Increasing Average Period Lengths by Switching of Robust Chaos Maps in Finite Precision

Grebogi, Ott and Yorke (Phys. Rev. A 38(7), 1988) have investigated the effect of finite precision on average period length of chaotic maps. They showed that the average length of periodic orbits ($T$) of a dynamical system scales as a function of computer precision ($\epsilon$) and the correlation dimension ($d$) of the chaotic attractor: $T \sim \epsilon^{-d/2}$. In this work, we are concerned with increasing the average period length which is desirable for chaotic cryptography applications. Our experiments reveal that random and chaotic switching of deterministic chaotic dynamical systems yield higher average length of periodic orbits as compared to simple sequential switching or absence of switching. To illustrate the application of switching, a novel generalization of the Logistic map that exhibits Robust Chaos (absence of attracting periodic orbits) is first introduced. We then propose a pseudo-random number generator based on chaotic switching between Robust Chaos maps which is found to successfully pass stringent statistical tests of randomness.Comment: 10 pages, 5 figures, 2 tables. This paper will be published in European Physical Journal Special Topics, Vol. 165, pp. 73--83, 2008. (Related work was presented at the International conference on Nonlinear Dynamics and Chaos: Advances and Perspectives, September 17-21, 2007, Aberdeen.

Functional outcome of mild and moderate residual varus in posterior stabilized total knee arthroplasty in primary osteoarthritis knee: a prospective study

Background: Total knee arthroplasty (TKA) is one of the most commonly done orthopaedic surgical procedures for treating severe arthritis of the knee joint caused by osteoarthritis or inflammatory arthritis. The current clinical investigation, done at the Sanjay Gandhi Institute of Trauma and Orthopaedics in Bengaluru, provided the short-term functional result of mild and moderate residual varus in posterior stabilized TKA. The aim was to evaluate the efficacy of mild and moderate residual varus in total knee replacement for primary OA knee in terms of pain relief, range of motion and stability of the joint. Methods: 30 total knee replacements were performed. All patients were examined pre- and post-operatively using the knee society clinical and functional score. The average pre-op KSS knee score was 38.7, with a functional score of 23.3. The most common reason for TKR was osteoarthritis. The follow-up time ranged from 6 to 12 months.Results: By the knee society clinical, functional score method, 96.6% of our patients received an outstanding assessment after scoring 80 points or higher. The mean post-operative KSS knee score is 86.57, and the knee society functional score is 92. 92% of patients had little/no pain after surgery, and walking ability increased and was unlimited in 80% of patients.Conclusions: After a short term follow up of 1 year in a research population of 30 with pre-operative osteo arthritis of the knee, with post-operative mild to moderate varus alignment showed better clinical results

Discrete Audio Representation as an Alternative to Mel-Spectrograms for Speaker and Speech Recognition

Discrete audio representation, aka audio tokenization, has seen renewed interest driven by its potential to facilitate the application of text language modeling approaches in audio domain. To this end, various compression and representation-learning based tokenization schemes have been proposed. However, there is limited investigation into the performance of compression-based audio tokens compared to well-established mel-spectrogram features across various speaker and speech related tasks. In this paper, we evaluate compression based audio tokens on three tasks: Speaker Verification, Diarization and (Multi-lingual) Speech Recognition. Our findings indicate that (i) the models trained on audio tokens perform competitively, on average within $1\%$ of mel-spectrogram features for all the tasks considered, and do not surpass them yet. (ii) these models exhibit robustness for out-of-domain narrowband data, particularly in speaker tasks. (iii) audio tokens allow for compression to 20x compared to mel-spectrogram features with minimal loss of performance in speech and speaker related tasks, which is crucial for low bit-rate applications, and (iv) the examined Residual Vector Quantization (RVQ) based audio tokenizer exhibits a low-pass frequency response characteristic, offering a plausible explanation for the observed results, and providing insight for future tokenizer designs.Comment: Preprint. Submitted to ICASSP 202

FORMULATION AND EVALUATION OF SOLID SELF MICRO EMULSIFYING DISPERSIBLE TABLET OF PIROXICAM

Objective: The aim of this study was to formulate the solid self-micro emulsifying dispersible tablets for promoting the dissolution of Piroxicam. Methods: Solubility study test was performed to know the solubility of various oil phase, surfactants, cosurfactants. Self-emulsifying grading test was done by visual grading system. Ternary phase diagrams and droplet size analysis test were performed to screen and optimize the Piroxicam-self microemulsifying drug delivery system (SMEDS). Then microcrystalline cellulose (KG802) was added as a suitable adsorbent and dispersible tablet were prepared by wet granulation compression method. Results: The final composition of Piroxicam-SMEDS was oil phase (oleic acid, 23%), surfactant (Cremophor R H-40,61%), co-surfactant (PEG-400,16%) based on the result of solubility test, self-emulsifying grading test, droplet size analysis and ternary phase diagrams. Microcrystalline cellulose (KG802) was selected based on dissolution study (98.35%) and added to liquid Piroxicam-Smeds formulation to form dispersible tablets. The in vitro dissolution study showed 98.02 % of drug release from Piroxicam-SMEDS tablets. Conclusion: Piroxicam–Self microemulsifying dispersible tablets have increased the solubility and bioavailability of the Piroxicam to a greater extent. SMEDS formulation can help the solubility of poorly water-soluble drugs

The CHiME-7 Challenge: System Description and Performance of NeMo Team's DASR System

We present the NVIDIA NeMo team's multi-channel speech recognition system for the 7th CHiME Challenge Distant Automatic Speech Recognition (DASR) Task, focusing on the development of a multi-channel, multi-speaker speech recognition system tailored to transcribe speech from distributed microphones and microphone arrays. The system predominantly comprises of the following integral modules: the Speaker Diarization Module, Multi-channel Audio Front-End Processing Module, and the ASR Module. These components collectively establish a cascading system, meticulously processing multi-channel and multi-speaker audio input. Moreover, this paper highlights the comprehensive optimization process that significantly enhanced our system's performance. Our team's submission is largely based on NeMo toolkits and will be publicly available