Reducing Health Misinformation in Search Results

People commonly search the web for answers to health-related questions. With health information being added to the Internet every day, misinformation proliferates and disseminates wildly. Previous work has shown that if health misinformation exists in search results, people can make incorrect decisions, which may cause negative effects on their lives. To reduce health misinformation in search results, we need to be able to find web documents that contain correct information and promote them to higher positions in search results over documents that contain misinformation. In this thesis, we describe our efforts in reducing health misinformation in search results. First, we describe our participation in the TREC 2021 Health Misinformation Track, which provides a framework for evaluating ranking approaches to reducing health misinformation in search results. This track uses the Compatibility Difference as the primary evaluation metric, which measures the approach's ability to rank correct and credible documents before incorrect and non-credible documents. In the 2021 track, runs that used the provided correct answers were viewed as manual runs. By making use of the known answers and applying a Stance Detection Model for reranking, our manual method achieved a Compatibility Difference score of 0.176, a dramatic improvement over the BM25 baseline with a score of -0.022. Second, as an extension of our work above, we present a pipeline to automatically derive correct answers by learning trustworthy web sources and then reduce health misinformation in search engine results. Determining the correct answer has been a difficult hurdle to overcome for participants in the TREC Health Misinformation Track. In the 2021 track, automatic runs were not allowed to use the known answer to a topic’s health question. By exploiting an existing set of health questions and corresponding known answers, we show it is possible to learn which web hosts are trustworthy, from which we can predict the correct answers to the 2021 health questions with an accuracy of 76%. Using our predicted answers, we can promote documents that we predict contain this answer and achieve a Compatibility Difference score of 0.129, achieving a three-fold performance increase compared with the previous best automatic method with a score of 0.043. To wrap up, evaluated on the TREC 2021 Health Misinformation Track, our final pipeline achieves new state-of-the-art performance among automatic runs

Mitigate Replication and Copying in Diffusion Models with Generalized Caption and Dual Fusion Enhancement

While diffusion models demonstrate a remarkable capability for generating high-quality images, their tendency to `replicate' training data raises privacy concerns. Although recent research suggests that this replication may stem from the insufficient generalization of training data captions and duplication of training images, effective mitigation strategies remain elusive. To address this gap, our paper first introduces a generality score that measures the caption generality and employ large language model (LLM) to generalize training captions. Subsequently, we leverage generalized captions and propose a novel dual fusion enhancement approach to mitigate the replication of diffusion models. Our empirical results demonstrate that our proposed methods can significantly reduce replication by 43.5% compared to the original diffusion model while maintaining the diversity and quality of generations

Making Models Shallow Again: Jointly Learning to Reduce Non-Linearity and Depth for Latency-Efficient Private Inference

Large number of ReLU and MAC operations of Deep neural networks make them ill-suited for latency and compute-efficient private inference. In this paper, we present a model optimization method that allows a model to learn to be shallow. In particular, we leverage the ReLU sensitivity of a convolutional block to remove a ReLU layer and merge its succeeding and preceding convolution layers to a shallow block. Unlike existing ReLU reduction methods, our joint reduction method can yield models with improved reduction of both ReLUs and linear operations by up to 1.73x and 1.47x, respectively, evaluated with ResNet18 on CIFAR-100 without any significant accuracy-drop

A Numerical Simulation of the Mean Water Pathways in the Subtropical and Tropical Pacific Ocean

