DESIGN OF TWO STAGE BULK-DRIVEN OPERATIONAL TRANSCONDUCTANCE AMPLIFIER (OTA) WITH A HIGH GAIN FOR LOW VOLTAGE APPLICATION

An Operational Transconductance Amplifier (further abbreviated as OTA) is a voltage controlled current source used to produce an output current proportional to the input voltage. A schematic architecture for a 180nm OTA is presented in this thesis with the goal of improving the open-loop gain for a 0.9V supply voltage with a rail-to-rail bulk-driven input stage. Results show an open loop gain 97.14 dB with a power consumption of 3.33uW. An OTA with over 90 dB open loop gain and lower power consumption is highly suitable for low-voltage applications. The slew rate of the OTA is 0.05V/uS with a unity-gain bandwidth of 8.4MHz. A 10uA ideal bias current reference is utilized for the design. The phase margin is around 49.2 degrees. The threshold voltage for a 180nm N-channel Metal Oxide Semiconductor (also known as NMOS) device is around 400mV which restricts the low voltage applications in most amplifier circuits. The fourth terminal (bulk) of the MOS device is utilized to optimize the voltage headroom (Vds). The bulk terminal uses a much lesser source to drain voltage than the gate-driven transistors, and the transistors remain ON with an input voltage as low as 0.1V. A bulk-driven input stage ensures the amplification in the subthreshold region (input signal less than the threshold voltage of the MOS device). However, even with the bulk input MOS device, a rail-to-rail input stage is employed to improve the dynamic range for the input signal from 0V to 0.9V with a supply voltage of 0.9V. The fluctuation in open loop gain concerning the change in input signal in the published research is because of the constant instability in the intrinsic transconductance of the input devices. A possible solution is presented in this thesis by adding a second dominant pole to the circuit (i.e., second stage for the OTA), which reduces the dependency of intrinsic transconductance (bulk-driven device) on the total open loop gain of the amplifier. Thus, a significant gain of 97.14 dB with minimal fluctuations is achieved. Furthermore, adding a second stage improves the gain by distributing the dependency of the gain due to the first stage to both poles in the circuit. Hence, the problem of fluctuating transconductance of the input stage is resolved by the constant intrinsic transconductance of the MOS near the second pole (M19). To improve the gain, a folded cascoded amplifier connected with the input stage results in a better impedance (in the first stage) known as the gain stage. In the second stage, a large PMOS common source amplifier gives a good output current compared to the input stage to enhance the output swing and drive a purely capacitive load of 0.5pF. Furthermore, a miller capacitance is used to compensate for the frequency between the first and the second stage and improving the unity-gain bandwidth. An additional biasing circuit in the second stage amplifies the current output of the first stage and thus improving the slew rate of the entire device. In addition, the biasing circuit resolves the biasing issues for the second-stage common-source amplifier. It improves the output swing of the device to obtain a clean/undistorted output waveform. All the simulations are carried out in the LTSpice simulation tool to test the waveforms and bode plot for open loop gain and phase margin (49.2 degrees) at different processes (slow, typical, and fast), input voltages (0-0.9V), supply voltage (0.8V, 0.9V, 1.0V) and temperatures (-10 to 100 degree C)

Neural Character-based Composition Models for Abuse Detection

The advent of social media in recent years has fed into some highly undesirable phenomena such as proliferation of offensive language, hate speech, sexist remarks, etc. on the Internet. In light of this, there have been several efforts to automate the detection and moderation of such abusive content. However, deliberate obfuscation of words by users to evade detection poses a serious challenge to the effectiveness of these efforts. The current state of the art approaches to abusive language detection, based on recurrent neural networks, do not explicitly address this problem and resort to a generic OOV (out of vocabulary) embedding for unseen words. However, in using a single embedding for all unseen words we lose the ability to distinguish between obfuscated and non-obfuscated or rare words. In this paper, we address this problem by designing a model that can compose embeddings for unseen words. We experimentally demonstrate that our approach significantly advances the current state of the art in abuse detection on datasets from two different domains, namely Twitter and Wikipedia talk page.Comment: In Proceedings of the EMNLP Workshop on Abusive Language Online 201

Scientific and Creative Analogies in Pretrained Language Models

This paper examines the encoding of analogy in large-scale pretrained language models, such as BERT and GPT-2. Existing analogy datasets typically focus on a limited set of analogical relations, with a high similarity of the two domains between which the analogy holds. As a more realistic setup, we introduce the Scientific and Creative Analogy dataset (SCAN), a novel analogy dataset containing systematic mappings of multiple attributes and relational structures across dissimilar domains. Using this dataset, we test the analogical reasoning capabilities of several widely-used pretrained language models (LMs). We find that state-of-the-art LMs achieve low performance on these complex analogy tasks, highlighting the challenges still posed by analogy understanding.Comment: To be published in Findings of EMNLP 202

Investigating the Robustness of Sequential Recommender Systems Against Training Data Perturbations: an Empirical Study

Sequential Recommender Systems (SRSs) have been widely used to model user behavior over time, but their robustness in the face of perturbations to training data is a critical issue. In this paper, we conduct an empirical study to investigate the effects of removing items at different positions within a temporally ordered sequence. We evaluate two different SRS models on multiple datasets, measuring their performance using Normalized Discounted Cumulative Gain (NDCG) and Rank Sensitivity List metrics. Our results demonstrate that removing items at the end of the sequence significantly impacts performance, with NDCG decreasing up to 60\%, while removing items from the beginning or middle has no significant effect. These findings highlight the importance of considering the position of the perturbed items in the training data and shall inform the design of more robust SRSs

The impact of therapy on the quality of life in asymptomatic patients with freshly detected hypertension

Background: Hypertension is an commonly encountered disease which adversely affect  all aspects of quality of life (QoL). The existing studies are confounded by the presence of multiple comorbidities and inclusion of elderly, which by themselves impairs the QoL. There is thus a need to study the impact of hypertension on QoL, in isolation.Method: This is a single center, prospective, intention to treat, observation study. The aim of the study is to evaluate the change in the QoL over six months, in newly diagnosed asymptomatic patients of hypertension. The tools used to assess the QoL included World Health Organisation’s Quality of Life Questionnaire (WHOQOL- BREF) and Short Form-36 (SF-36).Result: A total of 232 patients (172 males and 60 females) were enrolled in the study. The mean age was 44.66 years. A total of 102 patients (43.97%) had stage-1 and 130 patients (56.03%) had stage-2 hypertension. The female gender is associated with a higher likelihood of presentation with stage-2 hypertension. The male cohort had a better baseline QoL. The desired blood pressures was achieved in 40.52%. With therapy, the QoL improved significantly; sub-hoc analysis showed, the improvement was higher in males and those with stage-1 hypertension. There is an inverse relationship between the QoL and requirement for higher number of antihypertensive mediations.Conclusions: In patients with asymptomatic primary hypertension, treatment improves all aspects of QoL. The factors adversely affecting the QoL include female gender, higher stage of hypertension, poor blood pressure control and requirement of higher numbers of antihypertensive medicine