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Determining the behaviour of convection and clouds is one of the biggest challenges in our understanding of exoplanetary climates. Given the lack of in situ observations, one of the most preferable approaches is to use cloud-resolving or cloud-permitting models (CPM). Here we present CPM simulations in a quasi-global domain with high spatial resolution (4$\times$4 km grid) and explicit convection to study the cloud regime of 1 to 1 tidally locked rocky planets orbiting around low-mass stars. We show that the substellar region is covered by deep convective clouds and cloud albedo increases with increasing stellar flux. The CPM produces relatively less cloud liquid water concentration, smaller cloud coverage, lower cloud albedo, and deeper H2O spectral features than previous general circulation model (GCM) simulations employing empirical convection and cloud parameterizations. Furthermore, cloud streets--long bands of low-level clouds oriented nearly parallel to the direction of the mean boundary-layer winds--appear in the CPM and substantially affect energy balance and surface precipitation at a local level.

The ability to dynamically extend a model to new data and classes is critical for multiple organ and tumor segmentation. However, due to privacy regulations, accessing previous data and annotations can be problematic in the medical domain. This poses a significant barrier to preserving the high segmentation accuracy of the old classes when learning from new classes because of the catastrophic forgetting problem. In this paper, we first empirically demonstrate that simply using high-quality pseudo labels can fairly mitigate this problem in the setting of organ segmentation. Furthermore, we put forward an innovative architecture designed specifically for continuous organ and tumor segmentation, which incurs minimal computational overhead. Our proposed design involves replacing the conventional output layer with a suite of lightweight, class-specific heads, thereby offering the flexibility to accommodate newly emerging classes. These heads enable independent predictions for newly introduced and previously learned classes, effectively minimizing the impact of new classes on old ones during the course of continual learning. We further propose incorporating Contrastive Language-Image Pretraining (CLIP) embeddings into the organ-specific heads. These embeddings encapsulate the semantic information of each class, informed by extensive image-text co-training. The proposed method is evaluated on both in-house and public abdominal CT datasets under organ and tumor segmentation tasks. Empirical results suggest that the proposed design improves the segmentation performance of a baseline neural network on newly-introduced and previously-learned classes along the learning trajectory., MICCAI-2023

For a nondegenerate $r$-graph $F$, large $n$, and $t$ in the regime $[0, c_{F} n]$, where $c_F>0$ is a constant depending only on $F$, we present a general approach for determining the maximum number of edges in an $n$-vertex $r$-graph that does not contain $t+1$ vertex-disjoint copies of $F$. In fact, our method results in a rainbow version of the above result and includes a characterization of the extremal constructions. Our approach applies to many well-studied hypergraphs (including graphs) such as the edge-critical graphs, the Fano plane, the generalized triangles, hypergraph expansions, the expanded triangles, and hypergraph books. Our results extend old results of Simonovits~\cite{SI68} and Moon~\cite{Moon68} on complete graphs and can be viewed as a step towards a general density version of the classical Corr\'{a}di--Hajnal Theorem~\cite{CH63}., Comment: corrected some typo and added a theorem (THM 2.7) about expanded Erdos-Sos trees

Current performance analysis processes for drag-dominant tidal turbines are unsuitable as disk actuator theory lacks support for varying swept blockage area, bypass flow downstream interaction, and parasitic rotor drag, whereas blade element momentum theory is computably effective for three-blade lift-dominated aerofoil. This study proposes a novel technique to calculate the optimal turbine tip speed ratio (TSR) with a cost-effective and user-friendly moment balancing algorithm. A reliable dynamic TSR matrix was developed with varying rotational speeds and fluid velocities, unlike previous works simulated at a fixed fluid velocity. Thrust and idle moments are introduced as functions of inlet fluid velocity and rotational speed, respectively. The quadratic relationships are verified through regression analysis, and net moment equations are established. Rotational speed was a reliable predictor for Pinwheel’s idle moment, while inlet velocity was a reliable predictor for thrust moment for both models. The optimal (Cp, TSR) values for Pinwheel and Savonius turbines were (0.223, 2.37) and (0.63, 0.29), respectively, within an acceptable error range for experimental validation. This study aims to improve prevailing industry practices by enhancing an engineer’s understanding of optimal blade design by adjusting the rotor speed to suit the inlet flow case compared to ‘trial and error’ with cost-intensive simulations. Published version The authors would like to thank Nanyang Technological University for providing the computing facilities needed to carry out this study, as well as the Interdisciplinary Graduate School scholarship for funding this project.

