Enabling Calibration In The Zero-Shot Inference of Large Vision-Language Models

Calibration of deep learning models is crucial to their trustworthiness and safe usage, and as such, has been extensively studied in supervised classification models, with methods crafted to decrease miscalibration. However, there has yet to be a comprehensive study of the calibration of vision-language models that are used for zero-shot inference, like CLIP. We measure calibration across relevant variables like prompt, dataset, and architecture, and find that zero-shot inference with CLIP is miscalibrated. Furthermore, we propose a modified version of temperature scaling that is aligned with the common use cases of CLIP as a zero-shot inference model, and show that a single learned temperature generalizes for each specific CLIP model (defined by a chosen pre-training dataset and architecture) across inference dataset and prompt choice

Seasonal variability in physicochemical parameters and fish larval abundance along the coastal waters of Dakshina Kannada, southwest coast of India

The study attempted to record the seasonal variability in the physicochemical parameters that influence the fish larval abundance in six stations from the Netravathi-Gurupura and the Mulki-Pavanje estuaries along the coast of Dakshina Kannada for a period of 36 months (2014-2016). Pronounced variations in the primary production, chlorophyll a, and physicochemical parameters such as water temperature, pH, salinity, dissolved oxygen, and nutrients were observed in the estuarine and coastal waters. Between stations, the one-way ANOVA revealed highly significant variations (p<0.001) in pH, salinity, Chlorophyll a, Nitrate-N, Ammonia- N, and Silicate-Si concentrations and significant differences in dissolved oxygen and chlorophyll c (p< 0.05) concentrations. Multivariate comparisons (Tukey HSD) revealed variations in the physicochemical parameters within the stations. Nearshore stations and estuarine waters were distinct concerning physicochemical parameters. Environmental factors influencing fish larval abundance in the nearshore waters include rainfall (r = 0.487, p< 0.01), river discharge (r = 0.444, p< 0.01), dissolved oxygen (r = 0.395, p< 0.05), and silicate-Si concentration (r = 0.423, p<0.05). Similarly, the tidal height (r= 0.536, p<0.01) also played an additional key role in influencing and determining the seasonal abundance of fish larvae in the estuarine waters. The water quality index (WQI) in estuaries and nearshore waters is indicated as Good to Poor state as per USEPA (2012) rating. Improving the quality of near-shore coastal waters can increase the survival of fish larvae, protect fish breeding sites, and ultimately contribute to enhanced fisheries productivit

Biometric Assessment of Temporomandibular Disorders in Orthodontics: A Multi-arm Randomized Controlled Trial

Objective:This randomized controlled trial aimed to evaluate the role of fixed orthodontic treatment in the aggravation, precipitation, or alleviation of temporomandibular disorders in young adults.Methods:Sixty patients were randomly assigned to 4 groups of 15 patients each (group I, orthodontic treatment in temporomandibular disorder-free orthodontic patients; group II, orthodontic treatment in patients with mild symptoms of temporomandibular disorders; group III, splint therapy accompanied by orthodontic treatment in patients with moderate symptoms; and group IV, control with no treatment). The biometric equipment used were the T-scan, to analyze the occlusal component; the BioEMG for muscular analysis; BioJVA for temporomandibular joint acoustic analysis; and JT3D for mandibular kinematic analysis. The paired t-test and ANOVA were used for intragroup and intergroup comparisons, respectively. The difference between groups was assessed using post hoc Tukey’s test.Results:Groups I and III showed significant difference in the occlusal, muscular, temporomandibular joint vibration, and kinematic mandibular assessment variables. Group II showed significant improvement in occlusal variables only. Group IV did not show improvement in any of the variables except for certain muscular components.Conclusion:Successful practical utilization of biometric equipment revealed that fixed orthodontic treatment does not aggravate temporomandibular disorders. It was also found that temporomandibular disorders due to malocclusion can be treated successfully with orthodontic treatment, whereas temporomandibular disorders due to multifactorial temporomandibular joint and muscular components might require splint therapy before orthodontic intervention

