bluuebunny/crossref_metadata_2025 huggingface dataset:
Clinical predictors of fracture in patients with shoulder dislocation: systematic review of diagnostic test accuracy studies
Authors: Oldrini Ilaria , Coventry Laura , Novak Alex , Gwilym Steve , Metcalfe David
Published: November 2022

Background

Prereduction radiographs are conventionally used to exclude fracture before attempts to reduce a dislocated shoulder in the ED. However, this step increases cost, exposes patients to ionising radiation and may delay closed reduction. Some studies have suggested that prereduction imaging may be omitted for a subgroup of patients with shoulder dislocations.

Objectives

To determine whether clinical predictors can identify patients who may safely undergo closed reduction of a dislocated shoulder without prereduction radiographs.

Methods

A systematic review and meta-analysis of diagnostic test accuracy studies that have evaluated the ability of clinical features to identify concomitant fractures in patients with shoulder dislocation. The search was updated to 23 June 2022 and language limits were not applied. All fractures were included except for Hill-Sachs lesions. Quality assessment was undertaken using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Data were pooled and meta-analysed by fitting univariate random effects and multilevel mixed effects logistic regression models.

Results

Eight studies reported data on 2087 shoulder dislocations and 343 concomitant fractures. The most important potential sources of bias were unclear blinding of those undertaking the clinical (6/8 studies) and radiographic (3/8 studies) assessment. The prevalence of concomitant fracture was 17.5%. The most accurate clinical predictors were age >40 (positive likelihood ratio (LR+) 1.8, 95% CI 1.5 to 2.1; negative likelihood ratio (LR−) 0.4, 95% CI 0.2 to 0.6), female sex (LR+ 2.0, 95% CI 1.6 to 2.4; LR− 0.7, 95% CI 0.6 to 0.8), first-time dislocation (LR+ 1.7, 95% CI 1.4 to 2.0; LR− 0.2, 95% CI 0.1 to 0.5) and presence of humeral ecchymosis (LR+ 3.0–5.7, LR− 0.8–1.1). The most important mechanisms of injury were high-energy mechanism fall (LR+ 2.0–9.8, LR− 0.4–0.8), fall >1 flight of stairs (LR+ 3.8, 95% CI 0.6 to 13.1; LR− 1.0, 95% CI 0.9 to 1.0) and motor vehicle collision (LR+ 2.3, 95% CI 0.5 to 4.0; LR− 0.9, 95% CI 0.9 to 1.0). The Quebec Rule had a sensitivity of 92.2% (95% CI 54.6% to 99.2%) and a specificity of 33.3% (95% CI 23.1% to 45.3%), but the Fresno-Quebec rule identified all clinically important fractures across two studies: sensitivity of 100% (95% CI 89% to 100%) in the derivation dataset and 100% (95% CI 90% to 100%) in the validation study. The specificity of the Fresno-Quebec rule ranged from 34% (95% CI 28% to 41%) in the derivation dataset to 24% (95% CI 16% to 33%) in the validation study.

Conclusion

Clinical prediction rules may have a role in supporting shared decision making after shoulder dislocation, particularly in the prehospital and remote environments when delay to imaging is anticipated.
Artificial intelligence and health inequities in primary care: a systematic scoping review and framework
Authors: d'Elia Alexander , Gabbay Mark , Rodgers Sarah , Kierans Ciara , Jones Elisa , Durrani Irum , Thomas Adele , Frith Lucy
Published: November 2022

Objective

Artificial intelligence (AI) will have a significant impact on healthcare over the coming decade. At the same time, health inequity remains one of the biggest challenges. Primary care is both a driver and a mitigator of health inequities and with AI gaining traction in primary care, there is a need for a holistic understanding of how AI affect health inequities, through the act of providing care and through potential system effects. This paper presents a systematic scoping review of the ways AI implementation in primary care may impact health inequity.

Design

Following a systematic scoping review approach, we searched for literature related to AI, health inequity, and implementation challenges of AI in primary care. In addition, articles from primary exploratory searches were added, and through reference screening.

The results were thematically summarised and used to produce both a narrative and conceptual model for the mechanisms by which social determinants of health and AI in primary care could interact to either improve or worsen health inequities.

Two public advisors were involved in the review process.

Eligibility criteria

Peer-reviewed publications and grey literature in English and Scandinavian languages.

Information sources

PubMed, SCOPUS and JSTOR.

Results

A total of 1529 publications were identified, of which 86 met the inclusion criteria. The findings were summarised under six different domains, covering both positive and negative effects: (1) access, (2) trust, (3) dehumanisation, (4) agency for self-care, (5) algorithmic bias and (6) external effects. The five first domains cover aspects of the interface between the patient and the primary care system, while the last domain covers care system-wide and societal effects of AI in primary care. A graphical model has been produced to illustrate this. Community involvement throughout the whole process of designing and implementing of AI in primary care was a common suggestion to mitigate the potential negative effects of AI.

