Search

X-ray computed tomography (XCT) is a key tool in non-destructive evaluation of additively manufactured (AM) parts, allowing for internal inspection and defect detection. Despite its widespread use, obtaining high-resolution CT scans can be extremely time consuming. This issue can be mitigated by performing scans at lower resolutions; however, reducing the resolution compromises spatial detail, limiting the accuracy of defect detection. Super-resolution algorithms offer a promising solution for overcoming resolution limitations in XCT reconstructions of AM parts, enabling more accurate detection of defects. While 2D super-resolution methods have demonstrated state-of-the-art performance on natural images, they tend to under-perform when directly applied to XCT slices. On the other hand, 3D super-resolution methods are computationally expensive, making them infeasible for large-scale applications. To address these challenges, we propose a 2.5D super-resolution approach tailored for XCT of AM parts. Our method enhances the resolution of individual slices by leveraging multi-slice information from neighboring 2D slices without the significant computational overhead of full 3D methods. Specifically, we use neighboring low-resolution slices to super-resolve the center slice, exploiting inter-slice spatial context while maintaining computational efficiency. This approach bridges the gap between 2D and 3D methods, offering a practical solution for high-throughput defect detection in AM parts., Comment: To be published in 2024 58th Asilomar Conference on Signals, Systems, and Computers

Hyperspectral neutron computed tomography enables 3D non-destructive imaging of the spectral characteristics of materials. In traditional hyperspectral reconstruction, the data for each neutron wavelength bin is reconstructed separately. This per-bin reconstruction is extremely time-consuming due to the typically large number of wavelength bins. Furthermore, these reconstructions may suffer from severe artifacts due to the low signal-to-noise ratio in each wavelength bin. We present a novel fast hyperspectral reconstruction algorithm for computationally efficient and accurate reconstruction of hyperspectral neutron data. Our algorithm uses a subspace extraction procedure that transforms hyperspectral data into low-dimensional data within an intermediate subspace. This step effectively reduces data dimensionality and spectral noise. High-quality reconstructions are then performed within this low-dimensional subspace. Finally, the algorithm expands the subspace reconstructions into hyperspectral reconstructions. We apply our algorithm to measured neutron data and demonstrate that it reduces computation and improves reconstruction quality compared to the conventional approach.

Hyperspectral neutron computed tomography is a tomographic imaging technique in which thousands of wavelength-specific neutron radiographs are typically measured for each tomographic view. In conventional hyperspectral reconstruction, data from each neutron wavelength bin is reconstructed separately, which is extremely time-consuming. These reconstructions often suffer from poor quality due to low signal-to-noise ratio. Consequently, material decomposition based on these reconstructions tends to lead to both inaccurate estimates of the material spectra and inaccurate volumetric material separation. In this paper, we present two novel algorithms for processing hyperspectral neutron data: fast hyperspectral reconstruction and fast material decomposition. Both algorithms rely on a subspace decomposition procedure that transforms hyperspectral views into low-dimensional projection views within an intermediate subspace, where tomographic reconstruction is performed. The use of subspace decomposition dramatically reduces reconstruction time while reducing both noise and reconstruction artifacts. We apply our algorithms to both simulated and measured neutron data and demonstrate that they reduce computation and improve the quality of the results relative to conventional methods.

In the supervised classification setting, during inference, deep networks typically make multiple predictions. For a pair of such predictions (that are in the top-k predictions), two distinct possibilities might occur. On the one hand, each of the two predictions might be primarily driven by two distinct sets of entities in the input. On the other hand, it is possible that there is a single entity or set of entities that is driving the prediction for both the classes in question. This latter case, in effect, corresponds to the network making two separate guesses about the identity of a single entity type. Clearly, both the guesses cannot be true, i.e. both the labels cannot be present in the input. Current techniques in interpretability research do not readily disambiguate these two cases, since they typically consider input attributions for one class label at a time. Here, we present a framework and method to do so, leveraging modern segmentation and input attribution techniques. Notably, our framework also provides a simple counterfactual "proof" of each case, which can be verified for the input on the model (i.e. without running the method again). We demonstrate that the method performs well for a number of samples from the ImageNet validation set and on multiple models., Comment: Accepted to Neural Information Processing Systems (NeurIPS 2024). ATTRIB Workshop

