A Generative Model For Zero Shot Learning Using Conditional Variational Autoencoders

Zero shot learning in Image Classification refers to the setting where images from some novel classes are absent in the training data but other information such as natural language descriptions or attribute vectors of the classes are available. This setting is important in the real world since one may not be able to obtain images of all the possible classes at training. While previous approaches have tried to model the relationship between the class attribute space and the image space via some kind of a transfer function in order to model the image space correspondingly to an unseen class, we take a different approach and try to generate the samples from the given attributes, using a conditional variational autoencoder, and use the generated samples for classification of the unseen classes. By extensive testing on four benchmark datasets, we show that our model outperforms the state of the art, particularly in the more realistic generalized setting, where the training classes can also appear at the test time along with the novel classes

Counting Basic-Irreducible Factors Mod p^k in Deterministic Poly-Time and p-Adic Applications

Finding an irreducible factor, of a polynomial f(x) modulo a prime p, is not known to be in deterministic polynomial time. Though there is such a classical algorithm that counts the number of irreducible factors of f mod p. We can ask the same question modulo prime-powers p^k. The irreducible factors of f mod p^k blow up exponentially in number; making it hard to describe them. Can we count those irreducible factors mod p^k that remain irreducible mod p? These are called basic-irreducible. A simple example is in f=x^2+px mod p^2; it has p many basic-irreducible factors. Also note that, x^2+p mod p^2 is irreducible but not basic-irreducible! We give an algorithm to count the number of basic-irreducible factors of f mod p^k in deterministic poly(deg(f),k log p)-time. This solves the open questions posed in (Cheng et al, ANTS\u2718 & Kopp et al, Math.Comp.\u2719). In particular, we are counting roots mod p^k; which gives the first deterministic poly-time algorithm to compute Igusa zeta function of f. Also, our algorithm efficiently partitions the set of all basic-irreducible factors (possibly exponential) into merely deg(f)-many disjoint sets, using a compact tree data structure and split ideals

Occupational Health and Safety in Healthcare Settings – Effect of Training on the Knowledge of Resident Doctors

Background: Over the past years several diseases and disorders associated with different kinds of occupations have been identified, including healthcare. It is important for medical personnel especially resident doctors to have adequate knowledge of occupational health and safety and prevention of occupational hazards.Objective: To assess the knowledge among resident doctors regarding occupational health and safety in healthcare settings, and the effect of training on the same.Methodology: A before and after intervention study without control was done among a group of resident doctors of a medical college in Delhi. Training on occupational health and safety was given to the study participants in the form of a two-day workshop. Pre- and post-test questionnaires filled by the participantswere scored and the mean scores were compared and tested for statistically significant difference using Mann Whitney U test.Results: The study was done on 17 post-graduate resident doctors. The mean post test score was higher than the mean pre-test score and the difference was statistically significant at p<0.05.Conclusion: Training of doctors and other health personnel on occupational health and safety and prevention and control of occupational hazards can prove to be effective in improving their knowledge regarding the same

Improving RNN-Transducers with Acoustic LookAhead

RNN-Transducers (RNN-Ts) have gained widespread acceptance as an end-to-end model for speech to text conversion because of their high accuracy and streaming capabilities. A typical RNN-T independently encodes the input audio and the text context, and combines the two encodings by a thin joint network. While this architecture provides SOTA streaming accuracy, it also makes the model vulnerable to strong LM biasing which manifests as multi-step hallucination of text without acoustic evidence. In this paper we propose LookAhead that makes text representations more acoustically grounded by looking ahead into the future within the audio input. This technique yields a significant 5%-20% relative reduction in word error rate on both in-domain and out-of-domain evaluation sets.Comment: 5 pages, 1 fig, 7 tables, Proceedings of Interspeech 202

Analysis of hybrid elliptical air hole ring As2Se3 glass PCF for Zero Dispersion

Volume 1 Issue 3 (May 2013