Conclusion

AI has the potential to affect health inequities through a multitude of ways, both directly in the patient consultation and through transformative system effects. This review summarises these effects from a system tive and provides a base for future research into responsible implementation.
Ligufalimab, a novel anti-CD47 antibody with no hemagglutination demonstrates both monotherapy and combo antitumor activity
Authors: Qu Tailong , Zhong Tingting , Pang Xinghua , Huang Zhaoliang , Jin Chunshan , Wang Zhongmin Maxwell , Li Baiyong , Xia Yu
Published: November 2022

Background

CD47 is a widely expressed transmembrane glycoprotein that delivers an antiphagocytic signal on macrophages through its interaction with SIRPα. CD47 is highly expressed in cancer cells and its overexpression is correlated with poor prognosis. CD47 blocking antibodies are actively being developed worldwide for cancer therapy, and the most challenging concern is associated with hematotoxicity. Ligufalimab (AK117) is a novel humanized IgG4 anti-CD47 antibody without hemagglutination effect. Blockade of CD47-SIRPα pathway by AK117 leads to a promising therapeutic strategy for cancer treatment with unique safety features.

Methods

AK117 was discovered through a screening hierarchy excluding hemagglutination. AK117 was characterized by detecting CD47-SIRPα blocking potential. Its effect on human red blood cells was examined and the mechanism of its binding with erythrocytes was studied. The abilities of AK117 and its combination with various opsonizing antibodies to promote macrophage-dependent phagocytosis of multiple human tumor cells were determined using fluorescence microscopy and flow cytometry. In vivo, the antitumor efficacy of AK117 monotherapy and combination with AK112 (an anti-PD-1/VEGF-A bispecific antibody) was assessed in a variety of xenograft models. Toxicologic studies were evaluated in non-human primates.

Results

AK117 bound to CD47 with high affinity and blocked the CD47-SIRPα interaction. AK117 did not induce hemagglutination and showed significantly lower degree of erythrophagocytosis compared with Hu5F9-G4, and this mechanism of hemagglutination resistance might be related to the binding conformation. AK117 enhanced macrophage-mediated phagocytosis in both hematologic cancer and solid tumor cell lines as a single agent or in combination with cetuximab and rituximab in vitro, respectively. The antitumor effects of AK117 as a single agent or in combination with AK112 were also encouraging in various xenograft models. In non-human primates, AK117 showed less hematotoxicity compared with Hu5F9-G4.

Conclusions

AK117 eliminated hemagglutination and also enabled to maintain full effectiveness of CD47 blockade on tumor cells, which resulted in excellent antitumor efficacy and favorable safety profile of AK117. A series of clinical trials of AK117 as a therapeutic agent in combination with various agents such as AK112 are in progress for the treatment of multiple hematologic malignancies and solid tumors.
Final results from TAIL: updated long-term efficacy of atezolizumab in a diverse population of patients with previously treated advanced non-small cell lung cancer
Authors: Ardizzoni Andrea , Azevedo Sergio , Rubio-Viqueira Belen , Rodriguez-Abreu Delvys , Alatorre-Alexander Jorge , Smit Hans J M , Yu Jinming , Syrigos Konstantinos , Höglander Elen , Kaul Monika , Tolson Jonathan , Hu Youyou , Vollan Hans Kristian , Newsom-Davis Thomas
Published: November 2022

In patients with previously treated advanced or metastatic non-small cell lung cancer (NSCLC), atezolizumab therapy improves survival with manageable safety. The open-label, single-arm phase III/IV TAIL study (NCT03285763) evaluated atezolizumab monotherapy in patients with previously treated NSCLC, including those with Eastern Cooperative Oncology Group performance status of 2, severe renal impairment, prior anti-programmed death 1 therapy, autoimmune disease, and age ≥75 years. Patients received atezolizumab intravenously (1200 mg) every 3 weeks. At data cut-off for final analysis, the median follow-up was 36.1 (range 0.0–42.3) months. Treatment-related (TR) serious adverse events (SAEs) and TR immune-related adverse events (irAEs) were the coprimary endpoints. Secondary endpoints included overall survival (OS), progression-free survival (PFS), overall response rate, and duration of response. Safety and efficacy in key patient subgroups were also assessed. TR SAEs and TR irAEs occurred in 8.0% and 9.4% of patients, respectively. No new safety signals were documented. In the overall population, median OS and PFS (95% CI) were 11.2 months (8.9 to 12.7) and 2.7 months (2.3 to 2.8), respectively. TAIL showed that atezolizumab has a similar risk-benefit profile in clinically diverse patients with previously treated NSCLC, which may guide treatment decisions for patients generally excluded from pivotal clinical trials.
Phase II clinical and immune correlate study of adjuvant nivolumab plus ipilimumab for high-risk resected melanoma
Authors: Khushalani Nikhil I , Vassallo Melinda , Goldberg Judith D , Eroglu Zeynep , Kim Younchul , Cao Biwei , Ferguson Robert , Monson Kelsey R , Kirchhoff Tomas , Amato Carol M , Burke Paulo , Strange Ann , Monk Emily , Gibney Geoffrey Thomas , Kudchadkar Ragini , Markowitz Joseph , Brohl Andrew S , Pavlick Anna , Richards Alison , Woods David M , Weber Jeffrey
Published: November 2022

Background

Adjuvant therapy for high-risk resected melanoma with programmed cell-death 1 blockade results in a median relapse-free survival (RFS) of 5 years. The addition of low dose ipilimumab (IPI) to a regimen of adjuvant nivolumab (NIVO) in CheckMate-915 did not result in increased RFS. A pilot phase II adjuvant study of either standard dose or low dose IPI with NIVO was conducted at two centers to evaluate RFS with correlative biomarker studies.

