An Image is Worth Multiple Words: Multi-attribute Inversion for Constrained Text-to-Image Synthesis

We consider the problem of constraining diffusion model outputs with a user-supplied reference image. Our key objective is to extract multiple attributes (e.g., color, object, layout, style) from this single reference image, and then generate new samples with them. One line of existing work proposes to invert the reference images into a single textual conditioning vector, enabling generation of new samples with this learned token. These methods, however, do not learn multiple tokens that are necessary to condition model outputs on the multiple attributes noted above. Another line of techniques expand the inversion space to learn multiple embeddings but they do this only along the layer dimension (e.g., one per layer of the DDPM model) or the timestep dimension (one for a set of timesteps in the denoising process), leading to suboptimal attribute disentanglement. To address the aforementioned gaps, the first contribution of this paper is an extensive analysis to determine which attributes are captured in which dimension of the denoising process. As noted above, we consider both the time-step dimension (in reverse denoising) as well as the DDPM model layer dimension. We observe that often a subset of these attributes are captured in the same set of model layers and/or across same denoising timesteps. For instance, color and style are captured across same U-Net layers, whereas layout and color are captured across same timestep stages. Consequently, an inversion process that is designed only for the time-step dimension or the layer dimension is insufficient to disentangle all attributes. This leads to our second contribution where we design a new multi-attribute inversion algorithm, MATTE, with associated disentanglement-enhancing regularization losses, that operates across both dimensions and explicitly leads to four disentangled tokens (color, style, layout, and object)

Iterative Multi-granular Image Editing using Diffusion Models

Recent advances in text-guided image synthesis has dramatically changed how creative professionals generate artistic and aesthetically pleasing visual assets. To fully support such creative endeavors, the process should possess the ability to: 1) iteratively edit the generations and 2) control the spatial reach of desired changes (global, local or anything in between). We formalize this pragmatic problem setting as Iterative Multi-granular Editing. While there has been substantial progress with diffusion-based models for image synthesis and editing, they are all one shot (i.e., no iterative editing capabilities) and do not naturally yield multi-granular control (i.e., covering the full spectrum of local-to-global edits). To overcome these drawbacks, we propose EMILIE: Iterative Multi-granular Image Editor. EMILIE introduces a novel latent iteration strategy, which re-purposes a pre-trained diffusion model to facilitate iterative editing. This is complemented by a gradient control operation for multi-granular control. We introduce a new benchmark dataset to evaluate our newly proposed setting. We conduct exhaustive quantitatively and qualitatively evaluation against recent state-of-the-art approaches adapted to our task, to being out the mettle of EMILIE. We hope our work would attract attention to this newly identified, pragmatic problem setting.Comment: Pre-prin

Temporomandibular Joint Pain

Temporomandibular joint (TMJ) is a synovial articulation between mandibular condyle and glenoid fossa in the temporal bone. Any structural and/or functional changes can affect the TMJ and related structures. Temporomandibular disorder (TMD) is a heterogeneous group of musculoskeletal disorders mainly characterised by regional pain in the facial and preauricular area and/or limitations/interference of jaw movement. TMD has multifactorial aetiology, which includes biology, and environmental social, emotional, and cognitive factors. TMD is more common orofacial pain condition and nondental origin. Factors associated with TMD include other pain condition, auto-immune disorder and psychiatric illness. The clinical conditions may present with limitation in opening and closing mouth, pain and articular noise. So this chapter mainly deals with the classification of TMJ disorder, diagnosis and management particularly TENS and ultrasound therapy for TMJ disorder

Optimization of effective doping concentration of emitter for ideal c-Si solar cell device with PC1D simulation

Increasing silicon solar cell efficiency plays a vital role in improving the dominant market share of photo-voltaic systems in the renewable energy sector. The performance of the solar cells can be evaluated by making a profound analysis on various effective parameters, such as the sheet resistance, doping concentration, thickness of the solar cell, arbitrary dopant profile, etc., using software simulation tools, such as PC1D. In this paper, we present the observations obtained from the evaluation carried out on the impact of sheet resistance on the solar cell’s parameters using PC1D software. After which, the EDNA2 simulation tool was used to analyse the emitter saturation current density for the chosen arbitrary dopant profile. Results indicated that the diffusion profile with low surface concentration and shallow junction depth can improve the blue response at the frontal side of the solar cell. The emitter saturation current density decreases from 66.52 to 36.82 fA/cm2 for the subsequent increase in sheet resistance. The blue response also increased from 89.6% to 97.5% with rise in sheet resistance. In addition, the short circuit density and open circuit voltage was also observed to be improved by 0.6 mA/cm2 and 3 mV for the sheet resistance value of 130 Ω/sq, which resulted in achieving the highest efficiency of 20.6%