For a nondegenerate $r$-graph $F$, large $n$, and $t$ in the regime $[0, c_{F} n]$, where $c_F>0$ is a constant depending only on $F$, we present a general approach for determining the maximum number of edges in an $n$-vertex $r$-graph that does not contain $t+1$ vertex-disjoint copies of $F$. In fact, our method results in a rainbow version of the above result and includes a characterization of the extremal constructions. Our approach applies to many well-studied hypergraphs (including graphs) such as the edge-critical graphs, the Fano plane, the generalized triangles, hypergraph expansions, the expanded triangles, and hypergraph books. Our results extend old results of Simonovits~\cite{SI68} and Moon~\cite{Moon68} on complete graphs and can be viewed as a step towards a general density version of the classical Corrádi--Hajnal Theorem~\cite{CH63}., corrected some typo and added a theorem (THM 2.7) about expanded Erdos-Sos trees

Music contains hierarchical structures beyond beats and measures. While hierarchical structure annotations are helpful for music information retrieval and computer musicology, such annotations are scarce in current digital music databases. In this paper, we explore a data-driven approach to automatically extract hierarchical metrical structures from scores. We propose a new model with a Temporal Convolutional Network-Conditional Random Field (TCN-CRF) architecture. Given a symbolic music score, our model takes in an arbitrary number of voices in a beat-quantized form, and predicts a 4-level hierarchical metrical structure from downbeat-level to section-level. We also annotate a dataset using RWC-POP MIDI files to facilitate training and evaluation. We show by experiments that the proposed method performs better than the rule-based approach under different orchestration settings. We also perform some simple musicological analysis on the model predictions. All demos, datasets and pre-trained models are publicly available on Github., Accepted at the International Society for Music Information Retrieval (ISMIR), 2022

In this study, we explore the representation mapping from the domain of visual arts to the domain of music, with which we can use visual arts as an effective handle to control music generation. Unlike most studies in multimodal representation learning that are purely data-driven, we adopt an analysis-by-synthesis approach that combines deep music representation learning with user studies. Such an approach enables us to discover \textit{interpretable} representation mapping without a huge amount of paired data. In particular, we discover that visual-to-music mapping has a nice property similar to equivariant. In other words, we can use various image transformations, say, changing brightness, changing contrast, style transfer, to control the corresponding transformations in the music domain. In addition, we released the Vis2Mus system as a controllable interface for symbolic music generation., Submitted to ICASSP 2023. GitHub repo: https://github.com/ldzhangyx/vis2mus

An $\mathcal{F}$-saturated $r$-graph is a maximal $r$-graph not containing any member of $\mathcal{F}$ as a subgraph. Let $\mathcal{K}_{\ell + 1}^{r}$ be the collection of all $r$-graphs $F$ with at most $\binom{\ell+1}{2}$ edges such that for some $\left(\ell+1\right)$-set $S$ every pair $\{u, v\} \subset S$ is covered by an edge in $F$. Our first result shows that for each $\ell \geq r \geq 2$ every $\mathcal{K}_{\ell+1}^{r}$-saturated $r$-graph on $n$ vertices with $t_{r}(n, \ell) - o(n^{r-1+1/\ell})$ edges contains a complete $\ell$-partite subgraph on $(1-o(1))n$ vertices, which extends a stability theorem for $K_{\ell+1}$-saturated graphs given by Popielarz, Sahasrabudhe and Snyder. We also show that the bound is best possible. Our second result is motivated by a celebrated theorem of Andr\'{a}sfai, Erd\H{o}s and S\'{o}s which states that for $\ell \geq 2$ every $K_{\ell+1}$-free graph $G$ on $n$ vertices with minimum degree $\delta(G) > \frac{3\ell-4}{3\ell-1}n$ is $\ell$-partite. We give a hypergraph version of it. The \emph{minimum positive co-degree} of an $r$-graph $\mathcal{H}$, denoted by $\delta_{r-1}^{+}(\mathcal{H})$, is the maximum $k$ such that if $S$ is an $(r-1)$-set contained in a edge of $\mathcal{H}$, then $S$ is contained in at least $k$ distinct edges of $\mathcal{H}$. Let $\ell\ge 3$ be an integer and $\mathcal{H}$ be a $\mathcal{K}_{\ell+1}^3$-saturated $3$-graph on $n$ vertices. We prove that if either $\ell \ge 4$ and $\delta_{2}^{+}(\mathcal{H}) > \frac{3\ell-7}{3\ell-1}n$; or $\ell = 3$ and $\delta_{2}^{+}(\mathcal{H}) > 2n/7$, then $\mathcal{H}$ is $\ell$-partite; and the bound is best possible. This is the first stability result on minimum positive co-degree for hypergraphs.