Methods

Patients with resected stages IIIB/IIIC/IV melanoma received either IPI 3 mg/kg and NIVO 1 mg/kg (cohort 4) or IPI 1 mg/kg and NIVO 3 mg/kg (cohorts 5 and 6) induction therapy every 3 weeks for 12 weeks, followed by maintenance NIVO. In an amalgamated subset of patients across cohorts, peripheral T cells at baseline and on-treatment were assessed by flow cytometry and RNA sequencing for exploratory biomarkers.

Results

High rates of grade 3–4 adverse events precluded completion of induction therapy in 50%, 35% and 7% of the patients in cohorts 4, 5 and 6, respectively. At a median of 63.9 months of follow-up, 16/56 patients (29%) relapsed. For all patients, at 5 years, RFS was 71% (95% CI: 60 to 84), and overall survival was 94% (95% CI: 88 to 100). Expansion of CD3+CD4+CD38+CD127−GARP− T cells, an on-treatment increase in CD39 expression in CD8+ T cells, and T-cell expression of phosphorylated signal-transducer-and-activator-of-transcription (STAT)2 and STAT5 were associated with relapse.

Conclusions

Adjuvant IPI/NIVO at the induction doses used resulted in promising relapse-free and overall survival, although with a high rate of grade 3–4 adverse events. Biomarker analyses highlight an association of ectoenzyme-expressing T cells and STAT signaling pathways with relapse, warranting future validation.

Trial registration number

NCT01176474andNCT02970981.
Evaluating disease control following belimumab treatment in patients with SLE enrolled in the US OBSErve study
Authors: Hunnicutt Jacob N , Fairburn-Beech Jolyon , Georgiou Mary Elizabeth , Richards Anna , Gregan Yun Irene , Quasny Holly , Chauhan Deven
Published: November 2022

Objective

To characterise disease control and remission in patients with SLE receiving belimumab for up to 12 months in the real world.

Methods

This post hoc analysis (GSK Study 213502) used data from the US evaluation Of use of Belimumab in clinical practice SEttings (OBSErve) study (GSK Study 117295), an observational cohort study of adults with SLE initiating and continuing belimumab for ≥6 months. Data were collected every 6 months by physician chart review; details of disease activity using the Safety of Estrogens in Lupus Erythematosus National Assessment–SLE Disease Activity Index (SELENA-SLEDAI) score were collected if routinely used by physicians. Disease control definitions evaluated were SELENA-SLEDAI score of ≤2 at 12 months, SELENA-SLEDAI score of ≤2 and glucocorticoid (prednisone equivalent) dose of ≤5 mg/day at 12 months, SELENA-SLEDAI score of ≤2 and glucocorticoid dose of ≤5 mg/day at both 6 and 12 months. Disease remission definition was SELENA-SLEDAI score=0 at 12 months. Glucocorticoid dose during follow-up was quantified.

Results

US OBSErve enrolled 501 patients, 90 of whom had eligible SELENA-SLEDAI scores for inclusion in this analysis. Mean (SD) SELENA-SLEDAI scores were 13.1 (3.0) at baseline and 4.9 (3.4) at 12 months. Disease control at 12 months was achieved by 31.1% of patients when defined as a SELENA-SLEDAI score of ≤2 (95% CI 21.8 to 41.7); this decreased to 25.6% when requiring a SELENA-SLEDAI score of ≤2 and glucocorticoid dose of ≤5 mg/day (95% CI 16.9 to 35.8) and 17.8% when requiring a SELENA-SLEDAI score of ≤2 and glucocorticoid dose of ≤5 mg/day at both 6 and 12 months (95% CI 10.5 to 27.3). No patient achieved remission at 12 months. Glucocorticoids decreased from a baseline median of 20.0 mg/day (IQR 15.0–30.0) to 5.0 mg/day (IQR 0–10.0) at 12 months.

Conclusion

Improved disease control and reduced glucocorticoid use was achieved for a proportion of patients following up to 12 months of belimumab treatment in a US real-world setting.
Three decades of environmental change studies at alpine Finse, Norway: climate trends and responses across ecological scales
Authors: Roos Ruben E. , Asplund Johan , Birkemoe Tone , Halbritter Aud H. , Olsen Siri Lie , Vassvik Linn , van Zuijlen Kristel , Klanderud Kari
Published: November 2022

The International Tundra Experiment (ITEX) was established to understand how environmental change impacts Arctic and alpine ecosystems. The success of the ITEX network has allowed for several important across-site syntheses, and for some ITEX sites enough data have now been collected to perform within-site syntheses on the effects of environmental change across ecological scales. In this study, we analyze climate data and synthesize three decades of research on the ecological effects of environmental change at the ITEX site at Finse, southern Norway. We found a modest warming rate of +0.36 °C per decade and minor effects on growing season length. Maximum winter snow depth was highest in winters with a positive North Atlantic Oscillation. Our synthesis included 80 ecological studies from Finse, biased towards primary producers with few studies on ecological processes. Species distributions depended on microtopography and microclimate. Experimental warming had contrasting effects on abundance and traits of individual species and only modest effects at the community level above and below ground. In contrast, nutrient addition experiments caused strong responses in primary producer and arthropod communities. This within-site synthesis enabled us to conclude how different environmental changes (experimental and ambient warming, nutrient addition, and environmental gradients) impact across ecological scales, which is challenging to achieve with across-site approaches.
Multiboson signals in the UN2HDM
Authors: Aguilar-Saavedra J. A. , Joaquim F. R. , Seabra J. F.
Published: November 2022

