OpenRaiser/Intern-Atlas huggingface dataset:
Discriminative Feature Learning Framework with Gradient Preference for Anomaly Detection
Venue: arXiv cs.LG 2022
Year: 2022

Unsupervised representation learning has been extensively employed in anomaly detection, achieving impressive performance. Extracting valuable feature vectors that can remarkably improve the performance of anomaly detection are essential in unsupervised representation learning. To this end, we propose a novel discriminative feature learning framework with gradient preference for anomaly detection. Specifically, we firstly design a gradient preference based selector to store powerful feature points in space and then construct a feature repository, which alleviate the interference of redundant feature vectors and improve inference efficiency. To overcome the looseness of feature vectors, secondly, we present a discriminative feature learning with center constrain to map the feature repository to a compact subspace, so that the anomalous samples are more distinguishable from the normal ones. Moreover, our method can be easily extended to anomaly localization. Extensive experiments on popular industrial and medical anomaly detection datasets demonstrate our proposed framework can achieve competitive results in both anomaly detection and localization. More important, our method outperforms the state-of-the-art in few shot anomaly detection.
Indoor simultaneous localization and mapping based on fringe projection profilometry
Venue: arXiv cs.RO 2022
Year: 2022

Simultaneous Localization and Mapping (SLAM) plays an important role in outdoor and indoor applications ranging from autonomous driving to indoor robotics. Outdoor SLAM has been widely used with the assistance of LiDAR or GPS. For indoor applications, the LiDAR technique does not satisfy the accuracy requirement and the GPS signals will be lost. An accurate and efficient scene sensing technique is required for indoor SLAM. As the most promising 3D sensing technique, the opportunities for indoor SLAM with fringe projection profilometry (FPP) systems are obvious, but methods to date have not fully leveraged the accuracy and speed of sensing that such systems offer. In this paper, we propose a novel FPP-based indoor SLAM method based on the coordinate transformation relationship of FPP, where the 2D-to-3D descriptor-assisted is used for mapping and localization. The correspondences generated by matching descriptors are used for fast and accurate mapping, and the transform estimation between the 2D and 3D descriptors is used to localize the sensor. The provided experimental results demonstrate that the proposed indoor SLAM can achieve the localization and mapping accuracy around one millimeter.
GAMORRA: An API-Level Workload Model for Rasterization-based Graphics Pipeline Architecture
Venue: arXiv cs.GR 2022
Year: 2022

The performance of applications that require frame rendering time estimation or dynamic frequency scaling, rely on the accuracy of the workload model that is utilized within these applications. Existing models lack sufficient accuracy in their core model. Hence, they require changes to the target application or the hardware to produce accurate results. This paper introduces a mathematical workload model for a rasterization-based graphics Application Programming Interface (API) pipeline, named GAMORRA, which works based on the load and complexity of each stage of the pipeline. Firstly, GAMORRA models each stage of the pipeline based on their operation complexity and the input data size. Then, the calculated workloads of the stages are fed to a Multiple Linear Regression (MLR) model as explanatory variables. A hybrid offline/online training scheme is proposed as well to train the model. A suite of benchmarks is also designed to tune the model parameters based on the performance of the target system. The experiments were performed on Direct3D 11 and on two different rendering platforms comparing GAMORRA to an AutoRegressive (AR) model, a Frame Complexity Model (FCM) and a frequency-based (FRQ) model. The experiments show an average of 1.27 ms frame rendering time estimation error (9.45%) compared to an average of 1.87 ms error (13.23%) for FCM which is the best method among the three chosen methods. However, this comes at the cost of 0.54 ms (4.58%) increase in time complexity compared to FCM. Furthermore, GAMMORA improves frametime underestimations by 1.1% compared to FCM.
Approximation Algorithms for Interdiction Problem with Packing Constraints
Venue: arXiv cs.DS 2022
Year: 2022

