Efficient Implementation of the Room Simulator for Training Deep Neural Network Acoustic Models

In this paper, we describe how to efficiently implement an acoustic room simulator to generate large-scale simulated data for training deep neural networks. Even though Google Room Simulator in [1] was shown to be quite effective in reducing the Word Error Rates (WERs) for far-field applications by generating simulated far-field training sets, it requires a very large number of Fast Fourier Transforms (FFTs) of large size. Room Simulator in [1] used approximately 80 percent of Central Processing Unit (CPU) usage in our CPU + Graphics Processing Unit (GPU) training architecture [2]. In this work, we implement an efficient OverLap Addition (OLA) based filtering using the open-source FFTW3 library. Further, we investigate the effects of the Room Impulse Response (RIR) lengths. Experimentally, we conclude that we can cut the tail portions of RIRs whose power is less than 20 dB below the maximum power without sacrificing the speech recognition accuracy. However, we observe that cutting RIR tail more than this threshold harms the speech recognition accuracy for rerecorded test sets. Using these approaches, we were able to reduce CPU usage for the room simulator portion down to 9.69 percent in CPU/GPU training architecture. Profiling result shows that we obtain 22.4 times speed-up on a single machine and 37.3 times speed up on Google's distributed training infrastructure.Comment: Published at INTERSPEECH 2018. (https://www.isca-speech.org/archive/Interspeech_2018/abstracts/2566.html

Embedding indices and bloom filters in parquet files for fast Apache arrow retrievals

Apache Parquet is a column major table file format developed for the Hadoop ecosystem, with support for data compression. Hadoop SQL engines process queries like relational databases but read the parquet file to retrieve data. The caveat is that reading takes time and needs to be optimized. Irrelevant to a query I/O must be avoided for faster reads. The file is organized in rows segmented serially per column, which are segmented serially into DataPages. Two indices were proposed, namely, ColumnIndex (storing DataPage minimum and maximum values) and OffsetIndex (storing DataPage offsets), which support reading only the required DataPages in retrieving a row, skipping irrelevant DataPages. In this thesis, we investigate methods to accelerate row retrieval in parquet files within Apache Arrow, which is an in-memory big data analytics library that supports fast data processing applications on modern hardware. Towards this, we first implement the proposed ColumnIndex and OffsetIndex. We then propose and integrate the indices with Split Block Bloom Filters (SBBF). Our hypothesis is that a combination of the indices and SBBF should enhance the overall performance by avoiding unnecessary I/O in queries with predicate values not present in the parquet file. We validate our hypothesis through extensive experimentation. Our experiments show that using either indices or SBBF reduces average reading time by 20x. Their combination reduces the average reading time by an additional 10%. Adding indices does not significantly increase the parquet file size, but adding SBBF approximately increases the parquet file size by 2x. We contribute our code to Apache Arrow open source project along with a conceptual design for DataPage level SBBF for further read optimization

A Unifying Approach to Decide Relations for Timed Automata and their Game Characterization

In this paper we present a unifying approach for deciding various bisimulations, simulation equivalences and preorders between two timed automata states. We propose a zone based method for deciding these relations in which we eliminate an explicit product construction of the region graphs or the zone graphs as in the classical methods. Our method is also generic and can be used to decide several timed relations. We also present a game characterization for these timed relations and show that the game hierarchy reflects the hierarchy of the timed relations. One can obtain an infinite game hierarchy and thus the game characterization further indicates the possibility of defining new timed relations which have not been studied yet. The game characterization also helps us to come up with a formula which encodes the separation between two states that are not timed bisimilar. Such distinguishing formulae can also be generated for many relations other than timed bisimilarity.Comment: In Proceedings EXPRESS/SOS 2013, arXiv:1307.690

Infant with a foreign body bronchus: A fishy situation!

Foreign body aspiration (FBA) is a common cause of respiratory compromise in early childhood. Numerous unique foreign bodies in the tracheobronchial tree have been reported in the literature. FBA can result in a spectrum of presentations ranging from incidental to acutely life threatening. Described here is a case of inhalation of a live fish caught from a household aquarium by a 10-month-old infant. The infant presented to us with worsening respiratory distress, and an emergency diagnostic rigid bronchoscopy retrieved the fish and the baby survived. This instance highlights the importance of actively investigating pediatric patients with  bronchoscopy when suspicion of FBA is high. This case report of a live fish aspiration in an infant that was successfully removed is a first of its kind.Keywords: bronchoscopy, foreign body aspiration, respiratory distres

Comparative study of efficiency of various oral hygiene measures on halitosis and its causative organisms in fixed appliance therapy patients