We address multiboson production from a heavy $$Z'$$

Z
′

resonance in the context of the UN2HDM, a standard model extension with an additional $$\text {U}(1)'$$

U

(
1
)

′

symmetry and an enlarged scalar sector with an extra doublet and a singlet. After taking into account theoretical and experimental constraints on the model, it turns out this type of signals – mostly uncovered by current searches – could be sizeable. We focus on three benchmark scenarios, each of them predicting up to 4000 multiboson events with the LHC Run 2 collected luminosity. Anomaly-detection methods could uncover those signals, if present in data.
Parameterisation space for Cornell potential in a QCD potential model
Authors: Pathak Krishna Kingkar , Bhattacharya Satyadeep , Das Tapashi
Published: November 2022

We make a critical analysis on the free parameters of the Cornell potential $$-\frac{4\alpha _{s}}{3r}+br+c$$

-

4

α
s

3
r

+
b
r
+
c

and provide a parameterisation space for the strong coupling constant $$\alpha _{s}$$

α
s

and the constant shift ‘c’ for choosing linear part as perturbation in the potential model. In the analysis of heavy-light mesons $$(D,D_{s},B,B_{s}\ \text {and}\ B_{c})$$

(
D
,

D
s

,
B
,

B
s

and

B
c

)

, we have found a wide range of values for the coupling constant i.e $$0.20 \le \alpha _{s}\le 0.64$$

0.20
≤

α
s

≤
0.64

with $$-1.2 \le c \le -0.66$$

-
1.2
≤
c
≤
-
0.66

which can be used to treat the confining part as perturbation.
Comparative analysis of numerical and newly constructed soliton solutions of stochastic Fisher-type equations in a sufficiently long habitat
Authors: Baber Muhammad Z. , Seadway Aly R. , Iqbal Muhammad S. , Ahmed Nauman , Yasin Muhammad W. , Ahmed Muhammad O.
Published: November 2022

This paper is a key contribution with respect to the applications of solitary wave solutions to the unique solution in the presence of the auxiliary data. Hence, this study provides an insight for the unique selection of solitons for the physical problems. Additionally, the novel numerical scheme is developed to compare the result. Further, this paper deals with the stochastic Fisher-type equation numerically and analytically with a time noise process. The nonstandard finite difference scheme of stochastic Fisher-type equation is proposed. The stability analysis and consistency of this proposed scheme are constructed with the help of Von Neumann analysis and Itô integral. This model is applicable in the wave proliferation of a viral mutant in an infinitely long habitat. Additionally, for the sake of exact solutions, we used the Riccati equation mapping method. The solutions are constructed in the form of hyperbolic, trigonometric and rational forms with the help of Mathematica 11.1. Lastly, the graphical comparisons of numerical solutions with exact wave solution with the help of Neumann boundary conditions are constructed successfully in the form of 3D and line graphs by using different values of parameters.
THERMAL CHARACTERISTICS OF NONLINEAR CONVECTION AND RADIATION FOR THE FLOW OF TRI-HYBRID NANOFLUIDS OVER STRETCHABLE SURFACE WITH ENERGY SOURCE
Authors: NASIR SALEEM , SIRISUBTAWEE SEKSON , GUL TAZA , JUNTHAREE PONGPOL , ALGHAMDI WAJDI , ALI ISHTIAQ
Published: November 2022

In this paper, an innovative form of nanofluids is identified as tri-hybrid nanofluid, which is synthesized by dispersing three or more varieties of nanomaterials in the considered base fluid. So, in this study, we comparatively examined SiO2/H2O nanofluid, TiO2+Al2O3/H2O hybrid nanofluid and SiO[Formula: see text]TiO2+Al2O3/H2O ternary hybrid nanofluid. Stretching of the flat surface enables us to develop the nanofluids flow. Additional considerations include the impacts of MHD, viscid dissipation, nonlinear thermal convection and radiation, joule heating and the presence of a heat source. For transforming PDEs (continuity, motion, heat equation and boundary constraints) into ODEs, an appropriate transformation procedure is used. HAM technique is used to solve these nonlinear coupled ODEs. Graphs are used to evaluate and examine the effect of numerous describing variables on nano, hybrid and tri-hybrid nanofluids speed and heat distribution. Furthermore, the computed values of engineering-relevant parameters ([Formula: see text] and Nu) are tabulated and analyzed. The velocity of nanofluids acquires enhancing tendency for nonlinear thermal and mix convection parameter, but reverse upshot is assured due to nanoparticle volume fraction, Weissenberg number and magnetic parameters. Thermal field gets intensified in nature for magnetic and Eckert number, heat generation, thermal radiation and nanoparticles volume fractions. The ternary hybrid nanofluid has the most efficient behavior according to the comparative examination of ternary, hybrid and nanofluids.
Primitive and prime rings with s.Baer or related modules
Authors: Birkenmeier Gary F. , Kara Yeliz , Tercan Adnan
Published: November 2022

In this paper, we characterize some classes of primitive or prime rings in terms of s.Baer, quasi-s.Baer, s.Rickart, or p.q.-s.Baer modules.
ASERNet: Automatic speech emotion recognition system using MFCC-based LPC approach with deep learning CNN
Authors: Jagadeeshwar Kalyanapu , Sreenivasarao T. , Pulicherla Padmaja , Satyanarayana K. N. V. , Lakshmi K. Mohana , Kumar Pala Mahesh
Published: November 2022

