Abstract

The Securities and Exchange Board of India is the regulatory body for securities and commodity market in India. A growing number of SEBI documents ranging from government regulations to legal case files are now available in the digital form. Advances in natural language processing and machine learning provide opportunities for extracting semantic insights from these documents. We present here a system that performs semantic processing of SEBI documents using state-of-the-art language models to produce enriched regulations containing timelines of amendments and cross references to legal case files.

Abstract

How the brain captures the meaning of linguistic stimuli across multiple views is still a critical open question in neuroscience. Consider three different views of the concept apartment: (1) picture (WP) presented with the target word label, (2) sentence (S) using the target word, and (3) word cloud (WC) containing the target word along with other semantically related words. Unlike previous efforts, which focus only on single view analysis, in this paper, we study the effectiveness of brain decoding in a zero-shot cross-view learning setup. Further, we propose brain decoding in the novel context of cross-view-translation tasks like image captioning (IC), image tagging (IT), keyword extraction (KE), and sentence formation (SF). Using extensive experiments, we demonstrate that cross-view zero-shot brain decoding is practical leading to ∼0.68 average pairwise accuracy across view pairs. Also, the decoded representations are sufficiently detailed to enable high accuracy for cross-view-translation tasks with following pairwise accuracy: IC (78.0), IT (83.0), KE (83.7) and SF (74.5).

Abstract

Several popular Transformer based language models have been found to be successful for text-driven brain encoding. However, existing literature leverages only pretrained text Transformer models and has not explored the efficacy of task-specific learned Transformer representations. In this work, we explore transfer learning from representations learned for ten popular natural language processing tasks (two syntactic and eight semantic) for predicting brain responses from two diverse datasets: Pereira (subjects reading sentences from paragraphs) and Narratives (subjects listening to the spoken stories). Encoding models based on task features are used to predict activity in different regions across the whole brain. Features from coreference resolution, NER, and shallow syntax parsing explain greater variance for the reading activity. On the other hand, for the listening activity, tasks such as paraphrase generation, summarization, and natural language inference show better encoding performance. Experiments across all 10 task representations provide the following cognitive insights: (i) language left hemisphere has higher predictive brain activity versus language right hemisphere, (ii) posterior medial cortex, temporoparieto-occipital junction, dorsal frontal lobe have higher correlation versus early auditory and auditory association cortex, (iii) syntactic and semantic tasks display a good predictive performance across brain region

Abstract

Enabling effective brain-computer interfaces requires understanding how the human brain encodes stimuli across modalities such as visual, language (or text), etc. Brain encoding aims at constructing fMRI brain activity given a stimulus. There exists a plethora of neural encoding models which study brain encoding for single mode stimuli: visual (pretrained CNNs) or text (pretrained language models). Few recent papers have also obtained separate visual and text representation models and performed late-fusion using simple heuristics. However, previous work has failed to explore: (a) the effectiveness of image Transformer models for encoding visual stimuli, and (b) co-attentive multi-modal modeling for visual and text reasoning. In this paper, we systematically explore the efficacy of image Transformers (ViT, DEiT, and BEiT) and multi-modal Transformers (VisualBERT, LXMERT, and CLIP) for brain encoding. Extensive experiments on two popular datasets, BOLD5000 and Pereira, provide the following insights. (1) To the best of our knowledge, we are the first to investigate the effectiveness of image and multi-modal Transformers for brain encoding. (2) We find that VisualBERT, a multi-modal Transformer, significantly outperforms previously proposed single-mode CNNs, image Transformers as well as other previously proposed multi-modal models, thereby establishing new state-of-theart. The supremacy of visio-linguistic models raises the question of whether the responses elicited in the visual regions are affected implicitly by linguistic processing even when passively viewing images. Future fMRI tasks can verify this computational insight in an appropriate experimental setting.

Abstract

Cryptocurrencies are poised to revolutionize the modern economy by democratizing commerce. These currencies operate on top of blockchain-based distributed ledgers. Existing permissionless blockchain-based protocols offer unparalleled benefits like decentralization, anonymity, and transparency. However, these protocols suffer in performance which hinders their widespread adoption. In particular, high time-to-finality and low transaction rates keep them from replacing centralized payment systems such as the Visa network. Permissioned blockchain protocols offer attractive performance guarantees, but they are not considered suitable for deploying decentralized cryptocurrencies due to their centralized nature. Researchers have developed several multi-layered blockchain protocols that combine both permissioned and permissionless blockchain protocols to achieve high performance along with decentralization. The key idea with existing layered blockchain protocols in literature is to divide blockchain operations into two layers and use different types of blockchain protocols to manage each layer. However, many such works come with the assumptions of honest majority which may not accurately reflect the real world where the participants may be self-interested or rational. These assumptions may render the protocols susceptible to security threats in the real world, as highlighted by the literature focused on exploring game-theoretic attacks on these protocols. We generalize the “layered” approach taken by existing protocols in the literature and present a framework to analyze the system in the BAR Model and provide a generalized game-theoretic analysis of such protocols. Using our analysis, we identify the critical system parameters required for a distributed ledger’s secure operation in a more realistic setting.

