Abstract

Experiences from past earthquake disasters clearly shows that the ground motion was responsible for majority of property and life loss. Among the collapsed structures during the 1964 Niigata earthquake, the 1995 Kobe earthquake, the 1999 Kocaeli (Izmit) earthquake, the 2001 Bhuj earthquake and the 2004 Sumatra earthquake, excessive damage had occurred to pile supported bridges, towers, chimneys, high rise structures, etc. In view of this there is a need to study the complex behaviour of soil-pile-structure interaction problems. However, clause 6.1.5 IS 1893-2016, says that soil-structure-interaction (SSI) is not needed for structures founded on rock at shallow depth, but not enough details are given for SSI.

Abstract

Learning linguistic generalizations from only a few examples is a challenging task. Recent work has shown that program synthesis – a method to learn rules from data in the form of programs in a domain-specific language – can be used to learn phonological rules in highly data-constrained settings. In this paper, we use the problem of phonological stress placement as a case to study how the design of the domain-specific language influences the generalization ability when using the same learning algorithm. We find that encoding the distinction between consonants and vowels results in much better performance, and providing syllable-level information further improves generalization. Program synthesis, thus, provides a way to investigate how access to explicit linguistic information influences what can be learnt from a small number of examples.

Abstract

In recent years, given the exponential increase in social media content also led to an increase in online hate speech. We need automatic hate speech detection methods due to the volume of data on the web. Various approaches have been proposed to address hate speech and offensive content on social media. This paper surveys how neural-based models have rapidly evolved to address hate speech on multilingual code-mixed data. We discuss the current state of the research in hate speech and offensive language detection on code-mixed Indian datasets.

Abstract

Modeling person interactions with their surroundings has proven to be effective for recognizing and localizing human actions in videos. While most recent works focus on learning short term interactions, in this work, we consider long-term person interactions and jointly localize actions of multiple actors over an entire video shot. We construct a graph with nodes that correspond to keyframe actor instances and connect them with two edge types. Spatial edges connect actors within a keyframe, and temporal edges connect multiple instances of the same actor over a video shot. We propose a Graph Neural Network that explicitly models spatial and temporal states for each person instance and learns to effectively combine information from both modalities to make predictions at the same time. We conduct experiments on the AVA dataset and show that our graph-based model provides consistent improvements over several video descriptors, achieving state-of-the-art performance without any fine-tuning.

Abstract

Vision-and-language navigation (VLN) aims to enable embodied agents to navigate in realistic environments us- ing natural language instructions. Given the scarcity of domain-specific training data and the high diversity of im- age and language inputs, the generalization of VLN agents to unseen environments remains challenging. Recent meth- ods explore pretraining to improve generalization, however, the use of generic image-caption datasets or existing small- scale VLN environments is suboptimal and results in limited improvements. In this work, we introduce BnB1, a large- scale and diverse in-domain VLN dataset. We first col- lect image-caption (IC) pairs from hundreds of thousands of listings from online rental marketplaces. Using IC pairs we next propose automatic strategies to generate millions of VLN path-instruction (PI) pairs. We further propose a shuffling loss that improves the learning of temporal or- der inside PI pairs. We use BnB to pretrain our Airbert2 model that can be adapted to discriminative and genera- tive settings and show that it outperforms state of the art for Room-to-Room (R2R) navigation and Remote Referring Ex- pression (REVERIE) benchmarks. Moreover, our in-domain pretraining significantly increases performance on a chal- lenging few-shot VLN evaluation, where we train the model only on VLN instructions from a few houses

Abstract

The visual world naturally exhibits an imbalance in the number of object or scene instances resulting in a long-tailed distribution. This imbalance poses significant challenges for classification models based on deep learning. Oversampling instances of the tail classes attempts to solve this imbalance. However, the limited visual diver- sity results in a network with poor representation ability. A simple counter to this is decoupling the representation and classifier net- works and using oversampling only to train the classifier. In this paper, instead of repeatedly re-sampling the same im- age (and thereby features), we explore a direction that attempts to generate meaningful features by estimating the tail category’s distribution. Inspired by ideas from recent work on few-shot learn- ing [ 53], we create calibrated distributions to sample additional features that are subsequently used to train the classifier. Through several experiments on the CIFAR-100-LT (long-tail) dataset with varying imbalance factors and on mini-ImageNet-LT (long-tail), we show the efficacy of our approach and establish a new state-of- the-art. We also present a qualitative analysis of generated features using t-SNE visualizations and analyze the nearest neighbors used to calibrate the tail class distributions. Our code is available at https://github.com/rahulvigneswaran/TailCalibX.