Automatic speech emotion recognition (ASER) from source speech signals is quite a challenging task since the recognition accuracy is highly dependent on extracted features of speech that are utilized for the classification of speech emotion. In addition, pre-processing and classification phases also play a key role in improving the accuracy of ASER system. Therefore, this paper proposes a deep learning convolutional neural network (DLCNN)-based ASER model, hereafter denoted with ASERNet. In addition, the speech denoising is employed with spectral subtraction (SS) and the extraction of deep features is done using integration of linear predictive coding (LPC) with Mel-frequency Cepstrum coefficients (MFCCs). Finally, DLCNN is employed to classify the emotion of speech from extracted deep features using LPC-MFCC. The simulation results demonstrate the superior performance of the proposed ASERNet model in terms of quality metrics such as accuracy, precision, recall, and F1-score, respectively, compared to state-of-the-art ASER approaches.
An Analysis of the U.S. Individual Investor Sentiment Influence on Cryptocurrency Returns and Volatility
Authors: Sayim Mustafa , My Nguyen Quang
Published: November 2022

In this research, the U.S. investor sentiment effect on cryptocurrency returns and volatility is examined by separating it into irrational and rational parts. According to the data, an unforeseen rise in the rational part of U.S. individual investor attitude influences cryptocurrency returns statistically and positively. In other words, rational sentiment can result in rising cryptocurrency returns. Additionally, a positive significant association exists between cryptocurrency volatility and the rational part of the individual U.S. investor sentiment. The findings confirm the hypothesis that the behavior of rational investors who utilize and study the impact of economic factors on asset prices reduces cryptocurrency volatility.
Unveiling Attributes of Human 15-Lipoxygenase-1 as a Potential Candidate for Prostate Cancer Drug Development Using in Silico Approaches
Authors: Fathi Shirin , Sakhteman Amirhossein , Solhjoo Aida
Published: November 2022

Prostate carcinoma is one of the most commonly diagnosed visceral malignancies and the fifth leading cause of cancer-related mortality in males. Reportedly, a series of dietary lipids are identified as 1-cis-4-cis-pentadiene polyunsaturated fatty acids (PUFAs), which play a dominant role in prostate carcinogenesis. Four species of human lipoxygenases (LOXs), a family of nonheme iron-containing enzymes, mediate the deoxygenation of the aforementioned PUFAs. 15-LOX-1 in particular metabolizes the [Formula: see text]-6 lipids and generates certain metabolites (e.g., 13-(S)-hydroxyoctadecaenoic acid) which results in vascular homeostasis, cell proliferation and tissue differentiation in the prostate. Furthermore, in prostate cancer (PCa), the expression of 15-LOX-1 is elevated and positively correlated with the Gleason score of the tumor (an indicator of the disease severity). As membrane receptors, kinases and transcriptional factors are all affected by carcinogenic signals of 15-LOX-1, therapeutic agents that directly inhibit this enzyme can be advantageous in the treatment of PCa. To our knowledge, there are limited effective treatments for PCa, and there is no therapy for its metastatic condition. In this respect, 15-LOX-1, as an appropriate candidate for drug development, was subjected to homology modeling, phylogenic assessment, cross-docking analysis and molecular dynamics (MD) simulation to identify an eligible inhibiting agent amongst a library of 30 potential targeting compounds for PCa management.
Disentangled Image Colorization via Global Anchors
Authors: Xia Menghan , Hu Wenbo , Wong Tien-Tsin , Wang Jue
Published: November 2022

Colorization is multimodal by nature and challenges existing frameworks to achieve colorful and structurally consistent results. Even the sophisticated autoregressive model struggles to maintain long-distance color consistency due to the fragility of sequential dependence. To overcome this challenge, we propose a novel colorization framework that disentangles color multimodality and structure consistency through global color anchors, so that both aspects could be learned effectively. Our key insight is that several carefully located anchors could approximately represent the color distribution of an image, and conditioned on the anchor colors, we can predict the image color in a deterministic manner by utilizing internal correlation. To this end, we construct a colorization model with dual branches, where the color modeler predicts the color distribution for anchor color representation, and the color generator predicts the pixel colors by referring the sampled anchor colors. Importantly, the anchors are located under two principles: color independence and global coverage, which is realized with clustering analysis on the deep color features. To simplify the computation, we creatively adopt soft superpixel segmentation to reduce the image primitives, which still nicely reserves the reversibility to pixel-wise representation. Extensive experiments show that our method achieves notable superiority over various mainstream frameworks in perceptual quality. Thanks to anchor-based color representation, our model has the flexibility to support diverse and controllable colorization as well.
ControlVAE
Authors: Yao Heyuan , Song Zhenhua , Chen Baoquan , Liu Libin
Published: November 2022

