Miracle-dz/UniFAR-FTUs huggingface dataset:
Serving and Optimizing Machine Learning Workflows on Heterogeneous Infrastructures

With the advent of ubiquitous deployment of smart devices and the Internet of Things, data sources for machine learning inference have increasingly moved to the edge of the network. Existing machine learning inference platforms typically assume a homogeneous infrastructure and do not take into account the more complex and tiered computing infrastructure that includes edge devices, local hubs, edge datacenters, and cloud datacenters. On the other hand, recent AutoML efforts have provided viable solutions for model compression, pruning and quantization for heterogeneous environments; for a machine learning model, now we may easily find or even generate a series of model variants with different tradeoffs between accuracy and efficiency. We design and implement JellyBean, a system for serving and optimizing machine learning inference workflows on heterogeneous infrastructures. Given service-level objectives (e.g., throughput, accuracy), JellyBean picks the most cost-efficient models that meet the accuracy target and decides how to deploy them across different tiers of infrastructures. Evaluations show that JellyBean reduces the total serving cost of visual question answering by up to 58% and vehicle tracking from the NVIDIA AI City Challenge by up to 36%, compared with state-of-the-art model selection and worker assignment solutions. JellyBean also outperforms prior ML serving systems (e.g., Spark on the cloud) up to 5x in serving costs.
Neural Optimal Stopping Boundary

A method based on deep artificial neural networks and empirical risk minimization is developed to calculate the boundary separating the stopping and continuation regions in optimal stopping. The algorithm parameterizes the stopping boundary as the graph of a function and introduces relaxed stopping rules based on fuzzy boundaries to facilitate efficient optimization. Several financial instruments, some in high dimensions, are analyzed through this method, demonstrating its effectiveness. The existence of the stopping boundary is also proved under natural structural assumptions.
SmartSAGE: training large-scale graph neural networks using in-storage processing architectures

Graph neural networks (GNNs) can extract features by learning both the representation of each objects (i.e., graph nodes) and the relationship across different objects (i.e., the edges that connect nodes), achieving state-of-the-art performance in various graph-based tasks. Despite its strengths, utilizing these algorithms in a production environment faces several challenges as the number of graph nodes and edges amount to several billions to hundreds of billions scale, requiring substantial storage space for training. Unfortunately, state-of-the-art ML frameworks employ an in-memory processing model which significantly hampers the productivity of ML practitioners as it mandates the overall working set to fit within DRAM capacity. In this work, we first conduct a detailed characterization on a state-of-the-art, large-scale GNN training algorithm, GraphSAGE. Based on the characterization, we then explore the feasibility of utilizing capacity-optimized NVMe SSDs for storing memory-hungry GNN data, which enables large-scale GNN training beyond the limits of main memory size. Given the large performance gap between DRAM and SSD, however, blindly utilizing SSDs as a direct substitute for DRAM leads to significant performance loss. We therefore develop SmartSAGE, our software/hardware co-design based on an in-storage processing (ISP) architecture. Our work demonstrates that an ISP based large-scale GNN training system can achieve both high capacity storage and high performance, opening up opportunities for ML practitioners to train large GNN datasets without being hampered by the physical limitations of main memory size.
Multiview Stereo with Cascaded Epipolar RAFT

We address multiview stereo (MVS), an important 3D vision task that reconstructs a 3D model such as a dense point cloud from multiple calibrated images. We propose CER-MVS (Cascaded Epipolar RAFT Multiview Stereo), a new approach based on the RAFT (Recurrent All-Pairs Field Transforms) architecture developed for optical flow. CER-MVS introduces five new changes to RAFT: epipolar cost volumes, cost volume cascading, multiview fusion of cost volumes, dynamic supervision, and multiresolution fusion of depth maps. CER-MVS is significantly different from prior work in multiview stereo. Unlike prior work, which operates by updating a 3D cost volume, CER-MVS operates by updating a disparity field. Furthermore, we propose an adaptive thresholding method to balance the completeness and accuracy of the reconstructed point clouds. Experiments show that our approach achieves competitive performance on DTU (the second best among known results) and state-of-the-art performance on the Tanks-and-Temples benchmark (both the intermediate and advanced set). Code is available at https://github.com/princeton-vl/CER-MVS
Deep learning based Chinese text sentiment mining and stock market correlation research

: We explore how to crawl financial forum data such as stock bars and combine them with deep learning models for sentiment analysis. In this paper, we will use the BERT model to train against the financial corpus and predict the SZSE Component Index, and find that applying the BERT model to the financial corpus through the maximum information coefficient comparison study. The obtained sentiment features will be able to reflect the fluctuations in the stock market and help to improve the prediction accuracy effectively. Meanwhile, this paper combines deep learning with financial text, in further exploring the mechanism of investor sentiment on stock market through deep learning method, which will be beneficial for national regulators and policy departments to develop more reasonable policy guidelines for maintaining the stability of stock market.
Real-time Forecasting of Time Series in Financial Markets Using Sequentially Trained Many-to-one LSTMs