Industrial cone-beam X-ray computed tomography (CT) scans of additively manufactured components produce a 3D reconstruction from projection measurements acquired at multiple predetermined rotation angles of the component about a single axis. Typically, a large number of projections are required to achieve a high-quality reconstruction, a process that can span several hours or days depending on the part size, material composition, and desired resolution. This paper introduces a novel real-time system designed to optimize the scanning process by intelligently selecting the best next angle based on the object's geometry and computer-aided design (CAD) model. This selection process strategically balances the need for measurements aligned with the part's long edges against the need for maintaining a diverse set of overall measurements. Through simulations, we demonstrate that our algorithm significantly reduces the number of projections needed to achieve high-quality reconstructions compared to traditional methods., Comment: Submitted to 2024 Asilomar Conference on Signals, Systems, and Computers

Maximal extractable value (MEV) in which block proposers unethically gain profits by manipulating the order in which transactions are included within a block, is a key challenge facing blockchains such as Ethereum today. Left unchecked, MEV can lead to a centralization of stake distribution thereby ultimately compromising the security of blockchain consensus. To preserve proposer decentralization (and hence security) of the blockchain, Ethereum has advocated for a proposer-builder separation (PBS) in which the functionality of transaction ordering is separated from proposers and assigned to separate entities called builders. Builders accept transaction bundles from searchers, who compete to find the most profitable bundles. Builders then bid completed blocks to proposers, who accept the most profitable blocks for publication. The auction mechanisms used between searchers, builders and proposers are crucial to the overall health of the blockchain. In this paper, we consider PBS design in Ethereum as a game between searchers, builders and proposers. A key novelty in our design is the inclusion of future block proposers, as all proposers of an epoch are decided ahead of time in proof-of-stake (PoS) Ethereum within the game model. Our analysis shows the existence of alternative auction mechanisms that result in a better (more profitable) equilibrium to players compared to state-of-the-art. Experimental evaluations based on synthetic and real-world data traces corroborate the analysis. Our results highlight that a rethinking of auction mechanism designs is necessary in PoS Ethereum to prevent disruption.

Data sharding$\unicode{x2013}$in which block data is sharded without sharding compute$\unicode{x2013}$is at the present the favored approach for scaling Ethereum and other popular blockchains. A key challenge toward implementing data sharding is verifying whether the entirety of a block's data is available in the network (across its shards). A central technique proposed to conduct this verification uses erasure-coded blocks and is called data availability sampling (DAS). While the high-level protocol details of DAS have been well discussed in the community, discussions around how such a protocol will be implemented at the peer-to-peer layer are lacking. We identify random sampling of nodes as a fundamental primitive necessary to carry out DAS and present Honeybee, a decentralized algorithm for sampling nodes that uses verifiable random walks. Honeybee is secure against attacks even in the presence of a large number of Byzantine nodes (e.g., 50% of the network). We evaluate Honeybee through experiments and show that the quality of sampling achieved by Honeybee is significantly better compared to the state-of-the-art. Our proposed algorithm has implications for DAS functions in both full nodes and light nodes., Comment: 26 pages

Large Language Models (LLMs), like ChatGPT, have gained widespread popularity and usage in various software engineering tasks, including refactoring, testing, code review, and program comprehension. Despite recent studies delving into refactoring documentation in commit messages, issues, and code review, little is known about how developers articulate their refactoring needs when interacting with ChatGPT. In this paper, our goal is to explore conversations between developers and ChatGPT related to refactoring to better understand how developers identify areas for improvement in code and how ChatGPT addresses developers' needs. Our approach relies on text mining refactoring-related conversations from 17,913 ChatGPT prompts and responses, and investigating developers' explicit refactoring intention. Our results reveal that (1) developer-ChatGPT conversations commonly involve generic and specific terms/phrases; (2) developers often make generic refactoring requests, while ChatGPT typically includes the refactoring intention; and (3) various learning settings when prompting ChatGPT in the context of refactoring. We envision that our findings contribute to a broader understanding of the collaboration between developers and AI models, in the context of code refactoring, with implications for model improvement, tool development, and best practices in software engineering.