Find Thy Neighbourhood: Privacy-Preserving Local Clustering

Identifying a cluster around a seed node in a graph, termed local clustering, finds use in several applications, including fraud detection, targeted advertising, community detection, etc. However, performing local clustering is challenging when the graph is distributed among multiple data owners, which is further aggravated by the privacy concerns that arise in disclosing their view of the graph. This necessitates designing solutions for privacy-preserving local clustering and is addressed for the first time in the literature. We propose using the technique of secure multiparty computation (MPC) to achieve the same. Our local clustering algorithm is based on the heat kernel PageRank (HKPR) metric, which produces the best-known cluster quality. En route to our final solution, we have two important steps: (i) designing data-oblivious equivalent of the state-of-the-art algorithms for computing local clustering and HKPR values, and (ii) compiling the data-oblivious algorithms into its secure realisation via an MPC framework that supports operations over fixed-point arithmetic representation such as multiplication and division. Keeping efficiency in mind for large graphs, we choose the best-known honest-majority 3-party framework of SWIFT (Koti et al., USENIX\u2721) and enhance it with some of the necessary yet missing primitives, before using it for our purpose. We benchmark the performance of our secure protocols, and the reported run time showcases the practicality of the same. Further, we perform extensive experiments to evaluate the accuracy loss of our protocols. Compared to their cleartext counterparts, we observe that the results are comparable and thus showcase the practicality of the designed protocols

Vogue: Faster Computation of Private Heavy Hitters

Consider the problem of securely identifying τ -heavy hitters, where given a set of client inputs, the goal is to identify those inputs which are held by at least τ clients in a privacy-preserving manner. Towards this, we design a novel system Vogue, whose key highlight in comparison to prior works, is that it ensures complete privacy and does not leak any information other than the heavy hitters. In doing so, Vogue aims to achieve as efficient a solution as possible. To showcase these efficiency improvements, we benchmark our solution and observe that it requires around 14 minutes to compute the heavy hitters for τ = 900 on 256-bit inputs when considering 400K clients. This is in contrast to the state of the art solution that requires over an hour for the same. In addition to the static input setting described above, Vogue also accounts for streaming inputs and provides a protocol that outperforms the state-of-the-art therein. The efficiency improvements witnessed while computing heavy hitters in both, the static and streaming input settings, are attributed to our new secure stable compaction protocol, whose round complexity is independent of the size of the input array to be compacte

Ruffle: Rapid 3-party shuffle protocols

Secure shuffle is an important primitive that finds use in several applications such as secure electronic voting, oblivious RAMs, secure sorting, to name a few. For time-sensitive shuffle-based applications that demand a fast response time, it is essential to design a fast and efficient shuffle protocol. In this work, we design secure and fast shuffle protocols relying on the techniques of secure multiparty computation. We make several design choices that aid in achieving highly efficient protocols. Specifically, we consider malicious 3-party computation setting with an honest majority and design robust ring-based protocols. Our shuffle protocols provide a fast online (i.e., input-dependent) phase compared to the state-of-the-art for the considered setting. To showcase the efficiency improvements brought in by our shuffle protocols, we consider two distinct applications of anonymous broadcast and secure graph computation via the GraphSC paradigm. In both cases, multiple shuffle invocations are required. Hence, going beyond standalone shuffle invocation, we identify two distinct scenarios of multiple invocations and provide customised protocols for the same. Further, we showcase that our customized protocols not only provide a fast response time, but also provide improved overall run time for multiple shuffle invocations. With respect to the applications, we not only improve in terms of efficiency, but also work towards providing improved security guarantees, thereby outperforming the respective state-of-the-art works. We benchmark our shuffle protocols and the considered applications to analyze the efficiency improvements with respect to various parameters

Blockchain and the World of Data

This is a video recording of a Workshop presented during the AIS 2022 Student Chapter Leadership Conference (SCLC) Blockchain and the World of Dat

Queer In AI: A Case Study in Community-Led Participatory AI

We present Queer in AI as a case study for community-led participatory design in AI. We examine how participatory design and intersectional tenets started and shaped this community's programs over the years. We discuss different challenges that emerged in the process, look at ways this organization has fallen short of operationalizing participatory and intersectional principles, and then assess the organization's impact. Queer in AI provides important lessons and insights for practitioners and theorists of participatory methods broadly through its rejection of hierarchy in favor of decentralization, success at building aid and programs by and for the queer community, and effort to change actors and institutions outside of the queer community. Finally, we theorize how communities like Queer in AI contribute to the participatory design in AI more broadly by fostering cultures of participation in AI, welcoming and empowering marginalized participants, critiquing poor or exploitative participatory practices, and bringing participation to institutions outside of individual research projects. Queer in AI's work serves as a case study of grassroots activism and participatory methods within AI, demonstrating the potential of community-led participatory methods and intersectional praxis, while also providing challenges, case studies, and nuanced insights to researchers developing and using participatory methods.Comment: To appear at FAccT 202