INTRODUCTION: Healthy human breath exhibits a slightly sweet, generally not discernible smell under normal circumstances. Factors such as time of day, salivation, oral flora, and food intake or dental hygiene can change breath intensity. Halitosis denotes the offensive smell of breath. Synonyms for bad breath are foetor ex ore, oral malodor or offensive breath. Halitosis combines various pathologies. A distinction is made between real halitosis (distinctive bad breath, intensity clearly above socially accepted degrees), which can be physiological or pathological, pseudo-halitosis (bad breath not discernible by others, improved situation after patient information) and halitophobia (bad breath not discernible by others, no improvement of situation after patient information). Various studies have investigated the influence of orthodontic appliances on the level of bacteria in the oral cavity. The side effects described include decalcification, white spots, cavities. Fixed orthodontic appliances favour the accumulation of plaque, therefore increasing the risk of halitosis during treatment. The design and surface structure of the orthodontic appliance, as well as the composite, influence plaque retention. The manner of mounting the orthodontic wire on the brackets also plays a role. Rationale Though studies have proved the effect of fixed appliance on halitosis, little has been done to establish the most economic , convenient and effective method of oral hygiene method and adjuvant in orthodontic patients undergoing fixed appliance therapy. AIM OF THE STUDY: The aim of this randomized clinical trial is to • To evaluate efficiency of four oral hygiene measures (OHM )in controlling halitosis during fixed appliance therapy, i) brushing and placebo ii)brushing and normal saline iii) brushing and oil pulling iv) brushing and chlorex mouthwash OBJECTIVES: 1. To find subjective measurement of improvement in odour after OHM. 2. To evaluate the microbacterial load as an indicator of effectiveness of OHM. Sample Size: Total of 40 samples. Groups: Group I (N= 10) – “brushing and placebo”, Group 2 (N=10 ) - “brushing and normal saline", Group 3 (N =10 ) – “brushing and oil pulling”, Group 4 (N =10) – “brushing and chlorex mouthwash”. INCLUSION CRITERIA: 1. Class I malocclusion. EXCLUSION CRITERIA: 1. History of antibiotic use within the past 3 months ; 2. History of otolaryngology consultation due to sinusitis, tonsillitis or tonsilloliths within the past 3 months ; 3. Use of a gargling solution on the day of screening ; 4. Periodontitis ; 5. Organoleptic score (ORS) of 0 ; 6. CH3 SH in mouth air < 26 ppb. MATERIALS AND METHODS: 1. Ortho brush (STIM), 2. COLGATE Regular toothpaste, 3. Hexidine oral rinse (chlorhexidine 0.2% w/v), 4. Normal Saline, 5. Sesame oil for oil pulling, 6. Orthox brackets 0.022’ size, 7. Niti wires, 8. Robertsonson cooked –meat broth medium, 9. Thyoglycolate broth, 10. Blood agar. METHODOLOGY: • Patients with common baseline feature of moderate crowding based on sevearity score will be choosen . • They will be divided randomly into 4 study groups. • Subjects will be studied at T1 , T2, T3 ( ALLIGNMENT PHASE). T1 – Immediately after giving FA. T 2 – 10 days after FA. T3 – 20 days after FA. • At T1,T2,T3 their organoleptic score will be recorded by modified organoleptic method. • This will be further substantiated by TANITA breath analyser score. • Plaque sample will be collected from the subgingival area at T1, T2, T3. Subgingival plaque samples were collected from four sites (16 mesiobuccal, 26 mesio buccal, 36 mesio buccal and 46 mesio buccal) by gracey currette. • They will be provided with a standard toothbrush and toothpaste to be used. DATA COLLECTION: Organoleptic method – KIM method. Scale of 0 to 4 (De Boever & Loesche) - 0 – no appreciable odor. 1. Barely noticeable of low intensity and acceptable limits. 2. Slight to moderate odor clearly noticeable and slightly unpleasant. 3. Moderate to high, clearly noticeable, unpleasant and moderately intense. 4. Offensive odor of strong intensity. RESULTS: The results show that all the oral hygiene measures were effective in reducing the anerobic bacterial load and hence improve the gingival health and reduce halitosis. Oil pulling was more efficient in improving the gingival health and reducing halitosis compared to chlorhexidine. Normal saline was also effective but was less efficient than oil pulling and chlorhexidine. CONCLUSION: Oil pulling and normal saline are efficient in reducing halitosis in fixed orthodontic patients

DEVELOPMENT OF A TELEREHABILITATION SOLUTION FOR REMOTE MONITORING AND CARE

Ph.DDOCTOR OF PHILOSOPH

Large-scale Package Deliveries with Unmanned Aerial Vehicles using Collective Learning

Unmanned aerial vehicles (UAVs) have significant practical advantages for delivering packages, and many logistics companies have begun deploying UAVs for commercial package deliveries. To deliver packages quickly and cost-effectively, the routes taken by UAVs from depots to customers must be optimized. This route optimization problem, a type of capacitated vehicle routing problem, has recently attracted considerable research interest. However, few papers have dealt with large-scale deliveries, where the number of customers exceed 1000. We present an innovative, practical package delivery model wherein multiple UAVs deliver multiple packages to customers who are compensated for late deliveries. Further, we propose an innovative methodology that combines a new plan-generation algorithm with a collective-learning heuristic to quickly determine cost-effective paths of UAVs even for large-scale deliveries up to 10000 customers. Specialized settings are applied to a collective-learning heuristic, the Iterative Economic Planning and Optimized Selections (I-EPOS) in order to coordinate collective actions of the UAVs. To demonstrate our methodology, we applied our highly flexible approach to a depot in Heathrow Airport, London. We show that a coordinated approach, in which the UAVs collectively determine their flight paths, leads to lower operational costs than an uncoordinated approach. Further, the coordinated approach enables large-scale package deliveries

Enhancing Programming eTextbooks with ChatGPT Generated Counterfactual-Thinking-Inspired Questions

Digital textbooks have become an integral part of everyday learning tasks. In this work, we consider the use of digital textbooks for programming classes. Generally, students struggle with utilizing textbooks on programming to the maximum, with a possible reason being that the example programs provided as illustration of concepts in these textbooks don't offer sufficient interactivity for students, and thereby not sufficiently motivating to explore or understand these programming examples better. In our work, we explore the idea of enhancing the navigability of intelligent textbooks with the use of ``counterfactual'' questions, to make students think critically about these programs and enhance possible program comprehension. Inspired from previous works on nudging students on counter factual thinking, we present the possibility to enhance digital textbooks with questions generated using GPT.Comment: Paper Under Revie