Abstract

Multi-object multi-part scene parsing is a challenging task which requires detecting multiple object classes in a scene and segmenting the semantic parts within each object. In this paper, we propose FLOAT, a factorized label space framework for scalable multi-object multi-part parsing. Our framework involves independent dense prediction of object category and part attributes which increases scalability and reduces task complexity compared to the monolithic label space counterpart. In addition, we propose an inference-time ‘zoom’ refinement technique which significantly improves segmentation quality, especially for smaller objects/parts. Compared to state of the art, FLOAT obtains an absolute improvement of 2.0% for mean IOU (mIOU) and 4.8% for segmentation quality IOU (sqIOU) on the Pascal-Part-58 dataset. For the larger Pascal-Part-108 dataset, the improvements are 2.1% for mIOU and 3.9% for

Abstract

The data distribution in popular crowd counting datasets is typically heavy tailed and discontinuous. This skew affects all stages within the pipelines of deep crowd counting approaches. Specifically, the approaches exhibit unacceptably large standard deviation wrt statistical measures (MSE, MAE). To address such concerns in a holistic manner, we make two fundamental contributions. Firstly, we modify the training pipeline to accommodate the knowledge of dataset skew. To enable principled and balanced minibatch sampling, we propose a novel smoothed Bayesian binning approach. More specifically, we propose a novel cost function which can be readily incorporated into existing crowd counting deep networks to encourage binaware optimization. As the second contribution, we introduce additional performance measures which are more inclusive and throw light on various comparative performance aspects of the deep networks. We also show that our binning-based modifications retain their superiority wrt the newly proposed performance measures. Overall, our contributions enable a practically useful and detail-oriented characterization of performance for crowd counting approaches.

Abstract

The Securities and Exchange Board of India (SEBI) is the regulatory body for securities and commodities in India. SEBI creates, and enforces regulations that must be followed by all listed companies. To the best of our knowledge, this is the first work on identifying the regulation (s) that a SEBI-related case violates, which could be of substantial value to companies, lawyers, and other stakeholders in the regulatory process. We create a dataset for this task by automatically extracting violations from publicly available case-files. Using this data, we explore various multi-label text classification methods to determine the potentially multiple regulations violated by (the facts of) a case. Our experiments demonstrate the importance of employing contextual text representations to understand complex financial and legal concepts. We also highlight the challenges that must be addressed to develop a fully functional system in the real-world.

Abstract

The normal foveal anatomy consists of the extrusion of the plexiform layers, a formed foveal pit, cone photoreceptor outer segment (OS) lengthening, and outer nuclear layer (ONL) widening relative to the parafoveal OS and ONL.1 Foveal development begins with the centrifugal displacement of the inner retinal layers, followed by the centripetal migration of the cone photoreceptors toward the incipient fovea and finally cone photoreceptor specialization (PRS) (Fig 1A–E ).2 3 , 4 Foveal pit formation begins midgestation and continues postnatally characterized by pit deepening and widening. Simultaneously, outer retinal specialization occurs at a rapid rate in the first 2 years of life, but ONL widening continues up to the age of 13 years.4 Lengthening of the OS layer represents cone PRS and reflects peak cone photoreceptor density.5 The maturation of the human fovea is a complex process and is critical for visual function.4 Many genes are involved in the development of the retina. Pathogenic variants in these genes often cause disruption to the foveal developmental process, resulting in foveal hypoplasia (FH), which describes the underdevelopment of the fovea.Foveal hypoplasia is characterized by the continuation of inner retinal layers posterior to the foveola, and progressive loss of these foveal elements is represented by increasing grades of FH. Three key foveal development stages form the basis of the Leicester Grading System for FH, developed by Thomas et al1 (Fig 1F–H). The Leicester Grading System is divided into 4 grades of typical FH (grades 1–4) and 1 grade of atypical FH, which is associated with photoreceptor degeneration. The Leicester Grading System can be applied to a diverse range of genetic disorders, including albinism, achromatopsia, and those caused by pathogenic variants in PAX6, SLC38A8, FRMD7, and AHR. In addition to providing insight into the degree of foveal development, identifying the grade of FH has diagnostic and prognostic implications.1 ,7 Rufai et al7 reported that identifying the grade of FH can predict future visual acuity (VA) in preverbal children with nystagmus. The introduction of OCT has revolutionized ophthalmic diagnosis with the ability to promptly visualize retinal morphology in a high-resolution, noninvasive manner.8 ,9 Furthermore, the advent of handheld OCT has facilitated the foveal examination of pediatric patients.10 OCT is now available in most ophthalmology clinics and subsequently has rapidly become part of routine ophthalmic assessment.8 Thus, the necessary technology to identify foveal developmental abnormalities is now accessible in most centers. The phenotypic spectrum of albinism has been reported, including the variance in degree of arrested retinal development.11 ,12 Furthermore, high grades of FH (grades 3 and 4) have recently been consistently associated with SLC38A8 variants13 ; however, to date, the full spectrum of FH linked with variants of known genes involved in retinal development has not been investigated. Thus, it is unclear whether variants of certain genes known to be related to FH and nystagmus are associated with a more underdeveloped foveal morphology. We performed a comparative study to investigate and characterize the genotypic and phenotypic spectrum of FH in albinism, achromatopsia, PAX6, SLC38A8, FRMD7, and AHR variants.

Abstract

Complementary Split Ring Resonators (CSRR) and Inter-Digitated Capacitors (IDC) based RF sensors have been ubiquitously optimized assuming empty cavities. How- ever, in bio-sensing applications, most of the samples are dissolved in respective solvents. As a result, designing sen-sors based on specific solvent-filled cavities is the optimal approach for future RF bio-sensors. This paper presents the application of Binary Particle Swarm Optimization (BPSO) technique for maximizing the sensitivity of CSRR and IDC sensors. BPSO optimizes the designs by cell patterning the sensing region in the presence of phosphate buffer as a solvent. An enhancement of 47.38× for the CSRR and 2.03× for the IDC in the detection of L-Lysine at frequencies 2.18 GHz and 0.72 GHz respectively has been achieved, when compared to sensitivities of their traditional counterparts. Promising results have also been observed in the detection of sucrose and glucose, making it a suitable contender for optimization of next-generation RF bio-sensors