Abstract

n this paper, we try to tackle the problem of erroneous English-Hindi machine translation (MT) outputs due to the presence of the Verb Phrase Ellipsis (VPE) in English. The phenomenon of VPE is prominent in spoken English, and the antecedent to the ellipsis can come from previous sentences in a conversation as well. MT systems translate sentences as a whole and ignore the contextual information from the previous sentences. For these two reasons, spoken English-Hindi transla- tions suffer. We approached this problem by manually annotating 1200 two-person conversations that contain VPE and by studying how their resolution affects the translation qualities. Using our studies, we designed a rule-based system for the detection and resolution of VPE in English with the goal of improving their subsequent Hindi translation qualities. Our rule-based system is capable of the following: 1) Detection of VPE, 2) Resolution of Elided Head verb, 3) Resolution of Elided Head verb’s children, 4) Resolution of non-verbal predicates of a copula or a ’be’ main verb, 5) Modifying original sentence in the conversation with the resolved verb phrase. We also tested the system’s performance on VPE datasets outside of our annotated data. In this paper, we present our annotated corpus on conversational English VPE, our rule-based system to tackle VPE in the context of improving English-Hindi MT, the observations made as we designed this rule-based system and the performance-related observations of our system

Abstract

UV radiation-induced DNA damages have adverse effects on genome integrity and cellular function. The most prevalent UV-induced DNA lesion is the cyclobutane pyrimidine dimer (CPD), which can cause skin disorders and cancers in humans. Rad4/XPC is a damage sensing protein that recognizes and repairs CPD lesions with high fidelity. However, the molecular mechanism of how Rad4/XPC interrogates CPD lesions remains elusive. Emerging viewpoints indicate that the association of Rad4/XPC with DNA, the insertion of a lesion- sensing β-hairpin of Rad4/XPC into the lesion site, and the flipping of CPD’s partner bases (5’-dA and 3’-dA) are essential for damage recognition. Characterizing these slow events is challenging due to their infrequent occurrence on molecular time scales. Herein, we have used enhanced sampling and molecular dynamics simulations to investigate the mechanism and energetics of lesion recognition by Rad4/XPC, considering multiple plausible pathways between the crystal structure of the Rad4-DNA complex and nine intermediate states. Our results shed light on the most likely sequence of events, their potential coupling and energetics. Upon association, Rad4 and DNA form an encounter complex in which CPD and its partner bases remain in the duplex and the BHD3 β- hairpin is yet to be inserted into the lesion site. Subsequently, sequential base flipping occurs, with the flipping of the 5’-dA base preceding that of the 3’-dA base, followed by the insertion of the BHD3 β-hairpin into the lesion site. The results presented here have significant implications for understanding the molecular basis of UV-related skin disorders and cancers and for paving the way for novel therapeutic strategies. INTRODUCTION DNA plays a central role in the

Abstract

Peripheral nerve blocks are crucial to treatment of post-surgical pain and are associated with reduction in perioperative opioid use and hospital stay. Accurate interpretation of sono-anatomy is critical for the success of ultrasound (US) guided peripheral nerve blocks and can be challenging to the new operators. This prospective study enrolled 227 subjects who were systematically scanned for supraclavicular and interscalene brachial plexus in various settings using three different US machines to create a dataset of 227 unique videos. In total, 41,000 video frames were annotated by experienced anaesthesiologists using partial automation with object tracking and active contour algorithms. Four baseline neural network models were trained on the dataset and their performance was evaluated for object detection and segmentation tasks. Generalizability of the best suited model was then tested on the datasets constructed from separate US scanners with and without fine-tuning. The results demonstrate that

Abstract

The current study examines the role of constraints in well-defined problem-solving in ill-defined problem-solving. We chose variants of Raven’s advanced progressive matri- ces(APM) for well-defined problem-solving and creative rea- soning tasks (CRT) for ill-defined problem-solving. Using traditional APM, we created a novel version of APM with comparatively lesser constraints available to solve the puzzle, called creative APM(cAPM). The cAPM task was designed to induce divergent thinking along with convergent thinking. It is assumed that the difference in constraints changes the na- ture of the problem space in solving APM and cAPM and may differently affect the following creative reasoning task. We randomly assigned 50 participants to perform APM or cAPM, followed by the CRT, in a fixed order. We observed a sig- nificant effect of constraints available to solve well-defined problems on ill-defined problem-solving. The current result showed higher CRT scores when CRT preceded cAPM (Me- dian = 79.25) than APM (Median = 53.00). The result sug- gests that the flexibility in constraints to solve a well-defined problem induces more divergent thinking alongside convergent thinking and facilitates creative thinking required in ill-defined problem-solving.

