Learned Nonlinear Predictor for Critically Sampled 3D Point Cloud Attribute Compression

We study 3D point cloud attribute compression via a volumetric approach: assuming point cloud geometry is known at both encoder and decoder, parameters $\theta$ of a continuous attribute function $f: \mathbb{R}^3 \mapsto \mathbb{R}$ are quantized to $\hat{\theta}$ and encoded, so that discrete samples $f_{\hat{\theta}}(\mathbf{x}_i)$ can be recovered at known 3D points $\mathbf{x}_i \in \mathbb{R}^3$ at the decoder. Specifically, we consider a nested sequences of function subspaces $\mathcal{F}^{(p)}_{l_0} \subseteq \cdots \subseteq \mathcal{F}^{(p)}_L$, where $\mathcal{F}_l^{(p)}$ is a family of functions spanned by B-spline basis functions of order $p$, $f_l^*$ is the projection of $f$ on $\mathcal{F}_l^{(p)}$ and encoded as low-pass coefficients $F_l^*$, and $g_l^*$ is the residual function in orthogonal subspace $\mathcal{G}_l^{(p)}$ (where $\mathcal{G}_l^{(p)} \oplus \mathcal{F}_l^{(p)} = \mathcal{F}_{l+1}^{(p)}$) and encoded as high-pass coefficients $G_l^*$. In this paper, to improve coding performance over [1], we study predicting $f_{l+1}^*$ at level $l+1$ given $f_l^*$ at level $l$ and encoding of $G_l^*$ for the $p=1$ case (RAHT($1$)). For the prediction, we formalize RAHT(1) linear prediction in MPEG-PCC in a theoretical framework, and propose a new nonlinear predictor using a polynomial of bilateral filter. We derive equations to efficiently compute the critically sampled high-pass coefficients $G_l^*$ amenable to encoding. We optimize parameters in our resulting feed-forward network on a large training set of point clouds by minimizing a rate-distortion Lagrangian. Experimental results show that our improved framework outperformed the MPEG G-PCC predictor by $11$ to $12\%$ in bit rate reduction

Volumetric 3D Point Cloud Attribute Compression: Learned polynomial bilateral filter for prediction

We extend a previous study on 3D point cloud attribute compression scheme that uses a volumetric approach: given a target volumetric attribute function $f : \mathbb{R}^3 \mapsto \mathbb{R}$, we quantize and encode parameters $\theta$ that characterize $f$ at the encoder, for reconstruction $f_{\hat{\theta}}(\mathbf(x))$ at known 3D points $\mathbf(x)$ at the decoder. Specifically, parameters $\theta$ are quantized coefficients of B-spline basis vectors $\mathbf{\Phi}_l$ (for order $p \geq 2$) that span the function space $\mathcal{F}_l^{(p)}$ at a particular resolution $l$, which are coded from coarse to fine resolutions for scalability. In this work, we focus on the prediction of finer-grained coefficients given coarser-grained ones by learning parameters of a polynomial bilateral filter (PBF) from data. PBF is a pseudo-linear filter that is signal-dependent with a graph spectral interpretation common in the graph signal processing (GSP) field. We demonstrate PBF's predictive performance over a linear predictor inspired by MPEG standardization over a wide range of point cloud datasets

Experimental investigations into the irregular synthesis of iron(iii) terephthalate metal-organic frameworks MOF-235 and MIL-101

MOF-235(Fe) and MIL-101(Fe) are two well-studied metal-organic frameworks (MOFs) with dissimilar crystal structures and topologies. Previously reported syntheses of the former show that it has greatly varying surface areas, indicating a lack of phase purity of the products, i.e. the possible presence of both MOFs in the same sample. To find the reason for this, we have tested and modified the commonly used synthesis protocol of MOF-235(Fe), where a 3 : 5 molar ratio of iron(iii) ions and a terephthalic acid linker is heated in a 1 : 1 DMF : ethanol solvent at 80 degrees C for 24 h. Using XRD and BET surface area (SA(BET)) measurements, we found that it is difficult to obtain a pure phase of MOF-235, as MIL-101 also appears to form during the solvothermal treatment. Comparison of the XRD peak height ratios of the synthesis products revealed a direct correlation between the MOF-235/MIL-101 content and surface area; more MOF-235 yields a lower surface area and vice versa. In general, using a larger (3 : 1) DMF : ethanol ratio than that reported in the literature and a stoichiometric (4 : 3) Fe(iii) : TPA ratio yields a nearly pure MOF-235 product (SA(BET) = 295 m(2) g(-1), 67% yield). An optimized synthesis procedure was developed to obtain high-surface area MIL-101(Fe) (SA(BET) > 2400 m(2) g(-1)) in a large yield and at a previously unreported temperature (80 degrees C vs. previously used 110-150 degrees C). In situ X-ray scattering was utilized to investigate the crystallization of MOF-235 and MIL-101. At 80 degrees C, only MOF-235 formed and at 85 and 90 degrees C, only MIL-101 formed

Prolonged and tunable residence time using reversible covalent kinase inhibitors.