Modern attacks against enterprises often have multiple targets inside the enterprise network. Due to the large size of these networks and increasingly stealthy attacks, attacker activities spanning multiple hosts are extremely difficult to correlate during a threat-hunting effort. In this paper, we present a method for an efficient cross-host attack correlation across multiple hosts. Unlike previous works, our approach does not require lateral movement detection techniques or host-level modifications. Instead, our approach relies on an observation that attackers have a few strategic mission objectives on every host that they infiltrate, and there exist only a handful of techniques for achieving those objectives. The central idea behind our approach involves comparing (OS agnostic) activities on different hosts and correlating the hosts that display the use of similar tactics, techniques, and procedures. We implement our approach in a tool called Ostinato and successfully evaluate it in threat hunting scenarios involving DARPA-led red team engagements spanning 500 hosts and in another multi-host attack scenario. Ostinato successfully detected 21 additional compromised hosts, which the underlying host-based detection system overlooked in activities spanning multiple days of the attack campaign. Additionally, Ostinato successfully reduced alarms generated from the underlying detection system by more than 90%, thus helping to mitigate the threat alert fatigue problem, Comment: 21 pages, 5 figures

The emergence of blockchain technology has fostered the development of numerous decentralized applications (dapps) in recent years Pub/sub (publish/subscribe) systems play a crucial role by associating messages with specific topics and propagating them from publishers to subscribers across the network. Decentralized pub/sub aims to provide this functionality without relying on centralized control or global network state information, enabling message propagation among nodes in a coordinated manner. Efficiency in pub/sub services entails ensuring that subscribers receive published messages promptly. We introduce Topiary, a rapid and scalable protocol designed for decentralized applications' pub/sub systems. Topiary autonomously learns an efficient peer-to-peer (p2p) topology tailored to the publish/subscribe network. It does so by analyzing peers' interactions with their neighbors. Inspired by concepts from the multi-armed bandit problem, Topiary strikes an optimal balance between maintaining connections with well-connected neighbors and exploring new connections within the network, based on their topical needs. Through experimental evaluations, Topiary has shown a 50% reduction in broadcast latency while achieving an interested topic coverage of over 98%, marking it as a promising solution for efficient decentralized pub/sub networks.

X-ray computed tomography (XCT) is an important tool for high-resolution non-destructive characterization of additively-manufactured metal components. XCT reconstructions of metal components may have beam hardening artifacts such as cupping and streaking which makes reliable detection of flaws and defects challenging. Furthermore, traditional workflows based on using analytic reconstruction algorithms require a large number of projections for accurate characterization - leading to longer measurement times and hindering the adoption of XCT for in-line inspections. In this paper, we introduce a new workflow based on the use of two neural networks to obtain high-quality accelerated reconstructions from sparse-view XCT scans of single material metal parts. The first network, implemented using fully-connected layers, helps reduce the impact of BH in the projection data without the need of any calibration or knowledge of the component material. The second network, a convolutional neural network, maps a low-quality analytic 3D reconstruction to a high-quality reconstruction. Using experimental data, we demonstrate that our method robustly generalizes across several alloys, and for a range of sparsity levels without any need for retraining the networks thereby enabling accurate and fast industrial XCT inspections.

The upper mid-band - roughly from 7 to 24 GHz - has attracted considerable recent interest for new cellular services. This frequency range has vastly more spectrum than the highly congested bands below 7 GHz while offering more favorable propagation and coverage than the millimeter wave (mmWave) frequencies. The upper mid-band can thus provide a powerful and complementary frequency range to balance coverage and capacity. Realizing the full potential of these bands, however, will require fundamental changes to the design of cellular systems. Most importantly, spectrum will likely need to be shared with incumbents including communication satellites, military RADAR, and radio astronomy. Also, the upper mid-band is simply a vast frequency range. Due to this wide bandwidth, combined with the directional nature of transmission and intermittent occupancy of incumbents, cellular systems will need to be agile to sense and intelligently use large spatial and frequency degrees of freedom. This paper attempts to provide an initial assessment of the feasibility and potential gains of wideband cellular systems operating in the upper mid-band. The study includes: (1) a system study to assess potential gains of multi-band systems in a representative dense urban environment and illustrate the value of wide band system with dynamic frequency selectivity; (2) an evaluation of potential cross interference between satellites and terrestrial cellular services and interference nulling to reduce that interference; and (3) design and evaluation of a compact multi-band antenna array structure. Leveraging these preliminary results, we identify potential future research directions to realize next-generation systems in these frequencies., Comment: 18 pages