Abstract

The Earth is a system of numerous interconnected spheres, such as the climate. Climate's global and regional influence requires understanding its evolution in space and time to improve knowledge and forecasts. Analyzing and studying decades of climate data is a data mining challenge. Cluster analysis minimizes data volumes and analyzes behavior by cluster. Understanding invariant behavior is as crucial as understanding variable behavior. Gridded data from two sources: Grided IMD data and CMIP5 HadCM3 decadal experiments, are studied using K-Means and MiSTIC clustering techniques to explore spatiotemporal clustering of maximum and minimum temperatures. The boundaries of k-means clustering correspond with topography. The Indian subcontinent's physiographic, climatic, and topographical characteristics affect MiSTIC's core areas. Both techniques yield overlapping clusters. The datasets' MiSTIC cluster counts varied significantly. The impact of data on this technique is shown in how the datasets group the Himalayas.

Abstract

We consider a distributed multi-user secret sharing setting consisting of a dealer, n storage nodes and m secrets where each user demands a t-subset of m secrets. The user downloads shares from the storage nodes based on the designed access structure and reconstructs its secrets. Earlier work in this setting dealt with the case of t = 1; in this work, we generalize it to natural numbers. We identify a necessary condition on the access structures to ensure weak secrecy. We also make a connection between the access structures for this problem and t-disjunct matrices. We apply various t-disjunct matrix constructions in this setting and compare their performance in terms of the number of storage nodes and communication complexity. We also derive bounds on the optimal communication complexity of a distributed secret sharing protocol. Finally, we characterize the capacity region of the DMUSS problem when the access structure is specified

Abstract

A code of length n is said to be (combinatorially) (ρ, L)-list decodable if the Hamming ball of radius ρn around any vector in the ambient space does not contain more than L codewords. We study a recently introduced class of higher order MDS codes, which are closely related (via duality) to codes that achieve a generalized Singleton bound for list decodability. For some ℓ ≥ 1, higher order MDS codes of length n, dimension k, and order ℓ are denoted as (n, k)-MDS(ℓ) codes. We present a number of results on the structure of these codes, identifying the ‘extend-ability’ of their parameters in various scenarios. Specifically, for some parameter regimes, we identify conditions under which (n1, k1)-MDS(ℓ1) codes can be obtained from (n2, k2)-MDS(ℓ2) codes, via various techniques. We believe that these results will aid in efficient constructions of higher order MDS codes. We also obtain a new field size upper bound for the existence of such codes, which arguably improves over the best known existing bound, in some parameter regimes. Due to space restrictions, the full version of this paper containing proofs of claims is made available in [1].

Abstract

Link forecasting in a temporal Knowledge Graph (tKG) in- volves predicting a future event from a given set of past events. Most previous studies suffered from reduced performance as they disregarded acyclic rules and enforced a tight constraint that all past events must exist in a strict temporal order. This paper proposes a novel explainable rule-based link forecasting framework by introducing two new concepts, namely ‘relaxed temporal cyclic and acyclic random walks’ and ‘link-star rules’. The former concept involves generating rules by performing cyclic and acyclic random walks on a tKG by taking into account the real-world phenomenon that the order of any two events may be ignored if their oc- currence time gap is within a threshold value. Link-star rules are a special class of acyclic rules generated based on the natural phenomenon that history repeats itself after a particular time. Link-star rules eliminate the problem of combinatorial rule explosion, thereby making our frame- work practicable. Experimental results demonstrate that our framework outperforms the state-of-the-art by a substantial margin. The evaluation measures hits@1 and mean reciprocal rank were improved by 45% and 23%, respectively. Keywords: Knowledge Graphs · Graph Analytics · Forecasting

Abstract

Bayesian Optimization (BO) is used to find the global optima of black box functions. In this work, we propose a practical BO method of function compositions where the form of the composition is known but the constituent functions are expensive to evaluate. By assuming an independent Gaussian process (GP) model for each of the constituent black-box function, we propose EI and UCB based BO algorithms and demonstrate their ability to outperform vanilla BO and the current state-of-art algorithms. We demonstrate a novel application of the proposed methods to dynamic pricing in revenue management when the underlying demand function is expensive to evaluate.

Abstract

This paper proposes a scalable distributed video analytics framework that can process thousands of video streams from sources such as CCTV cameras using semantic scene analysis. The main idea is to deploy deep learning pipelines on the fog nodes and generate semantic scene description records (SDRs) of video feeds from the associated CCTV cameras. These SDRs are transmitted to the cloud instead of video frames saving on network bandwidth. Using these SDRs stored on the cloud database, we can answer many complex queries and perform rich video analytics, within extremely low latencies. There is no need to scan and process the video streams again on a per query basis. The software architecture on the fog nodes allows for integrating new deep learning pipelines dynamically into the existing system, thereby supporting novel analytics and queries. We demonstrate the effectiveness of the system by proposing a novel distributed algorithm for real-time vehicle pursuit. The proposed algorithm involves asking multiple spatio-temporal queries in an adaptive fashion to reduce the query processing time and is robust to inaccuracies in the deployed deep learning pipelines and camera failures.