The uGMRT Observations of Three New Gigahertz-peaked Spectra Pulsars

Using the Giant Metrewave Radio Telescope, we report the detailed spectral measurements over a wide frequency range of three pulsars (J1741−3016, J1757−2223, and J1845−0743), which allow us to identify them as new gigahertz-peaked spectra pulsars. Our results indicate that their spectra show turnovers at the frequencies of 620 MHz, 640 MHz, and 650 MHz, respectively. Our analysis proves that wideband observations improve estimations of spectral nature using a free–free thermal absorption model, and thus allow for a more accurate approximation of the maximum energy in the spectrum. While there is no evidence as yet that these objects are associated with a supernova remnant or pulsar wind nebula, they will make good targets when looking for interesting environments in the future, or when conducting more sensitive sky surveys.

Ureteral injury during laparoscopic rectal resection and concurrent laparoscopic repair by uretero-ureterostomy

Iatrogenic ureteral injury is an uncommon but severe complication of laparoscopic colorectal surgery. If it is detected intraoperatively, conversion to open surgery is usually inevitable. Here, we described a complete ureteral transection during laparoscopic low anterior resection, which was simultaneously repaired by laparoscopic uretero-ureterostomy. The most important points during the anastomosis of two tiny tubular tissues are dissecting the tubular organs without trauma, obtaining meticulous hemostasis without causing any necrosis, and achieving accurate approximation of tissues with the sutures. To the best of our knowledge, this is the first report that focused on laparoscopic repair of ureteral injury during laparoscopic colorectal surgery. As there are still few data on laparoscopic repair of ureteral lesions, no firm conclusions can be drawn. But, in appropriate cases, if intracorporeal suture expertise is available, laparoscopic repair can be done during colorectal surgery.

Clustering in massive neutrino cosmologies via Eulerian Perturbation Theory

We introduce an Eulerian Perturbation Theory to study the clustering of tracers for cosmologies in the presence of massive neutrinos. Our approach is based on mapping recently-obtained Lagrangian Perturbation Theory results to the Eulerian framework. We add Effective Field Theory counterterms, IR-resummations and a biasing scheme to compute the one-loop redshift-space power spectrum. To assess our predictions, we compare the power spectrum multipoles against synthetic halo catalogues from the QUIJOTE simulations, finding excellent agreement on scales k ≲ 0.25 h Mpc-1. One can obtain the same fitting accuracy using higher wave-numbers, but then the theory fails to give a correct estimation of the linear bias parameter. We further discuss the implications for the tree-level bispectrum. Finally, calculating loop corrections is computationally costly, hence we derive an accurate approximation wherein we retain only the main features of the kernels, as produced by changes to the growth rate. As a result, we show how FFTLog methods can be used to further accelerate the loop computations with these reduced kernels.

Phonetic contouring of polished surface of denture base for speech enhancement

The three main factors in complete denture construction are mechanics, esthetics, and phonetics. Out of which phonetics is generally given least importance as the tongue tends to adapt and patients tend to return to normal speech after a post insertion practice period of several weeks to several months. This may not be acceptable to certain group of people in accordance with their needs to speak socially or vocationally. An accurate approximation of palatal contours of a maxillary complete denture to patient’s tongue can improve intelligibility and decrease the post- insertion practice period. This article outlines a technique to replicate the patient’s palatal anatomy onto the polished palatal surface of a complete denture.

Deep Bucket Elimination

Bucket Elimination (BE) is a universal inference scheme that can solve most tasks over probabilistic and deterministic graphical models exactly. However, it often requires exponentially high levels of memory (in the induced-width) preventing its execution. In the spirit of exploiting Deep Learning for inference tasks, in this paper, we will use neural networks to approximate BE. The resulting Deep Bucket Elimination (DBE) algorithm is developed for computing the partition function. We provide a proof-of-concept empirically using instances from several different benchmarks, showing that DBE can be a more accurate approximation than current state-of-the-art approaches for approximating BE (e.g. the mini-bucket schemes), especially when problems are sufficiently hard.

Deep learning-based approximation of Goldbach partition function

Goldbach’s conjecture is one of the oldest and famous unproved problems in number theory. Using a deep learning model, we obtain an approximation of the Goldbach partition function, which counts the number of ways of representing an even number greater than 4 as a sum of two primes. We use residues of number modulo first 25 primes as features and archive a more accurate approximation, which reduces error rate from [Formula: see text] to [Formula: see text].