Blockchains offer strong security gurarantees, but cannot protect users against the ordering of transactions. Players such as miners, bots and validators can reorder various transactions and reap significant profits, called the Maximal Extractable Value (MEV). In this paper, we propose an MEV aware protocol design called Masquerade, and show that it will increase user satisfaction and confidence in the system. We propose a strict per-transaction level of ordering to ensure that a transaction is committed either way even if it is revealed. In this protocol, we introduce the notion of a "token" to mitigate the actions taken by an adversary in an attack scenario. Such tokens can be purchased voluntarily by users, who can then choose to include the token numbers in their transactions. If the users include the token in their transactions, then our protocol requires the block-builder to order the transactions strictly according to token numbers. We show through extensive simulations that this reduces the probability that the adversaries can benefit from MEV transactions as compared to existing current practices.

Mining in proof-of-work blockchains has become an expensive affair requiring specialized hardware capable of executing several megahashes per second at huge electricity costs. Miners earn a reward each time they mine a block within the longest chain, which helps offset their mining costs. It is therefore of interest to miners to maximize the number of mined blocks in the blockchain and increase revenue. A key factor affecting mining rewards earned is the connectivity between miners in the peer-to-peer network. To maximize rewards a miner must choose its network connections carefully, ensuring existence of paths to other miners that are on average of a lower latency compared to paths between other miners. We formulate the problem of deciding whom to connect to for miners as a combinatorial bandit problem. Each node picks its neighbors strategically to minimize the latency to reach 90\% of the hash power of the network relative to the 90-th percentile latency from other nodes. A key contribution of our work is the use of a network coordinates based model for learning the network structure within the bandit algorithm. Experimentally we show our proposed algorithm outperforming or matching baselines on diverse network settings.

Graph convolutional networks (GCNs) have achieved huge success in several machine learning (ML) tasks on graph-structured data. Recently, several sampling techniques have been proposed for the efficient training of GCNs and to improve the performance of GCNs on ML tasks. Specifically, the subgraph-based sampling approaches such as ClusterGCN and GraphSAINT have achieved state-of-the-art performance on the node classification tasks. These subgraph-based sampling approaches rely on heuristics -- such as graph partitioning via edge cuts -- to identify clusters that are then treated as minibatches during GCN training. In this work, we hypothesize that rather than relying on such heuristics, one can learn a reinforcement learning (RL) policy to compute efficient clusters that lead to effective GCN performance. To that end, we propose PolicyClusterGCN, an online RL framework that can identify good clusters for GCN training. We develop a novel Markov Decision Process (MDP) formulation that allows the policy network to predict ``importance" weights on the edges which are then utilized by a clustering algorithm (Graclus) to compute the clusters. We train the policy network using a standard policy gradient algorithm where the rewards are computed from the classification accuracies while training GCN using clusters given by the policy. Experiments on six real-world datasets and several synthetic datasets show that PolicyClusterGCN outperforms existing state-of-the-art models on node classification task.

Peer-to-peer (P2P) networks underlie a variety of decentralized paradigms including blockchains, distributed file storage and decentralized domain name systems. A central primitive in P2P networks is the peer selection algorithm, which decides how a node should select a fixed number of neighbors to connect with. In this paper, we consider the design of a peer-selection algorithm for unstructured P2P networks with the goal of minimizing the broadcast latency. We propose Goldfish, a novel solution that dynamically decides the neighbor set by exploiting the past experiences as well as exploring new neighbors. The key technical contributions come from bringing ideas of matrix completion for estimating message delivery times for every possible message for every peer ever connected, and a streaming algorithm to efficiently perform the estimation while achieving good performance. The matrix completion interpolates the delivery times to all virtual connections in order to select the best combination of neighbors. Goldfish employs a streaming algorithm that only uses a short recent memory to finish matrix interpolation. When the number of publishing source is equal to a node's maximal number of connections, Goldfish found the global optimal solution with 92.7% probability by exploring every node only once. In more complex situations where nodes are publishing based on exponential distribution and adjusting connection in real time, we compare Goldfish with a baseline peer selection system, and show Goldfish saves approximately 14.5% less time under real world geolocation and propagation latency.