A reduced-gravity, primitive-equation, upper-ocean general circulation model is used to study the mean water pathways in the North Pacific subtropical and tropical ocean. The model features an explicit physical representation of the surface mixed layer, realistic basin geometry, observed wind and heat flux forcing, and a horizontal grid-stretching technique and a vertical sigma coordinate to obtain a realistic simulation of the subtropical/tropical circulation. Velocity fields, and isopycnal and trajectory analyses are used to understand the mean flow of mixed layer and thermocline waters between the subtropics and Tropics. Subtropical/tropical water pathways are not simply direct meridional routes; the existence of vigorous zonal current systems obviously complicates the picture. In the surface mixed layer, upwelled equatorial waters flow into the subtropical gyre mainly through the midlatitude western boundary current (the model Kuroshio). There is additionally an interior ocean pathway, through the Subtropical Countercurrent (an eastward flow across the middle of the subtropical gyre), that directly feeds subtropical subduction sites. Below the mixed layer, the water pathways in the subtropical thermocline essentially reflect the anticyclonic gyre circulation where we find that the model subtropical gyre separates into two circulation centers. The surface circulation also features a double-cell pattern, with the poleward cell centered at about 30°N and the equatorward component contained between 15° and 25°N. In addition, thermocline waters that can be traced to subtropical subduction sites move toward the Tropics almost zonally across the basin, succeeding in flowing toward the equator only along relatively narrow north–south conduits. The low-latitude western boundary currents serve as the main southward circuit for the subducted subtropical thermocline water. However, the model does find a direct flow of thermocline water into the Tropics through the ocean interior, confined to the far western Pacific (away from the low-latitude western boundary currents) across 10°N. This interior pathway is found just to the west of a recirculating gyre in and just below the mixed layer in the northeastern Tropics. This equatorward interior flow and a flow that can be traced directly to the western boundary are then swept eastward by the deeper branches of the North Equatorial Countercurrent, finally penetrating to the equator in the central and eastern Pacific. Most of these results are consistent with available observations and recently published theoretical and idealized numerical experiments, although the interior pathway of subtropical thermocline water into the Tropics found in this experiment is not apparent in other published numerical simulations. Potential vorticity dynamics are useful in explaining the pathways taken by subtropical thermocline water as it flows into the Tropics. In particular, a large-scale zonally oriented “island” of homogenous potential vorticity, whose signature is determined by thin isopycnal layers in the central tropical Pacific along about 10°N, is dynamically linked to a circulation that does not flow directly from the subtropics to the Tropics. This large-scale potential vorticity feature helps to explain the circuitous pathways of the subducted subtropical thermocline waters as they approach the equator. Consequently, waters must first flow westward to the western boundary north of these closed potential vorticity contours and then mostly move southward through the low-latitude western boundary currents, flow eastward with the North Equatorial Countercurrent, and finally equatorward to join the Equatorial Undercurrent in the thermocline

C2PI: An Efficient Crypto-Clear Two-Party Neural Network Private Inference

Recently, private inference (PI) has addressed the rising concern over data and model privacy in machine learning inference as a service. However, existing PI frameworks suffer from high computational and communication costs due to the expensive multi-party computation (MPC) protocols. Existing literature has developed lighter MPC protocols to yield more efficient PI schemes. We, in contrast, propose to lighten them by introducing an empirically-defined privacy evaluation. To that end, we reformulate the threat model of PI and use inference data privacy attacks (IDPAs) to evaluate data privacy. We then present an enhanced IDPA, named distillation-based inverse-network attack (DINA), for improved privacy evaluation. Finally, we leverage the findings from DINA and propose C2PI, a two-party PI framework presenting an efficient partitioning of the neural network model and requiring only the initial few layers to be performed with MPC protocols. Based on our experimental evaluations, relaxing the formal data privacy guarantees C2PI can speed up existing PI frameworks, including Delphi [1] and Cheetah [2], up to 2.89x and 3.88x under LAN and WAN settings, respectively, and save up to 2.75x communication costs

Parallel PWMs Based Fully Digital Transmitter with Wide Carrier Frequency Range

The carrier-frequency (CF) and intermediate-frequency (IF) pulse-width modulators (PWMs) based on delay lines are proposed, where baseband signals are conveyed by both positions and pulse widths or densities of the carrier clock. By combining IF-PWM and precorrected CF-PWM, a fully digital transmitter with unit-delay autocalibration is implemented in 180 nm CMOS for high reconfiguration. The proposed architecture achieves wide CF range of 2 M–1 GHz, high power efficiency of 70%, and low error vector magnitude (EVM) of 3%, with spectrum purity of 20 dB optimized in comparison to the existing designs