We study a bilevel optimization problem which is a zero-sum Stackelberg game. In this problem, there are two players, a leader and a follower, who pick items from a common set. Both the leader and the follower have their own (multi-dimensional) budgets, respectively. Each item is associated with a profit, which is the same to the leader and the follower, and will consume the leader's (follower's) budget if it is selected by the leader (follower). The leader and the follower will select items in a sequential way: First, the leader selects items within the leader's budget. Then the follower selects items from the remaining items within the follower's budget. The goal of the leader is to minimize the maximum profit that the follower can obtain. Let $s_A$ and $s_B$ be the dimension of the leader's and follower's budget, respectively. A special case of our problem is the bilevel knapsack problem studied by Caprara et al. [SIAM Journal on Optimization, 2014], where $s_A=s_B=1$. We consider the general problem and obtain an $(s_B+ε)$-approximation algorithm when $s_A$ and $s_B$ are both constant. In particular, if $s_B=1$, our algorithm implies a PTAS for the bilevel knapsack problem, which is the first O(1)-approximation algorithm. We also complement our result by showing that there does not exist any $(4/3-ε)$-approximation algorithm even if $s_A=1$ and $s_B=2$. We also consider a variant of our problem with resource augmentation when $s_A$ and $s_B$ are both part of the input. We obtain an O(1)-approximation algorithm with O(1)-resource augmentation, that is, we give an algorithm that returns a solution which exceeds the given leader's budget by O(1) times, and the objective value achieved by the solution is O(1) times the optimal objective value that respects the leader's budget.
Maximizing Non-Monotone Submodular Functions over Small Subsets: Beyond $1/2$-Approximation
Venue: arXiv cs.DS 2022
Year: 2022

In this work we give two new algorithms that use similar techniques for (non-monotone) submodular function maximization subject to a cardinality constraint. The first is an offline fixed parameter tractable algorithm that guarantees a $0.539$-approximation for all non-negative submodular functions. The second algorithm works in the random-order streaming model. It guarantees a $(1/2+c)$-approximation for symmetric functions, and we complement it by showing that no space-efficient algorithm can beat $1/2$ for asymmetric functions. To the best of our knowledge this is the first provable separation between symmetric and asymmetric submodular function maximization.
Realistic Evaluation of Transductive Few-Shot Learning
Venue: arXiv cs.LG 2022
Year: 2022

Transductive inference is widely used in few-shot learning, as it leverages the statistics of the unlabeled query set of a few-shot task, typically yielding substantially better performances than its inductive counterpart. The current few-shot benchmarks use perfectly class-balanced tasks at inference. We argue that such an artificial regularity is unrealistic, as it assumes that the marginal label probability of the testing samples is known and fixed to the uniform distribution. In fact, in realistic scenarios, the unlabeled query sets come with arbitrary and unknown label marginals. We introduce and study the effect of arbitrary class distributions within the query sets of few-shot tasks at inference, removing the class-balance artefact. Specifically, we model the marginal probabilities of the classes as Dirichlet-distributed random variables, which yields a principled and realistic sampling within the simplex. This leverages the current few-shot benchmarks, building testing tasks with arbitrary class distributions. We evaluate experimentally state-of-the-art transductive methods over 3 widely used data sets, and observe, surprisingly, substantial performance drops, even below inductive methods in some cases. Furthermore, we propose a generalization of the mutual-information loss, based on $α$-divergences, which can handle effectively class-distribution variations. Empirically, we show that our transductive $α$-divergence optimization outperforms state-of-the-art methods across several data sets, models and few-shot settings. Our code is publicly available at https://github.com/oveilleux/Realistic_Transductive_Few_Shot.
RedMulE: A Compact FP16 Matrix-Multiplication Accelerator for Adaptive Deep Learning on RISC-V-Based Ultra-Low-Power SoCs
Venue: arXiv cs.AR 2022
Year: 2022