The Sun becomes brighter with time, but Earth's climate is roughly temperate for life during its long-term history; for early Earth, this is known as the Faint Young Sun Problem (FYSP). Besides the carbonate-silicate feedback, recent researches suggest that a long-term cloud feedback may partially solve the FYSP. However, the general circulation models they used cannot resolve convection and clouds explicitly. This study re-investigates the clouds using a near-global cloud-permitting model without cumulus convection parameterization. Our results confirm that a stabilizing shortwave cloud feedback does exist, and its magnitude is $\approx$6 W m$^{-2}$ or 14% of the energy required to offset a 20% fainter Sun than today, or $\approx$10 W m$^{-2}$ or 16% for a 30% fainter Sun. When insolation increases and meanwhile CO$_2$ concentration decreases, low-level clouds increase, acting to stabilize the climate by raising planetary albedo, and vice versa.

Let $r\ge 2$ be an integer. The real number $\alpha\in [0,1]$ is a jump for $r$ if there exists a constant $c > 0$ such that for any $\epsilon >0$ and any integer $m \geq r$, there exists an integer $n_0(\epsilon, m)$ satisfying any $r$-uniform graph with $n\ge n_0(\epsilon, m)$ vertices and density at least $\alpha+\epsilon$ contains a subgraph with $m$ vertices and density at least $\alpha+c$. A result of Erd\H{o}s, Stone and Simonovits implies that every $\alpha\in [0,1)$ is a jump for $r=2$. Erd\H{o}s asked whether the same is true for $r\ge 3$. Frankl and R\"{o}dl gave a negative answer by showing that $1-\frac{1}{l^{r-1}}$ is not a jump for $r$ if $r\ge 3$ and $l>2r$. After that, more non-jumps are found using a method of Frankl and R\"{o}dl. In this note, we show a method to construct maps $f \colon [0,1] \to [0,1]$ that preserve non-jumps, if $\alpha$ is a non-jump for $r$ given by the method of Frankl and R\"{o}dl, then $f(\alpha)$ is also a non-jump for $r$. We use these maps to study hypergraph Tur\'{a}n densities and answer a question posed by Grosu.

We give the first exact and stability results for a hypergraph Tur\'{a}n problem with infinitely many extremal constructions that are far from each other in edit-distance. This includes an example of triple systems with Tur\'{a}n density $2/9$, thus answering some questions posed by the third and fourth authors and Reiher about the feasible region of hypergraphs. Our results also provide extremal constructions whose shadow density is a transcendental number. Our novel approach is to construct certain multilinear polynomials that attain their maximum (in the standard simplex) on a line segment and then to use these polynomials to define an operation on hypergraphs that gives extremal constructions., Comment: Minor change

Adversarial Propagation (AdvProp) is an effective way to improve recognition models, leveraging adversarial examples. Nonetheless, AdvProp suffers from the extremely slow training speed, mainly because: a) extra forward and backward passes are required for generating adversarial examples; b) both original samples and their adversarial counterparts are used for training (i.e., 2$\times$ data). In this paper, we introduce Fast AdvProp, which aggressively revamps AdvProp's costly training components, rendering the method nearly as cheap as the vanilla training. Specifically, our modifications in Fast AdvProp are guided by the hypothesis that disentangled learning with adversarial examples is the key for performance improvements, while other training recipes (e.g., paired clean and adversarial training samples, multi-step adversarial attackers) could be largely simplified. Our empirical results show that, compared to the vanilla training baseline, Fast AdvProp is able to further model performance on a spectrum of visual benchmarks, without incurring extra training cost. Additionally, our ablations find Fast AdvProp scales better if larger models are used, is compatible with existing data augmentation methods (i.e., Mixup and CutMix), and can be easily adapted to other recognition tasks like object detection. The code is available here: https://github.com/meijieru/fast_advprop., Comment: ICLR 2022 camera ready version

