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.

Abstract

The focus of this paper is (i) to derive a suitable dynamic model for a self-reconfigurable pavement sweeping robot PANTHERA and (ii) to design a robust controller for the same to tackle uncertainties stemming from the reconfiguration process, external disturbances and from actuator saturation. To meet the first objective, an Euler-Lagrangian dynamic model is proposed to incorporate the effects of configuration changes on the system dynamics. Based on this model, the second objective is met via designing a singular perturbation based robust controller which can tackle the aforementioned uncertainties without violating the actuation limits. To circumvent the vulnerability toward actuator saturation, the proposed controller is built on contraction theory, which, compared to a conventional Lyapunov theory based design, allows to improve closed-loop tracking performance without reducing the singular perturbation parameter. Experimental results on the PANTHERA reconfigurable robot validate the effectiveness of the proposed controller over the state of the art.

Abstract

Very small electromechanical coupling coefficient in micro-electromechanical systems (MEMS) or acoustic resonators is quite a concern for oscillator performance, specially at mmWave frequencies. This small coefficient is the manifestation of the small ratio of motional capacitance to static capacitance in the resonators. This work provides a general solution to overcome the problem of relatively high static capacitance at mmWave frequencies and presents analysis and design techniques for achieving extremely low phase noise and a very high figureof-merit (FoM) in an on-chip MEMS resonator based mmWave oscillator. The proposed analysis and techniques are validated with design and simulation of a 30 GHz oscillator with MEMS resonator having quality factor of 10,000 in 14 nm GF technology. Post layout simulation results show that it achieves a phase noise of −132 dBc/Hz and FoM of 217 dBc/Hz at offset of 1 MHz.

Abstract

Around 40% of the world’s population is currently without access to the Internet. The digital divide is due to the high cost of provisioning these services and the low return on investment for network operators. We propose using 5G network slicing with multi-tenancy (also known as neutral host networks (NHN)) for macro-cells and small cells in rural areas to reduce the costs. This paper investigates the techno-economic feasibility of using rural 5G NHN to minimise the digital divide. A generic model is developed to analyse the techno-economic analysis of 5G NHN deployment around the world, with a special focus on rural areas where no MNO is interested in providing services. To understand the application, it is applied to the Indian scenario. First, a discussion on existing infrastructure, competition and statistics for Indian telecommunications is presented. Next, the technical requirements are analysed using the key performance indicators (KPI) required for the rural 5G NHN for the Indian scenario. The study also analyses the relationship between coverage, investment in the network, the number of subscribers, investment time, demand, the investment per user and sensitivity analysis to understand the feasibility of the proposed solution for Indian villages with different input conditions. Later, a case study is carried out based on the proposed approach, along with coverage modelling for a few Indian villages having different topologies. The results show that 5G NHN using network slicing can significantly reduce the total investment required for providing 5G services in rural areas. Furthermore, the study shows that rural 5G NHN is a viable investment and a key enabler for Internet connectivity for villages with 10-year investment, having a subscribers’ base as low as 100 with a customer growth rate of 7%.

Abstract

Neural models excel at extracting statistical patterns from large amounts of data, but struggle to learn patterns or reason about language from only a few examples. In this paper, we ask: Can we learn explicit rules that generalize well from only a few examples? We explore this question using program synthesis. We develop a synthesis model to learn phonology rules as programs in a domain-specific language. We test the ability of our models to generalize from few training examples using our new dataset of problems from the Linguistics Olympiad, a challenging set of tasks that require strong linguistic reasoning ability. In addition to being highly sample-efficient, our approach generates human-readable programs, and allows control over the generalizability of the learnt programs.

Abstract

Data is expanding at an unimaginable rate, and with this development comes the responsibility of the quality of data. Data Quality refers to the relevance of the information present and helps in various operations like decision making and planning in a particular organization. Mostly data quality is measured on an ad-hoc basis, and hence none of the developed concepts provide any practical application.