The fast proliferation of extreme-edge applications using Deep Learning (DL) based algorithms required dedicated hardware to satisfy extreme-edge applications' latency, throughput, and precision requirements. While inference is achievable in practical cases, online finetuning and adaptation of general DL models are still highly challenging. One of the key stumbling stones is the need for parallel floating-point operations, which are considered unaffordable on sub-100 mW extreme-edge SoCs. We tackle this problem with RedMulE (Reduced-precision matrix Multiplication Engine), a parametric low-power hardware accelerator for FP16 matrix multiplications - the main kernel of DL training and inference - conceived for tight integration within a cluster of tiny RISC-V cores based on the PULP (Parallel Ultra-Low-Power) architecture. In 22 nm technology, a 32-FMA RedMulE instance occupies just 0.07 mm^2 (14% of an 8-core RISC-V cluster) and achieves up to 666 MHz maximum operating frequency, for a throughput of 31.6 MAC/cycle (98.8% utilization). We reach a cluster-level power consumption of 43.5 mW and a full-cluster energy efficiency of 688 16-bit GFLOPS/W. Overall, RedMulE features up to 4.65x higher energy efficiency and 22x speedup over SW execution on 8 RISC-V cores.
Hardware Acceleration for Third-Generation FHE and PSI Based on It
Venue: arXiv cs.CR 2022
Year: 2022

With the expansion of cloud services, serious concerns about the privacy of users' data arise due to the exposure of the unencrypted data to the server during computation. Various security primitives are under investigation to preserve privacy while evaluating private data, including Fully Homomorphic Encryption (FHE), Private Set Intersection (PSI), and others. However, the prohibitive processing time of these primitives hinders their practical applications. This work proposes and implements an architecture for accelerating third-generation FHE with Amazon Web Services (AWS) cloud FPGAs, marking the first hardware acceleration solution for third-generation FHE. We also introduce a novel unbalanced PSI protocol based on third-generation FHE, optimized for the proposed hardware architecture. Several algorithm-architecture co-optimization techniques are introduced to allow the communication and computation costs to be independent of the Sender's set size. The measurement results show that the proposed accelerator achieves $>21\times$ performance improvement compared to a software implementation for various crucial subroutines of third-generation FHE and the proposed PSI.
Farmer's Assistant: A Machine Learning Based Application for Agricultural Solutions
Venue: arXiv cs.LG 2022
Year: 2022

Farmers face several challenges when growing crops like uncertain irrigation, poor soil quality, etc. Especially in India, a major fraction of farmers do not have the knowledge to select appropriate crops and fertilizers. Moreover, crop failure due to disease causes a significant loss to the farmers, as well as the consumers. While there have been recent developments in the automated detection of these diseases using Machine Learning techniques, the utilization of Deep Learning has not been fully explored. Additionally, such models are not easy to use because of the high-quality data used in their training, lack of computational power, and poor generalizability of the models. To this end, we create an open-source easy-to-use web application to address some of these issues which may help improve crop production. In particular, we support crop recommendation, fertilizer recommendation, plant disease prediction, and an interactive news-feed. In addition, we also use interpretability techniques in an attempt to explain the prediction made by our disease detection model.
Model Repair via Symmetry
Venue: arXiv cs.LO 2022
Year: 2022

The symmetry of a Kripke structure $\mathcal{M}$ has been exploited to replace a model check of $\mathcal{M}$ by a model check of the potentially smaller structure $\mathcal{N}$ obtained as the quotient of $\mathcal{M}$ by its symmetry group $G$. We extend previous work to model repair: identify a substructure that satisfies a given temporal logic formula. We show that the substructures of $\mathcal{M}$ that are preserved by $G$ form a lattice that maps to the substructure lattice of $\mathcal{N}$. We also show the existence of a monotone Galois connection between the lattice of substructures of $\mathcal{N}$ and the lattice of substructures of $\mathcal{M}$ that are "maximal" w.r.t. an appropriately defined group action of $G$ on $\mathcal{M}$. These results enable us to repair $\mathcal{N}$ and then to lift the repair to $\mathcal{M}$. We can thus repair symmetric finite-state concurrent programs by repairing the corresponding $\mathcal{N}$, thereby effecting program repair while avoiding state-explosion.
Back-ends Selection for Deep Speaker Embeddings
Venue: arXiv cs.SD 2022
Year: 2022