Drugs with prolonged on-target residence times often show superior efficacy, yet general strategies for optimizing drug-target residence time are lacking. Here we made progress toward this elusive goal by targeting a noncatalytic cysteine in Bruton's tyrosine kinase (BTK) with reversible covalent inhibitors. Using an inverted orientation of the cysteine-reactive cyanoacrylamide electrophile, we identified potent and selective BTK inhibitors that demonstrated biochemical residence times spanning from minutes to 7 d. An inverted cyanoacrylamide with prolonged residence time in vivo remained bound to BTK for more than 18 h after clearance from the circulation. The inverted cyanoacrylamide strategy was further used to discover fibroblast growth factor receptor (FGFR) kinase inhibitors with residence times of several days, demonstrating the generalizability of the approach. Targeting of noncatalytic cysteines with inverted cyanoacrylamides may serve as a broadly applicable platform that facilitates 'residence time by design', the ability to modulate and improve the duration of target engagement in vivo

Gaming disorder and the COVID-19 pandemic: Treatment demand and service delivery challenges

Gaming activities have conferred numerous benefits during the COVID-19 pandemic. However, someindividuals may be at greater risk of problem gaming due to disruption to adaptive routines, increased anxiety and/or depression, and social isolation. This paper presents a summary of 2019–2021 service data from specialist addiction centers in Germany, Switzerland, Japan, and the United Kingdom. Treatment demand for gaming disorder has exceeded service capacity during the pandemic, with significant service access issues. These data highlight the need for adaptability of gaming disorder services and greater resources and funding to respond effectively in future public health crises

Continuous improvement through differential trajectories of individual minimal disease activity criteria with guselkumab in active psoriatic arthritis: post hoc analysis of a phase 3, randomized, double-blind, placebo-controlled study

Background To explore the trajectory of, and factors contributing to, achievement of individual criteria of minimal disease activity (MDA) in patients with active psoriatic arthritis (PsA) treated with guselkumab. Methods The Phase 3, randomized, placebo-controlled DISCOVER-2 study enrolled adults (N = 739) with active PsA despite standard therapies who were biologic/Janus kinase inhibitor-naive. Patients were randomized 1:1:1 to guselkumab 100 mg every 4 weeks; guselkumab 100 mg at week 0, week 4, then every 8 weeks; or placebo. In this post hoc analysis, patients randomized to guselkumab were included and pooled (N = 493). Longitudinal trajectories of achieving each MDA criterion through week 100 were derived using non-responder imputation. Time to achieve each criterion was estimated with Kaplan-Meier analysis. Multivariate regression for time to achieve each criterion (Cox regression) and achievement at week 100 (logistic regression) was used to identify contributing factors. Results Continuous improvement across all MDA domains was shown over time. ~70% of patients achieved near remission in swollen joint count (SJC), Psoriasis Area and Severity Index (PASI), and enthesitis through week 100. Median times to achieve individual criteria differed significantly (p Conclusions Substantial proportions of guselkumab-treated patients achieved individual MDA criteria, each showing continuous improvement through week 100, although with distinct trajectories. Median times to achieve physician-assessed MDA criteria were significantly faster compared with patient-driven criteria. Identification of modifiable factors affecting the time to achieve patient-reported criteria has the potential to optimize the achievement and sustainability of MDA in the clinic via a multidisciplinary approach to managing PsA, involving both medical and lifestyle interventions. Trial registration number NCT03158285. Trial registration date May 16, 2017

Facial expression recognition in dynamic sequences: An integrated approach

Automatic facial expression analysis aims to analyse human facial expressions and classify them into discrete categories. Methods based on existing work are reliant on extracting information from video sequences and employ either some form of subjective thresholding of dynamic information or attempt to identify the particular individual frames in which the expected behaviour occurs. These methods are inefficient as they require either additional subjective information, tedious manual work or fail to take advantage of the information contained in the dynamic signature from facial movements for the task of expression recognition. In this paper, a novel framework is proposed for automatic facial expression analysis which extracts salient information from video sequences but does not rely on any subjective preprocessing or additional user-supplied information to select frames with peak expressions. The experimental framework demonstrates that the proposed method outperforms static expression recognition systems in terms of recognition rate. The approach does not rely on action units (AUs) and therefore, eliminates errors which are otherwise propagated to the final result due to incorrect initial identification of AUs. The proposed framework explores a parametric space of over 300 dimensions and is tested with six state-of-the-art machine learning techniques. Such robust and extensive experimentation provides an important foundation for the assessment of the performance for future work. A further contribution of the paper is offered in the form of a user study. This was conducted in order to investigate the correlation between human cognitive systems and the proposed framework for the understanding of human emotion classification and the reliability of public databases