Miracle-dz/UniFAR-FTUs huggingface dataset:
Perspective: Assessing Tolerance to Nondigestible Carbohydrate Consumption

ABSTRACT Human intestinal enzymes do not hydrolyze nondigestible carbohydrates (NDCs), and thus, they are not digested and absorbed in the small intestine. Instead, NDCs are partially to completely fermented by the intestinal microbiota. Select NDCs are associated with health benefits such as laxation and lowering of blood cholesterol and glucose. NDCs provide functional attributes to processed foods, including sugar or fat replacers, thickening agents, and bulking agents. Additionally, NDCs are incorporated into processed foods to increase their fiber content. Although consumption of NDCs can benefit health and contribute functional characteristics to foods, they can cause gastrointestinal symptoms, such as flatulence and bloating. As gastrointestinal symptoms negatively affect consumer well-being and their acceptance of foods containing NDC ingredients, it is crucial to consider tolerance when designing food products and testing their physiological health benefits in clinical trials. This perspective provides recommendations for the approach to assess gastrointestinal tolerance to NDCs, with a focus on study design, population criteria, intervention, comparator, and outcome. Special issues related to studies in children and implications for stakeholders are also discussed. It is recommended that the evaluation of gastrointestinal tolerance to NDCs be conducted in randomized, blinded, controlled crossover studies using standard gastrointestinal questionnaires, with attention to study participant background diets, health status, lifestyle, and medications.
Enumeration of Certain Classes of $T_0$-hypergraphs

A hypergraph is a $T_0$-hypergraph if for every two different vertices of the hypergraph there exists an edge containing one of the vertices and not containing the other. A general method for the enumeration of certain classes of $T_0$-hypergraphs is given. $T_0$-hypergraphs that are considered here are singled out both by the properties they themselves satisfy and by the properties that dual hypergraphs associated with them satisfy. Though in case of the so-called ordered hipergraphs the property `to be a $T_0$-hypergraph' is reduced to the property `to having different columns' of corresponding matrices, combining this property with some properties, that we are considering here, gives sometimes classes of hypergraphs that are not so easy to enumerate. The problem of enumerating some of thus obtained classes remains unsolved. Special attention is devoted to enumerating of different classes of covers and connected hypergraphs.
Biofabrication of a tri-layered 3D-bioprinted CSC-based malignant melanoma model for personalized cancer treatment

Conventional in vitro cancer models do not accurately reproduce the tumor microenvironment (TME), so three-dimensional (3D)-bioprinting represents an excellent tool to overcome their limitations. Here, two multicellular tri-layered malignant melanoma (MM) models composed by cancer stem cells (CSCs) isolated from a MM established cell line or a primary-patient derived cell line, fibroblasts, mesenchymal stem cells, and endothelial cells, embedded within an agarose-collagen type I hydrogel were bioprinted. Embedded-cells showed high proliferation and metabolic activity, and actively remodeled their TME. MM hydrogels displayed similar rheological properties that skin and were able to support an early onset of vascularization. Besides, MM hydrogels displayed different response to vemurafenib compared with cell cultures, and supported tumorigenesis in murine xenotransplant achieving more mimetic in vivo models. For the first time a tri-layered 3D-bioprinted CSC-based human MM model is developed recreating TME in vitro and in vivo and response to treatment, being useful for precision treatment regimens against MM.
Retaliatory killing negatively affects African lion (Panthera leo) male coalitions in the Tarangire-Manyara Ecosystem, Tanzania

In landscapes where people and lions coexist, conflicts are common due to livestock predation and threats to human safety. Retaliatory lion killing by humans is often a consequence and is one of the leading causes of lion population declines across Africa. We assessed the effects of retaliatory lion killing on male lion coalitions in the Tarangire-Manyara Ecosystem (TME) using a long-term dataset of lion monitoring for ten lion prides, spanning over a fourteen year-period from 2004–2018. We also interviewed 214 respondents about their attitudes and awareness of the effects of retaliatory killing on lions. We found that male lion coalitions were larger and lasted for a longer tenure period in locations with low risk of retaliatory killing, as well as far away from active hunting blocks. Further, young people (18–35 years old) had a more positive attitude towards lion existence and conservation compared to older age classes. Surprisingly, people with primary or secondary level of education were more likely to having lions killed if they attack livestock compared to people with no formal education, although the former supported lion presence for tourism in protected areas. We conclude that retaliatory killing has a large effect on long-term lion coalition dynamics and, thus, survival. Community awareness on retaliation effect varies widely, and we recommend implementing better education and policy strategies at TME to protect the declining carnivore populations.
Maternal vitamin D and growth of under-five children: a systematic review and meta-analysis of observational and interventional studies

