Quantum resetting in continuous measurement induced dynamics of a qubit

We study the evolution of a two-state system that is monitored continuously but with interactions with the detector tuned so as to avoid the Zeno affect. The system is allowed to interact with a sequence of prepared probes. The post-interaction probe states are measured and this leads to a stochastic evolution of the system's state vector, which can be described by a single angle variable. The system's effective evolution consists of a deterministic drift and a stochastic resetting to a fixed state at a rate that depends on the instantaneous state vector. The detector readout is a counting process. We obtain analytic results for the distribution of number of detector events and the time-evolution of the probability distribution. Earlier work on this model found transitions in the form of the steady state on increasing the measurement rate. Here we study transitions seen in the dynamics. As a spin-off we obtain, for a general stochastic resetting process with diffusion, drift and position dependent jump rates, an exact and general solution for the evolution of the probability distribution.Comment: 27 pages, 4 figure

Unveiling the Power of Self-Attention for Shipping Cost Prediction: The Rate Card Transformer

Amazon ships billions of packages to its customers annually within the United States. Shipping cost of these packages are used on the day of shipping (day 0) to estimate profitability of sales. Downstream systems utilize these days 0 profitability estimates to make financial decisions, such as pricing strategies and delisting loss-making products. However, obtaining accurate shipping cost estimates on day 0 is complex for reasons like delay in carrier invoicing or fixed cost components getting recorded at monthly cadence. Inaccurate shipping cost estimates can lead to bad decision, such as pricing items too low or high, or promoting the wrong product to the customers. Current solutions for estimating shipping costs on day 0 rely on tree-based models that require extensive manual engineering efforts. In this study, we propose a novel architecture called the Rate Card Transformer (RCT) that uses self-attention to encode all package shipping information such as package attributes, carrier information and route plan. Unlike other transformer-based tabular models, RCT has the ability to encode a variable list of one-to-many relations of a shipment, allowing it to capture more information about a shipment. For example, RCT can encode properties of all products in a package. Our results demonstrate that cost predictions made by the RCT have 28.82% less error compared to tree-based GBDT model. Moreover, the RCT outperforms the state-of-the-art transformer-based tabular model, FTTransformer, by 6.08%. We also illustrate that the RCT learns a generalized manifold of the rate card that can improve the performance of tree-based models

Determining systematic differences in human graders for machine learning-based automated hiring

Firms routinely utilize natural language processing combined with other machine learning (ML) tools to assess prospective employees through automated resume classification based on pre-codified skill databases. The rush to automation can however backfire by encoding unintentional bias against groups of candidates. We run two experiments with human evaluators from two different countries to determine how cultural differences may affect hiring decisions. We use hiring materials provided by an international skill testing firm which runs hiring assessments for Fortune 500 companies. The company conducts a video-based interview assessment using machine learning, which grades job applicants automatically based on verbal and visual cues. Our study has three objectives: to compare the automatic assessments of the video interviews to assessments of the same interviews by human graders in order to assess how they differ; to examine which characteristics of human graders may lead to systematic differences in their assessments; and to propose a method to correct human evaluations using automation. We find that systematic differences can exist across human graders and that some of these differences can be accounted for by an ML tool if measured at the time of training

Impact of health education in improving awareness about Methicillin resistant Staphylococcus aureus in health care professionals of tertiary healthcare centre in India

Background: Methicillin resistant Staphylococcus aureus (MRSA) is a major pathogen causing morbidity and mortality in hospital setup. Healthcare professionals (HCPs) colonized by MRSA, play a key role in transmission of this organism to the patients. Compliance of the HCPs with sanitary guidelines is fundamental to prevent nosocomial Infections. Hence, imparting education and creating awareness is the first step towards this. The aim of this study was to determine baseline knowledge about MRSA in healthcare professionals (HCPs). The further aim of the study was to assess the impact of health education on HCPs.Methods: A total of 104 participants, including 54 nurses and 50 doctors, were surveyed using pre-validated questionnaire, regarding MRSA colonization, modes of transmission, high risk areas in hospital, isolation policy, disinfection and treatment. The survey was followed by a health education session on MRSA. Thereafter a post-test questionnaire was administered to study the impact of the health education session.Results: The study sample of 104 respondents comprised of 50 doctors (48%) and 54 nurses (52%). It was found that baseline awareness regarding MRSA was lesser in the nursing staff as compared to doctors. Statistically significant positive impact of the health education session on all the HCPs was observed when paired t-test was applied. Various challenges expressed by the participants in prevention of MRSA transmission were noted.Conclusions: Due to suboptimal awareness noted in HCPs, educational programs should be conducted to bridge the gap in knowledge and perception of HCPs to prevent spread of MRSA.

