This paper proposes Oryx, a system for efficiently detecting cycles in federated graphs where parts of the graph are held by different parties and are private. Cycle identification is an important building block in designing fraud detection algorithms that operate on confidential transaction data held by different financial institutions. Oryx allows detecting cycles of various length while keeping the topology of the graphs secret, and it does so efficiently. Oryx leverages the observation that financial graphs are very sparse, and uses this to achieve computational complexity that scales with the average degree of nodes in the graph rather than the maximum degree. Our implementation of Oryx running on a single 32-core AWS machine (for each party) can detect all cycles of up to length 6 in under 5 hours in a financial transaction graph that consists of tens of millions of nodes and edges. While the costs are high, Oryx's protocol parallelizes well and can use additional hardware resources. Furthermore, Oryx is, to our knowledge, the first system that can handle this task for large graphs.

Gliomas are the most common intracranial primary tumors . Accurate grading of gliomas is crucial in determining treatment options and prognosis. Clinicians conventionally rely on multiple Magnetic Resonance Imaging (MRI) sequences for accurate glioma assessment. Traditional deep learning methods typically utilize the individual MRI sequence with the annotations of Region of Interest (ROIs), incurring substantial manual effort while losing the complementary information provided by other sequences. This task is inherently a standard multi-modal problem. However, existing methods often adopt complex multi-stream networks for feature extraction and fusion, leading to high resource demands and potential feature redundancy. To address the above challenge, a discrepancy-aware self-distillation method is proposed for multi-modal glioma grading, which requires only a single-stream network to concurrently analyze multiple MRI sequences without auxiliary ROIs. The first Modality Discrepancy-aware Fusion (MDF) module considers the MRI imaging differentiation and widens the inter-modal contrasts, allowing the modality-specific features to be highlighted and the modality-invariant features to be diminished. Furthermore, the proposed Class Activation Self-Distillation (CASD) strategy leverages the generated Class Activation Maps (CAMs) as dark knowledge for the distillation process . This guides shallow layers to focus on the category discriminative features specifically within the lesion region. Extensive experiments were conducted on the BraTS2018 and BraTS2019 datasets to evaluate the effectiveness of our method. The results show that our method outperforms other relevant glioma grading and multi-modal fusion methods on both datasets.

Smart shape-changing materials can be adapted to different usages, which have been leveraged for dynamic affordances and on-demand haptic feedback in HCI. However, the applicability of these materials is often bottlenecked by their complex fabrication and the challenge of programming localized and individually addressable responses. In this work, we propose a toolkit for designing and fabricating programmable morphing objects using off-the-shelf epoxies. Our method involves varying the crosslinker to epoxy resin ratio to control morphing temperatures from 40 ℃ to 90 ℃, either across different regions of a shape memory device or across devices. Functional components (e.g., conductive fabric, magnetic particles) are also incorporated with the epoxy for sensing and active reconfiguration. A toolbox of fabrication methods and a primitive design library are introduced to support design ideation and programmable morphing. Finally, we demonstrate application examples, including morphing toys, a shape-changing input device, and an active window shutter.

Privilege separation (privsep) is an effective technique for improving software’s security, but privsep involves decomposing software into components and assigning them different privileges. This is often laborious and error-prone. This paper contributes the following for applying privsep to C software: (1) a portable, lightweight, and distributed runtime library that abstracts externally-enforced compartment isolation; (2) an abstract compartmentalization model of software for reasoning about privsep; and (3) a privsep-aware Clang-based tool for code analysis and semi-automatic software transformation to use the runtime library. The evaluation spans 19 compartmentalizations of third-party software and examines: Security: 4 CVEs in widely-used software were rendered unexploitable; Approximate Effort Saving: on average, the synthesis-to-annotation code ratio was greater than 11.9 (i.e., 10 × lines of code were generated for each annotation); and Overhead: execution-time overhead was less than 2%, and memory overhead was linear in the number of compartments.

This paper introduces Ibex, an advertising system that reduces the amount of data that is collected on users while still allowing advertisers to bid on real-time ad auctions and measure the effectiveness of their ad campaigns. Specifically, Ibex addresses an issue in recent proposals such as Google's Privacy Sandbox Topics API in which browsers send information about topics that are of interest to a user to advertisers and demand-side platforms (DSPs). DSPs use this information to (1) determine how much to bid on the auction for a user who is interested in particular topics, and (2) measure how well their ad campaign does for a given audience (i.e., measure conversions). While Topics and related proposals reduce the amount of user information that is exposed, they still reveal user preferences. In Ibex, browsers send user information in an encrypted form that still allows DSPs and advertisers to measure conversions, compute aggregate statistics such as histograms about users and their interests, and obliviously bid on auctions without learning for whom they are bidding. Our implementation of Ibex shows that creating histograms is 1.-2.5× more expensive for browsers than disclosing user information, and Ibex's oblivious bidding protocol can finish auctions within 550 ms. We think this makes Ibex capable of preserving a good experience while improving user privacy.