Hyperspectral neutron tomography is an effective method for analyzing crystalline material samples with complex compositions in a non-destructive manner. Since the counts in the hyperspectral neutron radiographs directly depend on the neutron cross-sections, materials may exhibit contrasting neutron responses across wavelengths. Therefore, it is possible to extract the unique signatures associated with each material and use them to separate the crystalline phases simultaneously. We introduce an autonomous material decomposition (AMD) algorithm to automatically characterize and localize polycrystalline structures using Bragg edges with contrasting neutron responses from hyperspectral data. The algorithm estimates the linear attenuation coefficient spectra from the measured radiographs and then uses these spectra to perform polycrystalline material decomposition and reconstructs 3D material volumes to localize materials in the spatial domain. Our results demonstrate that the method can accurately estimate both the linear attenuation coefficient spectra and associated reconstructions on both simulated and experimental neutron data.

Non-destructive characterization of multi-layered structures that can be accessed from only a single side is important for applications such as well-bore integrity inspection. Existing methods related to Synthetic Aperture Focusing Technique (SAFT) rapidly produce acceptable results but with significant artifacts. Recently, ultrasound model-based iterative reconstruction (UMBIR) approaches have shown significant improvements over SAFT. However, even these methods produce ringing artifacts due to the high fractional-bandwidth of the excitation signal. In this paper, we propose a ringing artifact reduction method for ultrasound image reconstruction that uses a multi-agent consensus equilibrium (RARE-MACE) framework. Our approach integrates a physics-based forward model that accounts for the propagation of a collimated ultrasonic beam in multi-layered media, a spatially varying image prior, and a denoiser designed to suppress the ringing artifacts that are characteristic of reconstructions from high-fractional bandwidth ultrasound sensor data. We test our method on simulated and experimental measurements and show substantial improvements in image quality compared to SAFT and UMBIR., Comment: arXiv admin note: text overlap with arXiv:2211.15859, arXiv:2202.09703

Neutron computed tomography (nCT) is a 3D characterization technique used to image the internal morphology or chemical composition of samples in biology and materials sciences. A typical workflow involves placing the sample in the path of a neutron beam, acquiring projection data at a predefined set of orientations, and processing the resulting data using an analytic reconstruction algorithm. Typical nCT scans require hours to days to complete and are then processed using conventional filtered back-projection (FBP), which performs poorly with sparse views or noisy data. Hence, the main methods in order to reduce overall acquisition time are the use of an improved sampling strategy combined with the use of advanced reconstruction methods such as model-based iterative reconstruction (MBIR). In this paper, we propose an adaptive orientation selection method in which an MBIR reconstruction on previously-acquired measurements is used to define an objective function on orientations that balances a data-fitting term promoting edge alignment and a regularization term promoting orientation diversity. Using simulated and experimental data, we demonstrate that our method produces high-quality reconstructions using significantly fewer total measurements than the conventional approach.

Collimated beam ultrasound systems are a technology for imaging inside multi-layered structures such as geothermal wells. These systems work by using a collimated narrow-band ultrasound transmitter that can penetrate through multiple layers of heterogeneous material. A series of measurements can then be made at multiple transmit frequencies. However, commonly used reconstruction algorithms such as Synthetic Aperture Focusing Technique (SAFT) tend to produce poor quality reconstructions for these systems both because they do not model collimated beam systems and they do not jointly reconstruct the multiple frequencies. In this paper, we propose a multi-frequency ultrasound model-based iterative reconstruction (UMBIR) algorithm designed for multi-frequency collimated beam ultrasound systems. The combined system targets reflective imaging of heterogeneous, multi-layered structures. For each transmitted frequency band, we introduce a physics-based forward model to accurately account for the propagation of the collimated narrow-band ultrasonic beam through the multi-layered media. We then show how the joint multi-frequency UMBIR reconstruction can be computed by modeling the direct arrival signals, detector noise, and incorporating a spatially varying image prior. Results using both simulated and experimental data indicate that multi-frequency UMBIR reconstruction yields much higher reconstruction quality than either single frequency UMBIR or SAFT.