Radiography imaging protocols focus on particular body regions, therefore producing images of great similarity and yielding recurrent anatomical structures across patients. To exploit this structured information, we propose the use of Space-aware Memory Queues for In-painting and Detecting anomalies from radiography images (abbreviated as SQUID). We show that SQUID can taxonomize the ingrained anatomical structures into recurrent patterns; and in the inference, it can identify anomalies (unseen/modified patterns) in the image. SQUID surpasses 13 state-of-the-art methods in unsupervised anomaly detection by at least 5 points on two chest X-ray benchmark datasets measured by the Area Under the Curve (AUC). Additionally, we have created a new dataset (DigitAnatomy), which synthesizes the spatial correlation and consistent shape in chest anatomy. We hope DigitAnatomy can prompt the development, evaluation, and interpretability of anomaly detection methods., CVPR 2023

While neural symbolic methods demonstrate impressive performance in visual question answering on synthetic images, their performance suffers on real images. We identify that the long-tail distribution of visual concepts and unequal importance of reasoning steps in real data are the two key obstacles that limit the models' real-world potentials. To address these challenges, we propose a new paradigm, Calibrating Concepts and Operations (CCO), which enables neural symbolic models to capture underlying data characteristics and to reason with hierarchical importance. Specifically, we introduce an executor with learnable concept embedding magnitudes for handling distribution imbalance, and an operation calibrator for highlighting important operations and suppressing redundant ones. Our experiments show CCO substantially boosts the performance of neural symbolic methods on real images. By evaluating models on the real world dataset GQA, CCO helps the neural symbolic method NSCL outperforms its vanilla counterpart by 9.1% (from 47.0% to 56.1%); this result also largely reduces the performance gap between symbolic and non-symbolic methods. Additionally, we create a perturbed test set for better understanding and analyzing model performance on real images. Code is available at https://github.com/Lizw14/CaliCO.git ., Comment: To appear in ICCV2021; Code at https://github.com/Lizw14/CaliCO.git

The field of computational pathology has witnessed great advancements since deep neural networks have been widely applied. These networks usually require large numbers of annotated data to train vast parameters. However, it takes significant effort to annotate a large histopathology dataset. We introduce a light-weight and interpretable model for nuclei detection and weakly-supervised segmentation. It only requires annotations on isolated nucleus, rather than on all nuclei in the dataset. Besides, it is a generative compositional model that first locates parts of nucleus, then learns the spatial correlation of the parts to further locate the nucleus. This process brings interpretability in its prediction. Empirical results on an in-house dataset show that in detection, the proposed method achieved comparable or better performance than its deep network counterparts, especially when the annotated data is limited. It also outperforms popular weakly-supervised segmentation methods. The proposed method could be an alternative solution for the data-hungry problem of deep learning methods.