In this paper, we introduce ControlVAE, a novel model-based framework for learning generative motion control policies based on variational autoencoders (VAE). Our framework can learn a rich and flexible latent representation of skills and a skill-conditioned generative control policy from a diverse set of unorganized motion sequences, which enables the generation of realistic human behaviors by sampling in the latent space and allows high-level control policies to reuse the learned skills to accomplish a variety of downstream tasks. In the training of ControlVAE, we employ a learnable world model to realize direct supervision of the latent space and the control policy. This world model effectively captures the unknown dynamics of the simulation system, enabling efficient model-based learning of high-level downstream tasks. We also learn a state-conditional prior distribution in the VAE-based generative control policy, which generates a skill embedding that outperforms the non-conditional priors in downstream tasks. We demonstrate the effectiveness of ControlVAE using a diverse set of tasks, which allows realistic and interactive control of the simulated characters.
Rhythmic Gesticulator
Authors: Ao Tenglong , Gao Qingzhe , Lou Yuke , Chen Baoquan , Liu Libin
Published: November 2022

Automatic synthesis of realistic co-speech gestures is an increasingly important yet challenging task in artificial embodied agent creation. Previous systems mainly focus on generating gestures in an end-to-end manner, which leads to difficulties in mining the clear rhythm and semantics due to the complex yet subtle harmony between speech and gestures. We present a novel co-speech gesture synthesis method that achieves convincing results both on the rhythm and semantics. For the rhythm, our system contains a robust rhythm-based segmentation pipeline to ensure the temporal coherence between the vocalization and gestures explicitly. For the gesture semantics, we devise a mechanism to effectively disentangle both low- and high-level neural embeddings of speech and motion based on linguistic theory. The high-level embedding corresponds to semantics, while the low-level embedding relates to subtle variations. Lastly, we build correspondence between the hierarchical embeddings of the speech and the motion, resulting in rhythm- and semantics-aware gesture synthesis. Evaluations with existing objective metrics, a newly proposed rhythmic metric, and human feedback show that our method outperforms state-of-the-art systems by a clear margin.
Learning to Relight Portrait Images via a Virtual Light Stage and Synthetic-to-Real Adaptation
Authors: Yeh Yu-Ying , Nagano Koki , Khamis Sameh , Kautz Jan , Liu Ming-Yu , Wang Ting-Chun
Published: November 2022

Given a portrait image of a person and an environment map of the target lighting, portrait relighting aims to re-illuminate the person in the image as if the person appeared in an environment with the target lighting. To achieve high-quality results, recent methods rely on deep learning. An effective approach is to supervise the training of deep neural networks with a high-fidelity dataset of desired input-output pairs, captured with a light stage. However, acquiring such data requires an expensive special capture rig and time-consuming efforts, limiting access to only a few resourceful laboratories. To address the limitation, we propose a new approach that can perform on par with the state-of-the-art (SOTA) relighting methods without requiring a light stage. Our approach is based on the realization that a successful relighting of a portrait image depends on two conditions. First, the method needs to mimic the behaviors of physically-based relighting. Second, the output has to be photorealistic. To meet the first condition, we propose to train the relighting network with training data generated by a virtual light stage that performs physically-based rendering on various 3D synthetic humans under different environment maps. To meet the second condition, we develop a novel synthetic-to-real approach to bring photorealism to the relighting network output. In addition to achieving SOTA results, our approach offers several advantages over the prior methods, including controllable glares on glasses and more temporally-consistent results for relighting videos.
A Biologically Inspired Hair Aging Model
Authors: Balbão Arthur E. , Walter Marcelo
Published: November 2022

Hair rendering has been a focal point of attention in computer graphics for the last couple of decades. However, there have been few contributions to the modeling and rendering of the natural hair aging phenomenon. We present a new technique that simulates the process of hair graying and hair thinning on digital models due to aging. Given a 3D human head model with hair, we first compute a segmentation of the head using K-means since hair aging occurs at different rates in distinct head parts. Hair graying is simulated according to recent biological knowledge on aging factors for hairs, and hair thinning decreases hair diameters linearly with time. Our system is biologically inspired, supports facial hair, both genders and many ethnicities, and is compatible with different lengths of hair strands. Our real-time results resemble real-life hair aging, accomplished by simulating the stochastic nature of the process and the gradual decrease of melanin.
Text2Light
Authors: Chen Zhaoxi , Wang Guangcong , Liu Ziwei
Published: November 2022

High-quality HDRIs (High Dynamic Range Images), typically HDR panoramas, are one of the most popular ways to create photorealistic lighting and 360-degree reflections of 3D scenes in graphics. Given the difficulty of capturing HDRIs, a versatile and controllable generative model is highly desired, where layman users can intuitively control the generation process. However, existing state-of-the-art methods still struggle to synthesize high-quality panoramas for complex scenes. In this work, we propose a zero-shot text-driven framework,
Text2Light
, to generate 4K+ resolution HDRIs without paired training data. Given a free-form text as the description of the scene, we synthesize the corresponding HDRI with two dedicated steps:
1
) text-driven panorama generation in low dynamic range (LDR) and low resolution (LR), and
2
) super-resolution inverse tone mapping to scale up the LDR panorama both in resolution and dynamic range. Specifically, to achieve zero-shot text-driven panorama generation, we first build dual codebooks as the discrete representation for diverse environmental textures. Then, driven by the pre-trained Contrastive Language-Image Pre-training (CLIP) model, a text-conditioned global sampler learns to sample holistic semantics from the global codebook according to the input text. Furthermore, a structure-aware local sampler learns to synthesize LDR panoramas patch-by-patch, guided by holistic semantics. To achieve super-resolution inverse tone mapping, we derive a continuous representation of 360-degree imaging from the LDR panorama as a set of structured latent codes anchored to the sphere. This continuous representation enables a versatile module to upscale the resolution and dynamic range simultaneously. Extensive experiments demonstrate the superior capability of Text2Light in generating high-quality HDR panoramas. In addition, we show the feasibility of our work in realistic rendering and immersive VR.
Dressing Avatars
Authors: Xiang Donglai , Bagautdinov Timur , Stuyck Tuur , Prada Fabian , Romero Javier , Xu Weipeng , Saito Shunsuke , Guo Jingfan , Smith Breannan , Shiratori Takaaki , Sheikh Yaser , Hodgins Jessica , Wu Chenglei
Published: November 2022