NeRFiller: Completing Scenes via Generative 3D Inpainting

We propose NeRFiller, an approach that completes missing portions of a 3D capture via generative 3D inpainting using off-the-shelf 2D visual generative models. Often parts of a captured 3D scene or object are missing due to mesh reconstruction failures or a lack of observations (e.g., contact regions, such as the bottom of objects, or hard-to-reach areas). We approach this challenging 3D inpainting problem by leveraging a 2D inpainting diffusion model. We identify a surprising behavior of these models, where they generate more 3D consistent inpaints when images form a 2$\times$2 grid, and show how to generalize this behavior to more than four images. We then present an iterative framework to distill these inpainted regions into a single consistent 3D scene. In contrast to related works, we focus on completing scenes rather than deleting foreground objects, and our approach does not require tight 2D object masks or text. We compare our approach to relevant baselines adapted to our setting on a variety of scenes, where NeRFiller creates the most 3D consistent and plausible scene completions. Our project page is at https://ethanweber.me/nerfiller.Comment: Project page: https://ethanweber.me/nerfille

ASIC: Aligning Sparse in-the-wild Image Collections

We present a method for joint alignment of sparse in-the-wild image collections of an object category. Most prior works assume either ground-truth keypoint annotations or a large dataset of images of a single object category. However, neither of the above assumptions hold true for the long-tail of the objects present in the world. We present a self-supervised technique that directly optimizes on a sparse collection of images of a particular object/object category to obtain consistent dense correspondences across the collection. We use pairwise nearest neighbors obtained from deep features of a pre-trained vision transformer (ViT) model as noisy and sparse keypoint matches and make them dense and accurate matches by optimizing a neural network that jointly maps the image collection into a learned canonical grid. Experiments on CUB and SPair-71k benchmarks demonstrate that our method can produce globally consistent and higher quality correspondences across the image collection when compared to existing self-supervised methods. Code and other material will be made available at \url{https://kampta.github.io/asic}.Comment: Web: https://kampta.github.io/asi

Association of clinical features, comorbidities and laboratory profile with outcomes among dengue patients admitted in a tertiary care hospital, Delhi NCR

Background: Dengue fever is an endemic disease across multiple countries. Dengue infection results in a wide spectrum of non-specific clinical manifestations with unpredictable clinical course and outcome. Objective of the study was to understand the association of different clinical features, comorbidities and laboratory profile with outcomes (ICU use, ventilation use and blood transfusion) among dengue patients admitted in a tertiary care hospital in Delhi, National Capital Region.Methods This cross-sectional study included 75 dengue patients with fever <1 week confirmed based on NS-1 antigen and/or IgM antibody positivity. Descriptive analysis was used.Results: Gender was not significantly associated with the outcomes. The duration of fever was significantly higher among those with ICU use (median: 6 versus 4 days; p=0.005), ventilator use (median: 5.5 versus 4.0 days; p=0.049] and blood transfusion (median: 6 versus 4 days; p=0.013). Dengue patients with co-morbidities (diabetes, hypertension, or chronic obstructive pulmonary disease) or co-infection had a significantly higher odds of the outcomes. The platelet level was significantly lower while liver enzymes were significantly higher among those with the outcomes.Conclusions: The clinical features, comorbidities and laboratory profile can help in identifying critical patients for ICU admission and timely intervention to improve outcome

NIOSOMES:THE UNIQUE VESICULAR DRUG CARRIERS

Drug targeting is the ability to direct a therapeutic agent specifically to desired site of action with little or no interaction with nontarget tissue. Niosomes are one of the best carriers for drug targeting. Niosomes are self assembled vesicles composed primarily of synthetic surfactants and cholesterol. They are analogous in structure to the more widely studied liposomes formed from biologically derived phospholipids. Niosomes are biodegradable, relatively nontoxic, more stable and inexpensive, an alternative to liposomes. The method of preparation of niosome is based on liposome technology. The basic process of preparation is the same i.e. hydration by aqueous phase of the lipid phase which may be either a pure surfactant or a mixture of surfactant with cholesterol. After preparing niosomal dispersion, unentrapped drug is separated by dialysis centrifugation or gel filtration. Niosomes can be SUV (Small Unilamellar Vesicles), MLV (Multilamellr Vesicles) or LUV (Large Unilamellar Vesicles). Niosomal drug delivery is potentially applicable to many pharmacological agents for their action against various diseases