Abstract

We identify a family of binary codes whose structure is similar to Reed-Muller (RM) codes and which include RM codes as a strict subclass. The codes in this family are denoted as Cn(r, m), and their duals are denoted as Bn(r, m). The length of these codes is n m, where n ≥ 2, and r is their ‘order’. When n = 2, Cn(r, m) is the RM code of order r and length 2 m. The special case of these codes corresponding to n being an odd prime was studied by Berman (1967) and Blackmore and Norton (2001). Following the terminology introduced by Blackmore and Norton, we refer to Bn(r, m) as the Berman code and Cn(r, m) as the dual Berman code. We identify these codes using a recursive Plotkin-like construction, and we show that these codes have a rich automorphism group, they are generated by the minimum weight codewords, and that they can be decoded up to half the minimum distance efficiently. Using a result of Kumar et al. (2016), we show that these codes achieve the capacity of the binary erasure channel (BEC) under bit-MAP decoding. Furthermore, except double transitivity, they satisfy all the code properties used by Reeves and Pfister to show that RM codes achieve the capacity of binary-input memoryless symmetric channels. Finally, when n is odd, we identify a large class of abelian codes that includes Bn(r, m) and Cn(r, m) and which achieves BEC capacity

Abstract

Recent state of the art models and new datasets have advanced many Natural Language Processing areas, especially, Machine Reading Comprehension tasks have improved with the help of datasets like SQuAD (Stanford Question Answering Dataset). But, large high quality datasets are still not a reality for low resource languages like Telugu to record progress in MRC. In this paper, we present a Telugu Question Answering Dataset - TeQuAD with the size of 82k parallel triples created by translating triples from the SQuAD. We also introduce a few methods to create similar Question Answering datasets for the low resource languages. Then, we present the performance of our models which outperform baseline models on Monolingual and Cross Lingual Machine Reading Comprehension (CLMRC) setups, the best of them resulting in an F1 score of 83 % and Exact Match (EM) score of 61 %

Abstract

Polysemy is the phenomenon where a single word form possesses two or more related senses. It is an extremely ubiquitous part of natural language and analyzing it has sparked rich discussions in the linguistics, psychology and philosophy communities alike. With scarce attention paid to polysemy in computational linguistics, and even scarcer attention toward quantifying polysemy, in this paper, we propose a novel, unsupervised framework to compute and estimate polysemy scores for words in multiple languages. We infuse our proposed quantification with syntactic knowledge in the form of dependency structures. This informs the final polysemy scores of the lexicon motivated by recent linguistic findings that suggest there is an implicit relation between syntax and ambiguity/polysemy. We adopt a graph based approach by computing the discrete Ollivier Ricci curvature on a graph of the contextual nearest neighbors. We test our framework on curated datasets controlling for different sense distributions of words in 3 typologically diverse languages - English, French and Spanish. The effectiveness of our framework is demonstrated by significant correlations of our quantification with expert human annotated language resources like WordNet. We observe a 0.3 point increase in the correlation coefficient as compared to previous quantification studies in English. Our research leverages contextual language models and syntactic structures to empirically support the widely held theoretical linguistic notion that syntax is intricately linked to ambiguity/polysemy

Abstract

We consider replication-based distributed storage systems in which each node stores the same quantum of data and each data bit stored has the same replication factor across the nodes. Such systems are referred to as balanced distributed databases. When existing nodes leave or new nodes are added to this system, the balanced nature of the database is lost, either due to the reduction in the replication factor, or the non-uniformity of the storage at the nodes. This triggers a rebalancing algorithm, that exchanges data between the nodes so that the balance of the database is reinstated. The goal is then to design rebalancing schemes with minimal communication load. In a recent work by Krishnan et al., coded transmissions were used to rebalance a carefully designed distributed database from a node removal or addition. These coded rebalancing schemes have optimal communication load, however, require the file-size to be at least exponential in the system parameters. In this work, we consider a cyclic balanced database (where data is cyclically placed in the system nodes) and present coded rebalancing schemes for node removal and addition in such a database. These databases (and the associated rebalancing schemes) require the file-size to be only cubic in the number of nodes in the system. We bound the advantage of our node removal rebalancing scheme over the uncoded scheme, and show that our scheme has a smaller communication load. In the node addition scenario, the rebalancing scheme presented is a simple uncoded scheme, which we show has optimal load. Due to space restrictions, the current version of this paper contains only a subset of the results concerning the node removal scenario. The full version of this paper, including additional results and examples, is available online

Abstract

In pliable index coding (PICOD), a number of clients are connected via a noise-free broadcast channel to a server which has a list of messages. Each client has a unique subset of messages at the server as side-information and requests for any one message not in the side-information. A PICOD scheme of length ℓ is a set of ℓ encoded transmissions broadcast from the server such that all clients are satisfied. Finding the optimal (minimum) length of PICOD and designing PICOD schemes that have small length are the fundamental questions in PICOD. In this paper, we use a hypergraph-based approach to derive new achievability and converse results for PICOD. We present an algorithm which gives an achievable scheme for PICOD with length at most ∆(H), where ∆(H) is the maximum degree of any vertex in a hypergraph that represents the PICOD problem. We also give a lower bound for the optimal PICOD length using a new structural parameter associated with the PICOD hypergraph called the nesting number. Finally, we also identify a class of problems for which our converse is tight.