Probabilistic Linear Discriminant Analysis (PLDA) was the dominant and necessary back-end for early speaker recognition approaches, like i-vector and x-vector. However, with the development of neural networks and margin-based loss functions, we can obtain deep speaker embeddings (DSEs), which have advantages of increased inter-class separation and smaller intra-class distances. In this case, PLDA seems unnecessary or even counterproductive for the discriminative embeddings, and cosine similarity scoring (Cos) achieves better performance than PLDA in some situations. Motivated by this, in this paper, we systematically explore how to select back-ends (Cos or PLDA) for deep speaker embeddings to achieve better performance in different situations. By analyzing PLDA and the properties of DSEs extracted from models with different numbers of segment-level layers, we make the conjecture that Cos is better in same-domain situations and PLDA is better in cross-domain situations. We conduct experiments on VoxCeleb and NIST SRE datasets in four application situations, single-/multi-domain training and same-/cross-domain test, to validate our conjecture and briefly explain why back-ends adaption algorithms work.
Single Object Tracking Research: A Survey
Venue: arXiv cs.CV 2022
Year: 2022

Visual object tracking is an important task in computer vision, which has many real-world applications, e.g., video surveillance, visual navigation. Visual object tracking also has many challenges, e.g., object occlusion and deformation. To solve above problems and track the target accurately and efficiently, many tracking algorithms have emerged in recent years. This paper presents the rationale and representative works of two most popular tracking frameworks in past ten years, i.e., the corelation filter and Siamese network for object tracking. Then we present some deep learning based tracking methods categorized by different network structures. We also introduce some classical strategies for handling the challenges in tracking problem. Further, this paper detailedly present and compare the benchmarks and challenges for tracking, from which we summarize the development history and development trend of visual tracking. Focusing on the future development of object tracking, which we think would be applied in real-world scenes before some problems to be addressed, such as the problems in long-term tracking, low-power high-speed tracking and attack-robust tracking. In the future, the integration of multimodal data, e.g., the depth image, thermal image with traditional color image, will provide more solutions for visual tracking. Moreover, tracking task will go together with some other tasks, e.g., video object detection and segmentation.
Long-run User Value Optimization in Recommender Systems through Content Creation Modeling
Venue: arXiv cs.IR 2022
Year: 2022

Content recommender systems are generally adept at maximizing immediate user satisfaction but to optimize for the \textit{long-run} user value, we need more statistically sophisticated solutions than off-the-shelf simple recommender algorithms. In this paper we lay out such a solution to optimize \textit{long-run} user value through discounted utility maximization and a machine learning method we have developed for estimating it. Our method estimates which content producers are most likely to create the highest long-run user value if their content is shown more to users who enjoy it in the present. We do this estimation with the help of an A/B test and heterogeneous effects machine learning model. We have used such models in Facebook's feed ranking system, and such a model can be used in other recommender systems.
A Review of Research on Civic Technology: Definitions, Theories, History and Insights
Venue: arXiv cs.CY 2022
Year: 2022

There have been initiatives that take advantage of information and communication technologies to serve civic purposes, referred to as civic technologies (Civic Tech). In this paper, we present a review of 224 papers from the ACM Digital Library focusing on Computer Supported Cooperative Work and Human-Computer Interaction, the key fields supporting the building of Civic Tech. Through this review, we discuss the concepts, theories and history of civic tech research and provide insights on the technological tools, social processes and participation mechanisms involved. Our work seeks to direct future civic tech efforts to the phase of by the citizens.
Strongly Aperiodic SFTs on Generalized Baumslag-Solitar groups
Venue: arXiv cs.DM 2022
Year: 2022