ABSTRACT Background Even though previous systematic reviews have reported on the role of prenatal vitamin D on birth outcomes, its effect on child growth is poorly understood. Objective To synthesize a systematic summary of the literature on the effect of maternal vitamin D supplementation on the linear growth of under-five children. Method This study includes studies (both observational and interventional with a control group) that evaluated the effects of prenatal vitamin D status on child linear growth. The mean child length/length for age with 95% confidence interval (CI) was pooled as the weighted mean difference using a random-effects model. A funnel plot was used to assess potential publication bias. Results A total of 45 studies and 66 reports covering a total population of 44,992 (19,683 intervention or high vitamin D group, and 25,309 control or low vitamin D group) were analyzed. Studies spanned from 1977 to 2022. The pooled weighted mean difference was 0.4 cm (95% CI: 0.15–0.65). A subgroup analysis, based on vitamin D supplementation frequency, showed that mothers who supplemented monthly or less frequently had a 0.7 cm (95% CI: 0.2–1.16 cm) longer child. Supplementation with a dose of >2000 international units increased child length at birth. The weighted mean difference was 0.35 cm (95% CI: 0.11–0.58). Conclusion The evidence from this review shows that maternal supplementation of vitamin D is associated with increased birth length. This is apparent at higher doses, low frequency (monthly or less frequent), and during the second/third trimester. It appears that vitamin D supplementation during pregnancy is protective of future growth in under-five children. Clinical trials are needed to establish evidence of effectiveness for the frequency and dose of supplementation.
Caulerpa chemnitzia in Darwin threatening Galapagos coral reefs

Coral reefs are rare in the Galapagos and there is concern that, like in many areas around the world, they may be degrading due to increasing anthropogenic pressure, which can cause changes and reorganizations of structure and function with associated phase shifts. Algae of the genus Caulerpa J.V. Lamouroux, 1809 are known as widespread and persistent marine invaders. They grow rapidly, particularly in disturbed areas where they can opportunistically monopolize substratum and compete with native species, thus reducing biodiversity. Caulerpa chemnitzia increased in abundance and overgrew corals on the reef since 2012, ultimately raising fears that a phase-shift from coral to algae might be imminent. However, from 2019 onwards algae populations strongly contracted and while not having returned to baseline level, there is currently low risk of corals being displaced. Visual censuses were conducted on a yearly basis since 2004 using sample quadrats (0.5 x 0.5m) every 5 m along a 50-m-long transects at a depth of 6–15 m at 5 permanent subtidal ecological monitoring sites around Darwin. In addition, 10 m photo-transects were taken using a graduated meter-long measuring stick in the centre of the frame in 2012, 2014, 2016, 2017, 2018 and 2021 at a depth of 15m at Wellington reef. The authors hypothesize that this species could have expanded its distribution over Wellington Reef because of its known morphological plasticity due to a response to change in the environment, in this case high temperature and low nutrients. As ENSO events are predicted to increase in intensity and frequency due to the impact of climate change it is important to develop and implement a functional alert system. Early Detection Rapid Response (EDRR) protocols are recommended to avoid climate driven Non-Indigenous Species (NIS) entering the GMR or for native species becoming invasive due to warming-related phase shifts.
The Sparse Abstract Machine

We propose the Sparse Abstract Machine (SAM), an abstract machine model for targeting sparse tensor algebra to reconfigurable and fixed-function spatial dataflow accelerators. SAM defines a streaming dataflow abstraction with sparse primitives that encompass a large space of scheduled tensor algebra expressions. SAM dataflow graphs naturally separate tensor formats from algorithms and are expressive enough to incorporate arbitrary iteration orderings and many hardware-specific optimizations. We also present Custard, a compiler from a high-level language to SAM that demonstrates SAM's usefulness as an intermediate representation. We automatically bind from SAM to a streaming dataflow simulator. We evaluate the generality and extensibility of SAM, explore the performance space of sparse tensor algebra optimizations using SAM, and show SAM's ability to represent dataflow hardware.
Sparsification of the regularized magnetic Laplacian with multi-type spanning forests