From creating input devices to rendering tangible information, the field of HCI is interested in using kinematic mechanisms to create human-computer interfaces. Yet, due to fabrication and design challenges, it is often difficult to create kinematic devices that are compact and have multiple reconfigurable motional degrees of freedom (DOFs) depending on the interaction scenarios. In this work, we combine compliant mechanisms (CMs) with tensioning cables to create dynamically reconfigurable kinematic mechanisms. The devices’ kinematics (DOFs) is enabled and determined by the layout of bendable rods. The additional cables function as on-demand motion constraints that can dynamically lock or unlock the mechanism's DOFs as they are tightened or loosened. We provide algorithms and a design tool prototype to help users design such kinematic devices. We also demonstrate various HCI use cases including a kinematic haptic display, a haptic proxy, and a multimodal input device.

Recent proposals for the disaggregation of compute, memory, storage, and accelerators in data centers promise substantial operational benefits. Unfortunately, for resources like memory, this comes at the cost of performance overhead due to the potential insertion of network latency into every load and store operation. This effect is particularly felt by data-intensive systems due to the size of their working sets, the frequency at which they need to access memory, and the relatively low computation per access. This performance impairment offsets the elasticity benefit of disaggregated memory. This paper presents TELEPORT, a compute pushdown framework for data-intensive systems that run on disaggregated architectures; compared to prior work on compute pushdown, TELEPORT is unique in its efficiency and flexibility. We have developed optimization prin- ciples for several popular systems including a columnar in-memory DBMS, a graph processing system, and a MapReduce system. The evaluation results show that using TELEPORT to push down simple operators improves the performance of these systems on state-of-the-art disaggregated OSes by an order of magnitude, thus fully exploiting the elasticity of disaggregated data centers.

This paper introduces Mycelium, the first system to process differentially private queries over large graphs that are distributed across millions of user devices. Such graphs occur, for instance, when tracking the spread of diseases or malware. Today, the only practical way to query such graphs is to upload them to a central aggregator, which requires a great deal of trust from users and rules out certain types of studies entirely. With Mycelium, users' private data never leaves their personal devices unencrypted, and each user receives strong privacy guarantees. Mycelium does require the help of a central aggregator with access to a data center, but the aggregator merely facilitates the computation by providing bandwidth and computation power; it never learns the topology of the graph or the underlying data. Mycelium accomplishes this with a combination of homomorphic encryption, a verifiable secret redistribution scheme, and a mix network based on telescoping circuits. Our evaluation shows that Mycelium can answer a range of different questions from the medical literature with millions of devices.

Most existing studies on two-way relay networks (TWRNs) typically assume that the channels are reciprocal. However, in time-frequency dispersive channels, the channels between the multiple access channel phase and broadcast channel phase become highly non-reciprocal, which significantly complicates the indispensable channel estimation for TWRNs employing coherent detection. In this paper, the challenging problem of channel estimation and data detection (CE-DD) is investigated for amplify-and-forward (AF) orthogonal frequency division multiplexing (OFDM)-based TWRNs over unknown non-reciprocal time-frequency dispersive channels. An independent CE-DD algorithm according to the minimum mean-square error (MMSE) criterion is firstly proposed. To further improve system performance, an iterative joint CE-DD algorithm employing the variational Bayesian inference (VBI) framework is then developed. Simulation results show that initialized by the proposed MMSE-based algorithm, the proposed VBI-based iterative algorithm converges in a few iterations and after convergence, its performance approaches the ideal case which supposes perfect knowledge of channel state informations.

Most existing works on two‐way relay systems (TWRSs) are based on the assumption that the channels are reciprocal. However, in high‐speed moving scenarios, the channels between the multiple access channel phase and broadcast channel phase become non‐reciprocal, which significantly complicates the indispensable channel estimation for TWRSs employing coherent detection. In this study, the challenging problem of channel estimation and data detection (CEaDD) is investigated for amplify‐and‐forward orthogonal frequency division multiplexing‐based TWRSs under unknown non‐reciprocal doubly selective fading channels. First, an independent CEaDD algorithm according to the minimum mean‐square error (MMSE) criterion is proposed. To further improve system performance, an iterative joint CEaDD algorithm employing the variational Bayesian inference (VBI) framework is developed. It is shown by simulations that initialised by the proposed MMSE‐based algorithm, the proposed VBI‐based iterative algorithm converges in a few iterations and after convergence, its performance approaches the ideal case which assumes perfect knowledge of channel state information.