The challenges of using bioluminescence as a light source

Bioluminescence is the production of light by living organisms like algae and fire- flies. The replication of this phenomenon to model an artificial light source was explored through this study. A bioreactor was designed and mixture theory was used to mathematically model the problem. The bioreactor was tested to examine the feasibility of the model. The mathematical model, solved using a finite difference scheme, was compared against the experimental results. It was found that though the concept holds potential, a much more refined model is required in order to put the concept to practical use

COVID-19 presenting as COVID - Clot obstructing vessels in deep

Corona Virus Disease-2019 (COVID-19), a global health emergency shows a wide spectrum of clinical presentations and manifestations with dry cough, fever and shortness of breath being the most common symptoms. Available evidences shows that COVID-19 can be complicated by thrombotic manifestations. High index of clinical suspicion and early initiation of prophylactic or therapeutic dose of anti-coagulants is needed to prevent the occurrence of life-threatening thrombosis. We hereby present two cases of COVID-19 which had thrombosis at the time of initial presentation itself

From hospitalization records to surveillance: The use of local patient profiles to characterize cholera in Vellore, India

<div><p>Despite availability of high quality medical records, health care systems often do not have the resources or tools to utilize these data efficiently. Yet, hospital-based, laboratory-confirmed records may pave the way for building reliable surveillance systems capable of monitoring temporal trends of emerging infections. In this communication, we present a new tool to compress and visualize medical records with a local population profile (LPP) approach, which transforms information into statistically comparable patterns. We provide a step-by-step tutorial on how to build, interpret, and expand the use of LPP using hospitalization records of laboratory-confirmed cholera. We abstracted case information from the databases maintained by the Department of Clinical Microbiology at Christian Medical College in Vellore, India. We used a single-year age distribution to construct LPPs for O1, O139, and non O1/O139 serotypes of <i>Vibrio cholerae</i>. Disease counts and hospitalization rates were converted into fitted kernel-based probability densities. We formally compared LPPs with the Kolmogorov-Smirnov test, and created multi-panel visuals to depict temporal trend, age distribution, and hospitalization rates simultaneously. Our first implementation of LPPs revealed information that is typically gathered from surveillance systems such as: i) estimates of the demographic distribution of diseases and identification of a population at risk, ii) changes in the dominant pathogen presence; and iii) trends in disease occurrence. The LPP demonstrated the benefit of increased resolution in pattern detection of disease for different <i>Vibrio cholerae</i> serotypes and two demographic categories by showing patterns and anomalies that would be obscured by traditional methods of analysis and visualization. LPP can be used effectively to compile basic patient information such as age, sex, diagnosis, location, and time into compact visuals. Future development of the proposed approach will allow public health researchers and practitioners to broadly utilize and efficiently compress large volumes of medical records without loss of information.</p></div

Descriptive statistics^* of cholera patient ages from Vellore.

<p>Descriptive statistics^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0182642#t001fn001" target="_blank">*</a>} of cholera patient ages from Vellore.</p

Location-specific patient profile plots for cholera patients.

<p>a) plot highlights the inflection points of disease probability of all Vellore patients; b) probability plots by sex with subscripts M and F referring to males and females; c) probability plot by serotype with subscripts 1, 2 and 3 referring to serotype O1, serotype O139, and non O1/O139 serotypes; d) probability plot by sex and serotype with subscripts as a combination from plot b and c. N is the total number of patients.</p

Before and after data smoothing.

<p>a) probability density plots of original and adjusted census data for 2011; b) comparison of original Vellore cholera patient disease count by age and adjusted disease count by age; c) original hospitalization rate calculated with original census data and patient values compared with adjusted rate based on adjusted census and adjusted patient data.</p