffuser, deformations in the speckle image can be ascribed to local variations in the phase of the impinging wavefront. The spatial shifts of the speckle grains in the images are registered using a rapid diffeomorphic image registration algorithm, generating a high-resolution mapping of the local phase gradients. The local phase gradients are then integrated in 2-D to reconstruct the wavefront profile. We have successfully demonstrated thermal wavefront reconstruction using LWIR speckle imaging in infrared optical samples, ranging from optical lenses to fabricated complex phase samples. The experimental setup and technique are characterized, keeping in mind its utility for promising future applications in imaging through visually non-transparent media like semiconductor wafers, engineered nano-electronic surfaces, and infrared optics.Le rayonnement infrarouge à ondes longues (LWIR), entre 7 et 14 µm, permet l'imagerie passive et constitue une gamme spectrale cruciale pour la spectroscopie. L'imagerie infrarouge (IR) a de nombreuses applications dans les domaines de la thermographie, de la détection aérienne et atmosphérique, de la détection des défauts et des tests médicaux non invasifs. Les motifs de tavelures, ou "speckle", permettent de détecter la phase ou l'intensité en utilisant des milieux diffusants complexes - aux longueurs d'onde visibles et infrarouges. Nous avons mis au point une nouvelle configuration d'imagerie du front d'onde, en utilisant un milieu diffusif mince et une caméra microbolométrique non refroidie travaillant dans la gamme LWIR. En exploitant le fort effet mémoire de diffuseurs minces, les déformations locales de la figure de speckle permettent de remonter à une information quantitative sur les gradients locaux de la phase optique. Ceux-ci sont ensuite intégrés en deux dimensions pour reconstruire le profil du front d'onde. Nous avons démontré avec succès la reconstruction d'un front d'onde LWIR dans des échantillons optiques infrarouges, allant de lentilles optiques et jusqu'à des échantillons de phase complexes. Le dispositif expérimental et la technique sont caractérisés, en gardant à l'esprit leur utilité pour des applications futures prometteuses dans l'imagerie à travers des milieux visuellement non transparents tels que les semi-conducteurs, les surfaces nano-électroniques et les optiques pour l'infrarouge

Automated Detection of Double Nuclei Galaxies using GOTHIC and the Discovery of a Large Sample of Dual AGN

We present a novel algorithm to detect double nuclei galaxies (DNG) called GOTHIC (Graph BOosted iterated HIll Climbing) - that detects whether a given image of a galaxy has two or more closely separated nuclei. Our aim is to detect samples of dual or multiple active galactic nuclei (AGN) in galaxies. Although galaxy mergers are common, the detection of dual AGN is rare. Their detection is very important as they help us understand the formation of supermassive black hole (SMBH) binaries, SMBH growth and AGN feedback effects in multiple nuclei systems. There is thus a need for an algorithm to do a systematic survey of existing imaging data for the discovery of DNGs and dual AGN. We have tested GOTHIC on a known sample of DNGs and subsequently applied it to a sample of a million SDSS DR16 galaxies lying in the redshift range of 0 to 0.75 approximately, and have available spectroscopic data. We have detected 159 dual AGN in this sample, of which 2 are triple AGN systems. Our results show that dual AGN are not common, and triple AGN even rarer. The color (u-r) magnitude plots of the DNGs indicate that star formation is quenched as the nuclei come closer and as the AGN fraction increases. The quenching is especially prominent for dual/triple AGN galaxies that lie in the extreme end of the red sequence

Long Wave Infrared Wavefront Reconstruction Through Complex Media

International audienceA novel broadband infrared (IR) speckle imaging system with a thin scatterer and an uncooled microbolometric camera is employed to encode wavefront phase variations as local speckle deformations. The phase reconstruction from speckle shifts using a fast diffeomorphic algorithm ultimately demonstrates IR wavefront reconstruction through complex media