This letter develops a novel iterative joint channel state information estimation (CSIE), jammer estimation (JE) and data detection (DD) algorithm for orthogonal frequency division multiplexing (OFDM)-based distributed antenna systems in the presence of multiple unknown jammers. In contrast to existing methods that only heuristically combine CSIE, JE and DD, the proposed algorithm rigorously casts the three problems under a unified variational Bayesian inference framework. Simulation results demonstrate that the proposed algorithm converges rapidly, and after convergence, the bit-error-rate performance of the proposed algorithm is very close to that of the ideal case which supposes perfect knowledge of CSI and jammer. In addition, it is shown that the proposed algorithm significantly outperforms other state-of-the-art methods.

In this paper, the challenging problem of signal detection under severe communication environment that plagued by unknown doubly selective channels (DSCs), multiple narrowband interferences (NBIs) and phase noises (PNs) is investigated for orthogonal frequency division multiplexing based virtual multiple-input multiple-output (OFDM-V-MIMO) systems. Based on the Variational Bayesian Inference framework, a novel iterative algorithm for joint signal detection, DSC, NBI and PN estimations is proposed. Simulation results demonstrate quick convergence of the proposed algorithm, and after convergence, the bit-error-rate performance of the proposed signal detection algorithm is very close to that of the ideal case which assumes perfect channel state information, no PN, and known positions and powers of NBIs plus additive white Gaussian noise. Furthermore, simulation results show that the proposed signal detection algorithm outperforms other state-of-the-art methods.

In this paper we propose a novel linear interpolated channel estimation method in non-continuous subcarrier mapping between contiguous pilot symbols. The proposed method completes channel interpolation in the time domain instead of in the frequency domain to avoid the discontinuity of the channel frequency response when non-continuous subcarrier mapping is adopted. The simulation results show that our proposed method can overcome the drawbacks of interpolation in the frequency domain in non-continuous subcarrier mapping and can significantly improve the system performance in various mobile environments.

This paper addresses the problem of tackling multiple constraints simultaneously during a partitioning driven placement (PDP) process, where a larger solution space is available for constraint-satisfying optimization compared to post placement methods. A general methodology of multi-constraint satisfaction that balances violation correction and primary optimization is presented. A number of techniques are introduced to ensure its convergence and enhance its solution search capability with intermediate relaxation. Application of our approach to congestion control modeled as pin density and external net distribution balance constraints shows it effectively reduces overall congestion by 14.3% and improves chip area by 8.9%, with reasonable running time and only 1.6% increase in wire length. As far as we know, this is the first time an approach to congestion reduction during placement optimization produced good congestion improvement with very small wire length increase.

In this paper we take a fresh look at the partition-driven placement (PDP) paradigm for standard-cell placement for wire-length minimization. The goal is to develop several new algorithms for incorporation into a PDP framework that can rectify the well-known drawbacks of traditional PDP (increasingly localized view of nets with increasing levels of the partitioning tree, min cut objective, inaccuracy and cost of terminal propagation (TP), irreversibility of move decisions), while preserving its considerable advantages (time efficiency, flexibility in accurately incorporating many optimization metrics, and flexibility in satisfying most constraints). We have developed several novel techniques within a PDP-based framework that yield the best wire-length results so far on all but two of the MONO benchmark suite. Our major innovations are: (1) simultaneous level partitioning (SLP) in which we partition the entire circuit globally in every level of the partitioning tree, across the current cutline(s); (2) cell gain computation based on a global or distributed view of entire nets (thus obviating TP) and on the bounding-box (BB) minimization of nets (as opposed to mincut in prior PDP); (3) move irreversibility tackled in a pest-processing phase via vertical and horizontal swaps. Empirical results indicate that our PDP algorithm SPADE (for Simultaneous level Partitioning with Distributed lie., global] nEt views) provides almost 202 better wirelength results than an internal version of "regular" PDP with min-cut based gains, 10.8% better than the previous best PDP method QUAD, 10.6% better than TimberWolf (TW) 7.0, 15.846 better than the state-of-the-art force-directed technique from U. Munich (termed FD-98 here), and 15.3% better than the multilevel placement technique Snap-On. Besides TW7.0, we are also the only ones to report results on the approximately 100 K-cell circuit golem3 (12.2% better than TW7.0). Our run times are quite reasonable.