Recent discoveries in both investigating and modulating cells have enriched our understanding and knowledge about cellular behavior, signaling pathways as well as molecular mechanisms. In addition, tremendous advances in nanomaterials, especially nanomaterial-based optical probes, have enabled scientists to not only study various molecular, cellular, cell-environment interactions at the nanoscale level, but also interrogate, perturb as well as manipulate biological behaviors at different scale functionally and therapeutically. As a result, nanotechnology-based optical investigation and manipulation at the biological and neural interfaces have gained great research interests and attentions for the purpose of understanding studying physiological mechanisms, identifying pathogenic pathways, and developing enhanced treatments of devastating diseases.In the introductory chapter, the discussion is focused on recent advances of nano-optical probe-based cellular sensing and manipulations. Firstly, the nanoprobe-based optical sensing of cellular environments is elaborated, followed by detailed discussion on applications in cellular activities investigation. Finally, light-mediated nanoparticle-based cellular manipulations and behavior controls is discussed. To better investigate and further regulate the complex biological processes, near infrared (NIR) could be an ideal stimulus for modulating the spatiotemporal dynamics of signals in living cells and organisms with high resolution. Subsequently, initial work on the development of NIR-responsive lanthanide-doped nanomaterials with orthogonal emissions for dynamic photo-switching reaction control is presented. Two studies utilizing NIR-responsive nanomaterials for small molecule release monitoring and neurotransmitter imaging are stated. These works represent the sensing aspects of photo-responsive nanomaterials probing at the cancer and neural interfaces. Two proof-of-concept studies on NIR-mediated stem cell behaviors control is incorporated in the later chapters. Lastly, in the final chapter, recent works on biodegradable dendritic peptide nanoparticle platform for regenerative medicine and cancer therapeutic applications are discussed. Moreover, the translational journey and perspectives of the dendritic peptide nanoparticle system for next-generation pancreatic cancer therapy are presented.

The elaboration of polarity is central to organismal development and to the maintenance of functional epithelia. Among the controls determining polarity are the PAR proteins, PAR6, aPKCι and PAR3, regulating both known and unknown effectors. Here, we identify FARP2 as a 'RIPR' motif-dependent partner and substrate of aPKCι that is required for efficient polarisation and junction formation. Binding is conferred by a FERM/FA domain-kinase domain interaction and detachment promoted by aPKCι-dependent phosphorylation. FARP2 is shown to promote GTP loading of Cdc42, which is consistent with it being involved in upstream regulation of the polarising PAR6-aPKCι complex. However, we show that aPKCι acts to promote the localised activity of FARP2 through phosphorylation. We conclude that this aPKCι-FARP2 complex formation acts as a positive feedback control to drive polarisation through aPKCι and other Cdc42 effectors.This article has an associated First Person interview with the first author of the paper.

Objectives To observe the correlation between inflammatory factor monocyte chemotactic protein -1 and high sensitivity C-reactive protein (hs-CRP) in hypertrophic cardiomyopathy (HCM) patients. Methods 286 cases of patients with HCM were selected from two hospitals from January 2005 to December 2010. HCM diagnostic criteria in accordance with the ACC/ESC expert consensus hypertrophic cardiomyopathy (2003). Records of all patients age, sex, medical history, NYHA classification of cardiac function, the main indicators of ECG and echocardiography to exclude diabetes, infectious diseases, cancer, collagen diseases, application of immunosuppressive drugs. The contents of MCP-1 and hs-CRP in all patients were determined by ELISA. Results The median value of MCP-1 of 286 patients was 370.28 ng/l (157.21±439.47 ng/l); hs-CRP median value was 1.97 mg/l (1.12±2.75 mg/l), two indicators were higher than normal. 89 cases (31.1%) underwent coronary angiography showed that 21 cases (7.3%) had coronary heart disease, these patients and those without coronary heart disease had no statistically significant difference between the two indicators. The levels of MCP-1 and hs-CRP increased associated with the largest increase in wall thickness (p Conclusions HCM may be associated with the inflammatory response, particularly in those patients with wall thickening, significantly T wave changes, chest pain or syncope symptoms.

Objective To observe the prognosis and therapeutic effect of atorvastatin on paroxysmal atrial fibrillation in patients with non-valvular disease and effects of atorvastatin on inflammatory factors. Methods 156 patients with non-valvular disease complicated with paroxysmal atrial fibrillation were randomly divided into two groups: treatment group [80 cases, treated by atorvastatin (20 mg/day); control group (76 cases). No significant differences between two groups of age, sex, hypertension, diabetes, heart function treatment. A treatment course was 6 months for two groups. Serum inflammatory cytokine interleukin 6 (IL-6), tumour necrosis factor α (TNFα), high sensitive C reactive protein (hs-CRP), human atrial natriuretic peptide (ANP) and sinus rhythm maintenance rate, the incidence of thromboembolic events in both groups were observed. Results The levels of IL-6, TNFα, hs-CRP and ANP in treatment group was significantly decreased than that of control group (p