Despite recent progress in developing animatable full-body avatars, realistic modeling of clothing - one of the core aspects of human self-expression - remains an open challenge. State-of-the-art physical simulation methods can generate realistically behaving clothing geometry at interactive rates. Modeling photorealistic appearance, however, usually requires physically-based rendering which is too expensive for interactive applications. On the other hand, data-driven deep appearance models are capable of efficiently producing realistic appearance, but struggle at synthesizing geometry of highly dynamic clothing and handling challenging body-clothing configurations. To this end, we introduce pose-driven avatars with explicit modeling of clothing that exhibit both photorealistic appearance learned from real-world data and realistic clothing dynamics. The key idea is to introduce a neural clothing appearance model that operates on top of explicit geometry: at training time we use high-fidelity tracking, whereas at animation time we rely on physically simulated geometry. Our core contribution is a physically-inspired appearance network, capable of generating photorealistic appearance with view-dependent and dynamic shadowing effects even for unseen body-clothing configurations. We conduct a thorough evaluation of our model and demonstrate diverse animation results on several subjects and different types of clothing. Unlike previous work on photorealistic full-body avatars, our approach can produce much richer dynamics and more realistic deformations even for many examples of loose clothing. We also demonstrate that our formulation naturally allows clothing to be used with avatars of different people while staying fully animatable, thus enabling, for the first time, photorealistic avatars with novel clothing.
Hidden Degrees of Freedom in Implicit Vortex Filaments
Authors: Ishida Sadashige , Wojtan Chris , Chern Albert
Published: November 2022

This paper presents a new representation of curve dynamics, with applications to vortex filaments in fluid dynamics. Instead of representing these filaments with explicit curve geometry and Lagrangian equations of motion, we represent curves
implicitly
with a new co-dimensional 2 level set description. Our implicit representation admits several redundant mathematical degrees of freedom in both the configuration and the dynamics of the curves, which can be tailored specifically to improve numerical robustness, in contrast to naive approaches for implicit curve dynamics that suffer from overwhelming numerical stability problems. Furthermore, we note how these hidden degrees of freedom perfectly map to a
Clebsch representation
in fluid dynamics. Motivated by these observations, we introduce
untwisted
level set functions and
non-swirling
dynamics which successfully regularize sources of numerical instability, particularly in the twisting modes around curve filaments. A consequence is a novel simulation method which produces stable dynamics for large numbers of interacting vortex filaments and effortlessly handles topological changes and re-connection events.
An Implicit Parametric Morphable Dental Model
Authors: Zhang Congyi , Elgharib Mohamed , Fox Gereon , Gu Min , Theobalt Christian , Wang Wenping
Published: November 2022

3D Morphable models of the human body capture variations among subjects and are useful in reconstruction and editing applications. Current dental models use an explicit mesh scene representation and model only the teeth, ignoring the gum. In this work, we present the first parametric 3D morphable dental model for both teeth and gum. Our model uses an implicit scene representation and is learned from rigidly aligned scans. It is based on a component-wise representation for each tooth and the gum, together with a learnable latent code for each of such components. It also learns a template shape thus enabling several applications such as segmentation, interpolation and tooth replacement. Our reconstruction quality is on par with the most advanced global implicit representations while enabling novel applications. The code will be available at https://github.com/cong-yi/DMM
3QNet
Authors: Huang Tianxin , Zhang Jiangning , Chen Jun , Ding Zhonggan , Tai Ying , Zhang Zhenyu , Wang Chengjie , Liu Yong
Published: November 2022

Since the development of 3D applications, the point cloud, as a spatial description easily acquired by sensors, has been widely used in multiple areas such as SLAM and 3D reconstruction. Point Cloud Compression (PCC) has also attracted more attention as a primary step before point cloud transferring and saving, where the geometry compression is an important component of PCC to compress the points geometrical structures. However, existing non-learning-based geometry compression methods are often limited by manually pre-defined compression rules. Though learning-based compression methods can significantly improve the algorithm performances by learning compression rules from data, they still have some defects. Voxel-based compression networks introduce precision errors due to the voxelized operations, while point-based methods may have relatively weak robustness and are mainly designed for sparse point clouds. In this work, we propose a novel learning-based point cloud compression framework named 3D Point Cloud Geometry Quantiation Compression Network (3QNet), which overcomes the robustness limitation of existing point-based methods and can handle dense points. By learning a codebook including common structural features from simple and sparse shapes, 3QNet can efficiently deal with multiple kinds of point clouds. According to experiments on object models, indoor scenes, and outdoor scans, 3QNet can achieve better compression performances than many representative methods.
Scalable Multi-Class Sampling via Filtered Sliced Optimal Transport
Authors: Salaün Corentin , Georgiev Iliyan , Seidel Hans-Peter , Singh Gurprit
Published: November 2022