We look at constructions of aperiodic SFTs on fundamental groups of graph of groups. In particular we prove that all generalized Baumslag-Solitar groups (GBS) admit a strongly aperiodic SFT. Our proof is based on a structural theorem by Whyte and on two constructions of strongly aperiodic SFTs on $\mathbb{F}_n\times \mathbb{Z}$ and $BS(m,n)$ of our own. Our two constructions rely on a path-folding technique that lifts an SFT on $\mathbb{Z}^2$ inside an SFT on $\mathbb{F}_n\times \mathbb{Z}$ or an SFT on the hyperbolic plane inside an SFT on $BS(m,n)$. In the case of $\mathbb{F}_n\times \mathbb{Z}$ the path folding technique also preserves minimality, so that we get minimal strongly aperiodic SFTs on unimodular GBS groups.
An overview of the Eight International Olympiad in Cryptography "Non-Stop University CRYPTO"
Venue: arXiv cs.CR 2022
Year: 2022

Non-Stop University CRYPTO is the International Olympiad in Cryptography that was held for the eight time in 2021. Hundreds of university and school students, professionals from 33 countries worked on mathematical problems in cryptography during a week. The aim of the Olympiad is to attract attention to curious and even open scientific problems of modern cryptography. In this paper, problems and their solutions of the Olympiad'2021 are presented. We consider 19 problems of varying difficulty and topics: ciphers, online machines, passwords, binary strings, permutations, quantum circuits, historical ciphers, elliptic curves, masking, implementation on a chip, etc. We discuss several open problems on quantum error correction, finding special permutations and s-Boolean sharing of a function, obtaining new bounds on the distance to affine vectorial functions.
Combining Visual Saliency Methods and Sparse Keypoint Annotations to Providently Detect Vehicles at Night
Venue: arXiv cs.CV 2022
Year: 2022

Provident detection of other road users at night has the potential for increasing road safety. For this purpose, humans intuitively use visual cues, such as light cones and light reflections emitted by other road users to be able to react to oncoming traffic at an early stage. This behavior can be imitated by computer vision methods by predicting the appearance of vehicles based on emitted light reflections caused by the vehicle's headlights. Since current object detection algorithms are mainly based on detecting directly visible objects annotated via bounding boxes, the detection and annotation of light reflections without sharp boundaries is challenging. For this reason, the extensive open-source dataset PVDN (Provident Vehicle Detection at Night) was published, which includes traffic scenarios at night with light reflections annotated via keypoints. In this paper, we explore the potential of saliency-based approaches to create different object representations based on the visual saliency and sparse keypoint annotations of the PVDN dataset. For that, we extend the general idea of Boolean map saliency towards a context-aware approach by taking into consideration sparse keypoint annotations by humans. We show that this approach allows for an automated derivation of different object representations, such as binary maps or bounding boxes so that detection models can be trained on different annotation variants and the problem of providently detecting vehicles at night can be tackled from different perspectives. With that, we provide further powerful tools and methods to study the problem of detecting vehicles at night before they are actually visible.
Coupling Matrix-based Beamforming for Superdirective Antenna Arrays
Venue: arXiv cs.IT 2022
Year: 2022

In most multiple-input multiple-output (MIMO) communication systems, e.g., Massive MIMO, the antenna spacing is generally no less than half a wavelength. It helps to reduce the mutual coupling and therefore facilitate the system design. The maximum array gain is the number of antennas in this settings. However, when the antenna spacing is made very small, the array gain of a compact array can be proportional to the square of the number of antennas - a value much larger than the traditional array. To achieve this so-called "superdirectivity" however, the calculation of the excitation coefficients (beamforming vector) is known to be a challenging problem. In this paper, we derive the beamforming vector of superdirective arrays based on a novel coupling matrix-enabled method. We also propose an approach to obtain the coupling matrix, which is derived by the spherical wave expansion method and active element pattern. The full-wave electromagnetic simulations are conducted to validate the effectiveness of our proposed method. Simulation results show that when the beamforming vector obtained by our method is applied, the directivity of the designed dipole antenna array has a good agreement with the theoretical values.