Financial markets are highly complex and volatile; thus, learning about such markets for the sake of making predictions is vital to make early alerts about crashes and subsequent recoveries. People have been using learning tools from diverse fields such as financial mathematics and machine learning in the attempt of making trustworthy predictions on such markets. However, the accuracy of such techniques had not been adequate until artificial neural network (ANN) frameworks were developed. Moreover, making accurate real-time predictions of financial time series is highly subjective to the ANN architecture in use and the procedure of training it. Long short-term memory (LSTM) is a member of the recurrent neural network family which has been widely utilized for time series predictions. Especially, we train two LSTMs with a known length, say $T$ time steps, of previous data and predict only one time step ahead. At each iteration, while one LSTM is employed to find the best number of epochs, the second LSTM is trained only for the best number of epochs to make predictions. We treat the current prediction as in the training set for the next prediction and train the same LSTM. While classic ways of training result in more error when the predictions are made further away in the test period, our approach is capable of maintaining a superior accuracy as training increases when it proceeds through the testing period. The forecasting accuracy of our approach is validated using three time series from each of the three diverse financial markets: stock, cryptocurrency, and commodity. The results are compared with those of an extended Kalman filter, an autoregressive model, and an autoregressive integrated moving average model.
Instantons, renormalons and the theta angle in integrable sigma models

Some sigma models which admit a theta angle are integrable at both \vartheta=0ϑ=0 and \vartheta=\piϑ=π. This includes the well-known O(3)O(3) sigma model and two families of coset sigma models studied by Fendley. We consider the ground state energy of these models in the presence of a magnetic field, which can be computed with the Bethe Ansatz. We obtain explicit results for its non-perturbative corrections and we study the effect of the theta angle on them. We show that imaginary, exponentially small corrections due to renormalons remain unchanged, while instanton corrections change sign, as expected. We find in addition corrections due to renormalons which also change sign as we turn on the theta angle. Based on these results we present an explicit non-perturbative formula for the topological susceptibility of the O(3)O(3) sigma model in the presence of a magnetic field, in the weak coupling limit.
Robust Learning of Parsimonious Deep Neural Networks

We propose a simultaneous learning and pruning algorithm capable of identifying and eliminating irrelevant structures in a neural network during the early stages of training. Thus, the computational cost of subsequent training iterations, besides that of inference, is considerably reduced. Our method, based on variational inference principles using Gaussian scale mixture priors on neural network weights, learns the variational posterior distribution of Bernoulli random variables multiplying the units/filters similarly to adaptive dropout. Our algorithm, ensures that the Bernoulli parameters practically converge to either 0 or 1, establishing a deterministic final network. We analytically derive a novel hyper-prior distribution over the prior parameters that is crucial for their optimal selection and leads to consistent pruning levels and prediction accuracy regardless of weight initialization or the size of the starting network. We prove the convergence properties of our algorithm establishing theoretical and practical pruning conditions. We evaluate the proposed algorithm on the MNIST and CIFAR-10 data sets and the commonly used fully connected and convolutional LeNet and VGG16 architectures. The simulations show that our method achieves pruning levels on par with state-of the-art methods for structured pruning, while maintaining better test-accuracy and more importantly in a manner robust with respect to network initialization and initial size.
Point-splitting regularization of the stress tensor of a coupling scalar field in de Sitter space

We perform the point-splitting regularization on the vacuum stress tensor of a coupling scalar field in de Sitter space under the guidance from the adiabatically regularized Green's function. For the massive scalar field with the minimal coupling ξ = 0, the 2nd order point-splitting regularization yields a finite vacuum stress tensor with a positive, constant energy density, which can be identified as the cosmological constant that drives de Sitter inflation. For the coupling ξ ≠ 0, we find that, even if the regularized Green's function is continuous, UV and IR convergent, the point-splitting regularization does not automatically lead to an appropriate stress tensor. The coupling ξ R causes log divergent terms, as well as higher-order finite terms which depend upon the path of the coincidence limit. After removing these unwanted terms by extra treatments, the 2nd-order regularization for small couplings ξ ∈ (0,1/7.04), and respectively the 0th-order regularization for the conformal coupling ξ=1/6, yield a finite, constant vacuum stress tensor, in analogy to the case ξ=0. For the massless field with ξ = 0 or ξ = 1/6, the point-splitting regularization yields a vanishing vacuum stress tensor, and there is no conformal trace anomaly for ξ = 1/6. If the 4th-order regularization were taken, the regularized energy density for general ξ would be negative, which is inconsistent with the de Sitter inflation, and the regularized Green's function would be singular at the zero mass, which is unphysical. In all these cases, the stress tensor from the point-splitting regularization is equal to that from the adiabatic one. We also discuss the issue of the adequate order of regularization.
Privadome: Protecting Citizen Privacy from Delivery Drones