Blockchains have become the catalyst for a growing movement to create a more decentralized Internet. A fundamental operation of applications in a decentralized Internet is data storage and retrieval. As today's blockchains are limited in their storage functionalities, in recent years a number of peer-to-peer data storage networks have emerged based on the Kademlia distributed hash table protocol. However, existing Kademlia implementations are not efficient enough to support fast data storage and retrieval operations necessary for (decentralized) Web applications. In this paper, we present Kadabra, a decentralized protocol for computing the routing table entries in Kademlia to accelerate lookups. Kadabra is motivated by the multi-armed bandit problem, and can automatically adapt to heterogeneity and dynamism in the network. Experimental results show Kadabra achieving between 15-50% lower lookup latencies compared to state-of-the-art baselines., Comment: Financial Cryptography and Data Security 2023 (FC 2023); 27 pages, 20 figures

Video conferencing has become the preferred way of interacting virtually. Current video conferencing applications, like Zoom, Teams or WebEx, are centralized, cloud-based platforms whose performance crucially depends on the proximity of clients to their data centers. Clients from low-income countries are particularly affected as most data centers from major cloud providers are located in economically advanced nations. Centralized conferencing applications also suffer from occasional outages and are embattled by serious privacy violation allegations. In recent years, decentralized video conferencing applications built over p2p networks and incentivized through blockchain are becoming popular. A key characteristic of these networks is their openness: anyone can host a media server on the network and gain reward for providing service. Strong economic incentives combined with lower entry barrier to join the network, makes increasing server coverage to even remote regions of the world. These reasons, however, also lead to a security problem: a server may obfuscate its true location in order to gain an unfair business advantage. In this paper, we consider the problem of multicast tree construction for video conferencing sessions in open p2p conferencing applications. We propose DecVi, a decentralized multicast tree construction protocol that adaptively discovers efficient tree structures based on an exploration-exploitation framework. DecVi is motivated by the combinatorial multi-armed bandit problem and uses a succinct learning model to compute effective actions. Despite operating in a multi-agent setting with each server having only limited knowledge of the global network and without cooperation among servers, experimentally we show DecVi achieves similar quality-of-experience compared to a centralized globally optimal algorithm while achieving higher reliability and flexibility.

We report a new workflow for background-oriented schlieren (BOS), termed "physics-informed BOS," to extract density, velocity, and pressure fields from a pair of reference and distorted images. Our method uses a physics-informed neural network (PINN) to produce flow fields that simultaneously satisfy the measurement data and governing equations. For the high-speed flows of interest in this work, we specify a physics loss based on the Euler and irrotationality equations. BOS is a quantitative fluid visualization technique that is used to characterize high-speed flows. Images of a background pattern, positioned behind the target flow, are processed using computer vision and tomography algorithms to determine the density field. Crucially, BOS features a series of ill-posed inverse problems that require supplemental information (i.e., in addition to the images) to accurately reconstruct the flow. Current BOS workflows rely upon interpolation of the images or a penalty term to promote a globally- or piecewise-smooth solution. However, these algorithms are invariably incompatible with the flow physics, leading to errors in the density field. Physics-informed BOS directly reconstructs all the flow fields using a PINN that includes the BOS measurement model and governing equations. This procedure improves the accuracy of density estimates and also yields velocity and pressure data, which was not previously available. We demonstrate our approach by reconstructing synthetic data that corresponds to analytical and numerical phantoms as well as experimental measurements. Our physics-informed reconstructions are significantly more accurate than conventional BOS estimates. Further, to the best of our knowledge, this work represents the first use of a PINN to reconstruct a supersonic flow from experimental data of any kind.