Abstract

We identify a family of binary codes whose structure is similar to Reed-Muller (RM) codes and which include RM codes as a strict subclass. The codes in this family are denoted as Cn(r,m), and their duals are denoted as Bn(r,m). The length of these codes is nm, where n≥2, and r is their `order'. When n=2, Cn(r,m) is the RM code of order r and length 2m. The special case of these codes corresponding to n being an odd prime was studied by Berman (1967) and Blackmore and Norton (2001). Following the terminology introduced by Blackmore and Norton, we refer to Bn(r,m) as the Berman code and Cn(r,m) as the dual Berman code. We identify these codes using a recursive Plotkin-like construction, and we show that these codes have a rich automorphism group, they are generated by the minimum weight codewords, and that they can be decoded up to half the minimum distance efficiently. Using a result of Kumar et al. (2016), we show that these codes achieve the capacity of the binary erasure channel (BEC) under bit-MAP decoding. Furthermore, except double transitivity, they satisfy all the code properties used by Reeves and Pfister to show that RM codes achieve the capacity of binary-input memoryless symmetric channels. Finally, when n is odd, we identify a large class of abelian codes that includes Bn(r,m) and Cn(r,m) and which achieves BEC capacity.

Abstract

Environmental flow (Eflow) allocations of reservoir-river systems do not adequately address the connections between reservoir inflows, releases, and corresponding downstream river water quality rendering water resources management difficult. The study integrated the Soil and Water Assessment Tool (SWAT) for reservoir inflows estimation and corresponding releases with Global Environmental Flow Calculator (GEFC) for Eflows allocations to simulate the resulting Dissolved Oxygen (DO) of the downstream river stretch using QUAL2K. The study considered various plausible scenarios of inflows (10 to 20% reduction), pollution scenarios of Biological Oxygen Demand (BOD) (0 to 100% treatment) for arriving drains by altering headwaters DO (4 to 8 mg/l). Eflow water quality charts were developed for the regulation of reservoir downstream river water quality for Bhadra river in India as case study. The study revealed that by maintaining the headwater DO in conjunction with BOD treatment of drains and with sufficient Eflow allocation, downstream river water quality can be improved. By maintaining a headwater DO of 7 mg/l with BOD treatment of drains of 25% and by providing Eflow allocation of 40.12 m3/s with class D, the Bhadravathi river stretch has shown an improved river water quality with about 6.65 mg/l of average DO.

Abstract

This paper presents a simple and effective framework to combine various data-driven machine learning (ML) algorithms for short-range reservoir inflow forecasting, including the large-scale climate phenomenon indices addressing forecasting uncertainty. Random Forest (RF), Gradient Boosting Regressor (GBR), K-Nearest Neighbors Regressor (KNN), and Long Short-Term Memory (LSTM) were employed for predicting daily reservoir inflows considering various climate phenomenon indices (e.g., Arctic Oscillation, North Atlantic Oscillation, and Southern Oscillation Index) and hydroclimatic variables (e.g., precipitation), accounting for time-lag effects. After training the individual ML algorithm, a framework was developed to create an ensemble model using a robust weighted voting regressor (VR) method to quantify forecasting uncertainty and to improve model performances. The results of the study reveal that, for 2-day forecasts, the LSTM approach has the greatest influence on prediction accuracy, followed comparably by each model. However, none of the four models seem to be noticeably superior to the VR method, regardless of the prediction lead time. The developed framework was examined on a tropical reservoir, Bhadra reservoir Tunga-Bhadra River, located in India

Abstract

Background: Association of visceral adipose tissue (VAT) with high molecular weight (HMW) adiponectin and insulin resistance (IR) is known, whereas impact of subcutaneous adipose tissue (SCAT) is unclear. Our objective was to find out association of VAT and SCAT-derived HMW adiponectin with serum HMW adiponectin and explore their impact on HOMA-IR and type 2 diabetes (T2DM). Methods: Among 51 subjects (Age-30 to 57 years), 33 subjects (women= 20, men= 13) without T2DM and 18 subjects (women= 11, men= 7) with new onset diabetes (less than 1 year) were categorized as control and T2DM respectively according to American Diabetic Association (ADA) guideline, 2010. Adipocytes were isolated and cultured from adipose tissue biopsies obtained from these subjects during their abdominal surgery. Adiponectin mRNA expression was analysed by Real-Time PCR, and HMW adiponectin …

Abstract

A face morph is created by strategically combining two or more face images corresponding to multiple identities. The intention is for the morphed image to match with multiple identities. Current morph attack detection strategies can detect morphs but cannot recover the images or identities used in creating them. The task of deducing the individual face images from a morphed face image is known as de-morphing. Existing work in de-morphing assume the availability of a reference image pertaining to one identity in order to recover the image of the accomplice - i.e., the other identity. In this work, we propose a novel demorphing method that can recover images of both identities simultaneously from a single morphed face image without needing a reference image or prior information about the morphing process. We propose a generative adversarial network that achieves single image-based de-morphing with a surprisingly high degree of visual realism and biometric similarity with the original face images. We demonstrate the performance of our method on landmark-based morphs and generative model-based morphs with promising results.