We propose a multi-class point optimization formulation based on continuous Wasserstein barycenters. Our formulation is designed to handle hundreds to thousands of optimization objectives and comes with a practical optimization scheme. We demonstrate the effectiveness of our framework on various sampling applications like stippling, object placement, and Monte-Carlo integration. We a derive multi-class error bound for perceptual rendering error which can be minimized using our optimization. We provide source code at https://github.com/iribis/filtered-sliced-optimal-transport.
Simulation of Hand Anatomy Using Medical Imaging
Authors: Zheng Mianlun , Wang Bohan , Huang Jingtao , Barbič Jernej
Published: November 2022

Precision modeling of the hand internal musculoskeletal anatomy has been largely limited to individual poses, and has not been connected into continuous volumetric motion of the hand anatomy actuating across the hand's entire range of motion. This is for a good reason, as hand anatomy and its motion are extremely complex and cannot be predicted merely from the anatomy in a single pose. We give a method to simulate the volumetric shape of hand's musculoskeletal organs to any pose in the hand's range of motion, producing external hand shapes and internal organ shapes that match ground truth optical scans and medical images (MRI) in multiple scanned poses. We achieve this by combining MRI images in multiple hand poses with FEM multibody nonlinear elastoplastic simulation. Our system models bones, muscles, tendons, joint ligaments and fat as separate volumetric organs that mechanically interact through contact and attachments, and whose shape matches medical images (MRI) in the MRI-scanned hand poses. The match to MRI is achieved by incorporating pose-space deformation and plastic strains into the simulation. We show how to do this in a non-intrusive manner that still retains all the simulation benefits, namely the ability to prescribe realistic material properties, generalize to arbitrary poses, preserve volume and obey contacts and attachments. We use our method to produce volumetric renders of the internal anatomy of the human hand in motion, and to compute and render highly realistic hand surface shapes. We evaluate our method by comparing it to optical scans, and demonstrate that we qualitatively and quantitatively substantially decrease the error compared to previous work. We test our method on five complex hand sequences, generated either using keyframe animation or performance animation using modern hand tracking techniques.
Learning Virtual Chimeras by Dynamic Motion Reassembly
Authors: Lee Seyoung , Lee Jiye , Lee Jehee
Published: November 2022

The
Chimera
is a mythological hybrid creature composed of different animal parts. The chimera's movements are highly dependent on the spatial and temporal alignments of its composing parts. In this paper, we present a novel algorithm that creates and animates chimeras by dynamically reassembling source characters and their movements. Our algorithm exploits a two-network architecture: part assembler and dynamic controller. The part assembler is a supervised learning layer that searches for the spatial alignment among body parts, assuming that the temporal alignment is provided. The dynamic controller is a reinforcement learning layer that learns robust control policy for a wide variety of potential temporal alignments. These two layers are tightly intertwined and learned simultaneously. The chimera animation generated by our algorithm is energy efficient and expressive in terms of describing weight shifting, balancing, and full-body coordination. We demonstrate the versatility of our algorithm by generating the motor skills of a large variety of chimeras from limited source characters.
Differentiable Hybrid Traffic Simulation
Authors: Son Sanghyun , Qiao Yi-Ling , Sewall Jason , Lin Ming C.
Published: November 2022

We introduce a novel
differentiable hybrid traffic simulator
, which simulates traffic using a hybrid model of both macroscopic and microscopic models and can be directly
integrated into a neural network for traffic control and flow optimization.
This is the first differentiable traffic simulator for macroscopic and hybrid models that can compute gradients for traffic states across time steps and inhomogeneous lanes. To compute the gradient flow between two types of traffic models in a hybrid framework, we present a novel intermediate conversion component that bridges the lanes in a differentiable manner as well. We also show that we can use analytical gradients to accelerate the overall process and enhance scalability. Thanks to these gradients, our simulator can provide more efficient and scalable solutions for complex learning and control problems posed in traffic engineering than other existing algorithms. Refer to https://sites.google.com/umd.edu/diff-hybrid-traffic-sim for our project.
Neural James-Stein Combiner for Unbiased and Biased Renderings
Authors: Gu Jeongmin , Iglesias-Guitian Jose A. , Moon Bochang
Published: November 2022

Unbiased rendering algorithms such as path tracing produce accurate images given a huge number of samples, but in practice, the techniques often leave visually distracting artifacts (i.e., noise) in their rendered images due to a limited time budget. A favored approach for mitigating the noise problem is applying learning-based denoisers to unbiased but noisy rendered images and suppressing the noise while preserving image details. However, such denoising techniques typically introduce a systematic error, i.e., the denoising bias, which does not decline as rapidly when increasing the sample size, unlike the other type of error, i.e., variance. It can technically lead to slow numerical convergence of the denoising techniques. We propose a new combination framework built upon the James-Stein (JS) estimator, which merges a pair of unbiased and biased rendering images, e.g., a path-traced image and its denoised result. Unlike existing post-correction techniques for image denoising, our framework helps an input denoiser have lower errors than its unbiased input without relying on accurate estimation of per-pixel denoising errors. We demonstrate that our framework based on the well-established JS theories allows us to improve the error reduction rates of state-of-the-art learning-based denoisers more robustly than recent post-denoisers.