Abstract

Facilitating the discovery of drugs by combining diverse compounds is becoming prevalent, especially for treating complex diseases like cancers and HIV. A drug is a chemi- cal compound structure and any sub-structure of a chemical compound is designated as a fragment. A chemical compound or a fragment can be modeled as a graph structure. Given a set of chemical compounds and their corresponding large set of fragments modeled as graph structures, we address the problem of identifying potential combinations of diverse chemical compounds, which cover a certain percentage of the set of fragments. In this regard, the key contributions of this work are three-fold: First, we introduce the notion of Graph Transactional Coverage Patterns (GTCPs) for any given graph transactional dataset. Second, we propose an efficient model and framework for extracting GTCPs from a given graph transactional dataset. Third, we conduct an extensive performance study using three real datasets to demonstrate that it is indeed feasible to efficiently extract GTCPs using our proposed GTCP-extraction framework. We also demonstrate the effectiveness of the GTCP-extraction framework through a case study in computer-aided drug design. Index Terms—Graph mining, Graph transactions, Coverage patterns, Drug discovery

Abstract

Most of the sensors that measure physical variables such as humidity, strain, motion, temperature, and pressure are designed based on the variation of capacitance or the resistance of a circuit entity. This paper presents a 12-bit combined resistance and capacitance to digital converter (RCDC) to analyze and process the information from these sensors. The read-out circuit first converts the capacitance or resistance to an analog voltage, followed by conversion to digital data using a successive approximation (SAR) type analog to digital converter (ADC). The circuit has been designed based on the 180 nm CMOS technology. The capacitance to digital converter (CDC) circuit covers a wide range of capacitance from 30 pF to 100 pF with a 17 fF resolution, whereas the resistance to digital converter (RDC) circuit covers a range of resistance from 1 Ω to 30 Ω and has a resolution of 7 mΩ. The 12-bit CDC and RDC have a measurement time of 20 μs and show a non-linearity of 0.66% and 0.57%, respectively. Index Terms—analog to digital converter; capacitance to dig- ital converter; resistance to digital converter; read-out circuit; successive approximation

Abstract

Detection of color change is an important tool used in several analytical chemistry processes in various industries such as biomedical, diagnostics, agriculture, food processing, and so on. In this study, we present an automated color detection system to perform qualitative analysis of liquid reagents. Many of the available techniques for colorimetric applications make use of expensive, high-resolution digital cameras. We have developed a simple low-cost system based on a light emitting diode (LED) and light-dependent resistor (LDR) pair to obtain information about the color of a liquid reagent. This system has been compared with a camera-based system for color detection. The LED-LDR system provides information about the color of an object by obtaining relative intensities of red, green, and blue (RGB) light reflected by the object in a sequential process. The system was tested by obtaining color information for different concentrations of potassium permanganate solution and by determining the endpoint of a titration experiment. This versatile system can be used for many other colorimetric measurements, for example, the amount of dissolved ammonia in water using Nesslers reagent (potassium tetraiodomercurate).

Abstract

We address a distributed adaptive synchronization problem for complex networks composed of nonlinear nodes under state-dependent a priori interconnections, i.e. interconnection terms acting before control design. The interconnection terms are uncertain and the heterogeneous dynamics of the network nodes further contain state-dependent uncertainty and uncertain input matrix gain. Adaptive distributed control laws are proposed to tackle such an unsolved design. The proposed controller is verified in simulation via a multi-area load frequency control for power systems.