As e-commerce companies begin to consider using delivery drones for customer fulﬁllment, there are growing concerns around citizen privacy. Drones are equipped with cameras, and the video feed from these cameras is often required as part of routine navigation, be it for semi-autonomous or fully-autonomous drones. Footage of ground-based citizens may be captured in this video feed, thereby leading to privacy concerns. This paper presents Privadome, a system that implements the vision of a virtual privacy dome centered around the citizen. Privadome is designed to be integrated with city-scale regulatory authorities that oversee delivery drone operations and realizes this vision through two components, PD-MPC and PD-ROS . PD-MPC allows citizens equipped with a mobile device to identify drones that have captured their footage. It uses secure two-party computation to achieve this goal without compromising the privacy of the citizen’s location. PD-ROS allows the citizen to communicate with such drones and obtain an audit trail showing how the drone uses their footage and determine if privacy-preserving steps are taken to sanitize the footage. An experimental evaluation of Privadome using our prototype implementations of PD-MPC and PD-ROS shows that the system scales to near-term city-scale delivery drone deployments (hundreds of drones). We show that with PD-MPC the mobile data usage on the citizen’s mobile device is comparable to that of routine activities on the device, such as streaming videos. We also show that the workﬂow of PD-ROS consumes a modest amount of additional CPU resources and power on our experimental platform. 1 Delivery drones register with a city-scale regulatory authority that oversees their operation. Each drone has a unique identiﬁer and keeps the regulatory authority updated with its location. 2 Privacy-conscious citizen queries the regulatory authority with their current location (using PD-MPC ), and obtains a shortlist of drones whose cameras may have the citizen in their ﬁeld of view. 3 Citizen communicates with the drone, either directly or via the regulatory authority (without revealing their identity). Well-intentioned drones running PD-ROS can provide an audit trail to citizens showing that their privacy is maintained in the footage. We use the drone’s current GPS location (Γ t , λ t ) and location after a short time interval (Γ t + δ , λ t + δ ) to determine its direction of motion. The drone camera’s ﬁeld of view is abstracted by the parameter θ . PD-MPC ’s geometric computation determines whether the citizen’s location (Γ c , λ c ) is within the drone’s ﬁeld of view, i.e., whether φ < θ . For the ﬁeld of view computation, PD-MPC converts location information captured as GPS coordinates into an equirectangular projection with (Γ t , λ t ) as origin. 3 An agent the manifests of applications launched for execution; 4 An audit logger that stores SROS manifests, and digitally signs and sends them in response to citizen queries.
Training personalized recommendation systems from (GPU) scratch: look forward not backwards

Personalized recommendation models (RecSys) are one of the most popular machine learning workload serviced by hyperscalers. A critical challenge of training RecSys is its high memory capacity requirements, reaching hundreds of GBs to TBs of model size. In RecSys, the so-called embedding layers account for the majority of memory usage so current systems employ a hybrid CPU-GPU design to have the large CPU memory store the memory hungry embedding layers. Unfortunately, training embeddings involve several memory bandwidth intensive operations which is at odds with the slow CPU memory, causing performance overheads. Prior work proposed to cache frequently accessed embeddings inside GPU memory as means to filter down the embedding layer traffic to CPU memory, but this paper observes several limitations with such cache design. In this work, we present a fundamentally different approach in designing embedding caches for RecSys. Our proposed ScratchPipe architecture utilizes unique properties of RecSys training to develop an embedding cache that not only sees the past but also the "future" cache accesses. ScratchPipe exploits such property to guarantee that the active working set of embedding layers can "always" be captured inside our proposed cache design, enabling embedding layer training to be conducted at GPU memory speed.
Corrugated Waveguides With Stepped Ridges and Stratified Core for High-Power Transmission: Modal Analysis by Radial-Cavity and Concentric Multilayer Green’s Functions

This paper presents a full-wave modal analysis of transverse corrugated circular waveguides with stepped ridges accompanied by segmented grooves and a waveguiding core that is stratified into concentric layers. The method is validated with the known results from literature and those generated by independent solvers. Its strengths over contemporary numerical alternatives such as the finite-element method (FEM) are also portrayed. Such a topology facilitates the study of ways for reducing metal losses and enhancing the aperture efficiency of the operational mode, especially in high-power high-frequency microwave applications, such as by implementing dielectric linings within the grooves or ceramic coatings over the surface of the corrugations, constituting just two cited examples amongst the many other forms of measures for potential performance enhancement which the technique is able to treat.
Characterization of Kepler targets based on medium-resolution LAMOST spectra analyzed with ROTFIT

In this work we present the results of our analysis of 16,300 medium-resolution LAMOST spectra of late-type stars in the Kepler field with the aim of determining the stellar parameters, activity level, lithium atmospheric content, and binarity. We have used a version of the code ROTFIT specifically developed for these spectra. We provide a catalog with the atmospheric parameters (Teff, log(g), and [Fe/H]), radial velocity (RV), and projected rotation velocity (vsini). For cool stars (Teff<6500 K), we also calculated the H-alpha and LiI-6708 equivalent width, which are important indicators of chromospheric activity and evolutionary stage, respectively. We have derived the RV and atmospheric parameters for 14,300 spectra of 7443 stars. Literature data were used for a quality control of the results. The Teff and log(g) values are in good agreement with the literature. The [Fe/H] values appear to be overestimated for metal-poor stars. We propose a relation to correct the [Fe/H] values derived with ROTFIT. We were able to identify double-lined binaries, stars with variable RVs, lithium-rich giants, and emission-line objects. Based on the H-alpha flux, we found 327 active stars. We detected the LiI-6708 line and measure its equivalent width for 1657 stars, both giants and stars on the main sequence. Regarding the latter, we performed a discrete age classification based on the atmospheric lithium abundance and the upper envelopes of a few open clusters. Among the giants, we found 195 Li-rich stars, 161 of which are reported here for the first time. No relationship is found between stellar rotation and lithium abundance, which allows us to rule out merger scenarios as the predominant explanation of the enrichment of Li in our sample. The fraction of Li-rich giants, about 4%, is higher than expected.
Fingerprinting of DNN with Black-box Design and Veriﬁcation

—Most of the existing veriﬁcation techniques for model integrity belong to private veriﬁability, which only provides a veriﬁcation service to honest parties. In contrast, public veriﬁability is intended for a broader range of users (i.e., ﬁngerprinting patterns are available to all parties, including adversaries), and accounts for the possibility of untrusted service providers ( e.g. , cloud platforms) featuring in the Cloud and MLaaS services. None of the current veriﬁcation approaches are feasible for public veriﬁability due to the inability to meet the new properties such as lightweight, smooth appearance and anti-counterfeiting. In this paper, we propose PublicCheck, a practical watermarking technique for ensuring the integrity of cloud-enabled services via public veriﬁcation. The intuition behind PublicCheck is that a model’s decision boundary could serve as an inherent ﬁngerprint for watermarking. The challenge is how to capture the decision boundary with a limited number of available samples. PublicCheck achieves this by generating encysted sample ﬁngerprints, a set of smoothly transformed and augmented inputs, enclosed around the model’s decision boundary to capture the inherent model ﬁngerprints. PublicCheck assumes that the watermarking sample design should accommodate cases where the model’s knowledge is unavailable or limited for the ﬁngerprinting design, in lieu of the gradient/parameter knowledge required by the state-of-the-art white-box watermarking approaches, as well as black-box veriﬁcation through the public API. PublicCheck is implemented and extensively evaluated against various model integrity attacks and model compression attacks. We show that PublicCheck can detect model integrity breaches with 100% accuracy within ﬁve black-box model accesses. Our evaluation of the appearance of encysted sample (augmented data points permeating the decision boundary) reveals that our structural perturbation results in a smooth appearance in the pixel space. We also demonstrate the feasibility and efﬁciency of generating a plethora of encysted samples for ﬁngerprinting.
Hey AI, Can You Solve Complex Tasks by Talking to Agents?

Training giant models from scratch for each complex task is resource- and data-inefficient. To help develop models that can leverage existing systems, we propose a new challenge: Learning to solve complex tasks by communicating with existing agents (or models) in natural language. We design a synthetic benchmark, CommaQA, with three complex reasoning tasks (explicit, implicit, numeric) designed to be solved by communicating with existing QA agents. For instance, using text and table QA agents to answer questions such as “Who had the longest javelin throw from USA? ”. We show that black-box models struggle to learn this task from scratch (accuracy under 50%) even with access to each agent’s knowledge and gold facts supervision. In contrast, models that learn to communicate with agents outperform black-box models, reaching scores of 100% when given gold decomposition supervision. However, we show that the challenge of learning to solve complex tasks by communicating with existing agents without relying on any auxiliary supervision or data still remains highly elusive. We will release CommaQA, along with a compositional generalization test split, to advance research in this direction.