In this paper, we consider a ${\rm U}(1)$-connection graph, that is, a graph where each oriented edge is endowed with a unit modulus complex number that is conjugated under orientation flip. A natural replacement for the combinatorial Laplacian is then the magnetic Laplacian, an Hermitian matrix that includes information about the graph's connection. Magnetic Laplacians appear, e.g., in the problem of angular synchronization. In the context of large and dense graphs, we study here sparsifiers of the magnetic Laplacian $\Delta$, i.e., spectral approximations based on subgraphs with few edges. Our approach relies on sampling multi-type spanning forests (MTSFs) using a custom determinantal point process, a probability distribution over edges that favours diversity. In a word, an MTSF is a spanning subgraph whose connected components are either trees or cycle-rooted trees. The latter partially capture the angular inconsistencies of the connection graph, and thus provide a way to compress the information contained in the connection. Interestingly, when the connection graph has weakly inconsistent cycles, samples from the determinantal point process under consideration can be obtained \`a la Wilson, using a random walk with cycle popping. We provide statistical guarantees for a choice of natural estimators of the connection Laplacian, and investigate two practical applications of our sparsifiers: ranking with angular synchronization and graph-based semi-supervised learning. From a statistical perspective, a side result of this paper of independent interest is a matrix Chernoff bound with intrinsic dimension, which allows considering the influence of a regularization -- of the form $\Delta + q \mathbb{I}$ with $q>0$ -- on sparsification guarantees.
Model-Free Adaptive Fault-Tolerant Control with Measurement Disturbance

This paper considers model-free adaptive fault tolerant control of nonlinear system with measurement disturbance. Developing fault detection rule and reconfiguring controller and suppressing measurement disturbance’s impact are main focus points. Firstly, a model by using dynamic linearization technique is given. Secondly, to obtain the fault detection rule, a estimator is developed. And an online approximator is developed to acquire fault dynamics. Thirdly, an fault tolerant controller is reconstructed and then the stability analysis of proposed algorithm is given. As measurement disturbance influence tracking error, a modified model-free adaptive fault tolerant controller is utilized to decrease measurement disturbance. Finally, effectiveness is illustrated by simulations.
Batch-Size Independent Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms or Independent Arms

In this paper, we study the combinatorial semi-bandits (CMAB) and focus on reducing the dependency of the batch-size $K$ in the regret bound, where $K$ is the total number of arms that can be pulled or triggered in each round. First, for the setting of CMAB with probabilistically triggered arms (CMAB-T), we discover a novel (directional) triggering probability and variance modulated (TPVM) condition that can replace the previously-used smoothness condition for various applications, such as cascading bandits, online network exploration and online influence maximization. Under this new condition, we propose a BCUCB-T algorithm with variance-aware confidence intervals and conduct regret analysis which reduces the $O(K)$ factor to $O(\log K)$ or $O(\log^2 K)$ in the regret bound, significantly improving the regret bounds for the above applications. Second, for the setting of non-triggering CMAB with independent arms, we propose a SESCB algorithm which leverages on the non-triggering version of the TPVM condition and completely removes the dependency on $K$ in the leading regret. As a valuable by-product, the regret analysis used in this paper can improve several existing results by a factor of $O(\log K)$. Finally, experimental evaluations show our superior performance compared with benchmark algorithms in different applications.
PFilter: Building Persistent Maps through Feature Filtering for Fast and Accurate LiDAR-based SLAM

Simultaneous localization and mapping (SLAM) based on laser sensors has been widely adopted by mobile robots and autonomous vehicles. These SLAM systems are required to support accurate localization with limited computational resources. In particular, point cloud registration, i.e., the process of matching and aligning multiple LiDAR scans collected at multiple locations in a global coordinate framework, has been deemed as the bottleneck step in SLAM. In this paper, we propose a feature filtering algorithm, PFilter, that can filter out invalid features and can thus greatly alleviate this bottleneck. Meanwhile, the overall registration accuracy is also improved due to the carefully curated feature points. We integrate PFilter into the well-established scan-to-map LiDAR odometry framework, F-LOAM, and evaluate its performance on the KITTI dataset. The experimental results show that PFilter can remove about 48.4% of the points in the local feature map and reduce feature points in scan by 19.3% on average, which save 20.9% processing time per frame. In the mean time, we improve the accuracy by 9.4%.
PyTorch Image Quality: Metrics for Image Quality Assessment

Image Quality Assessment (IQA) metrics are widely used to quantitatively estimate the extent of image degradation following some forming, restoring, transforming, or enhancing algorithms. We present PyTorch Image Quality (PIQ), a usability-centric library that contains the most popular modern IQA algorithms, guaranteed to be correctly implemented according to their original propositions and thoroughly verified. In this paper, we detail the principles behind the foundation of the library, describe the evaluation strategy that makes it reliable, provide the benchmarks that showcase the performance-time trade-offs, and underline the benefits of GPU acceleration given the library is used within the PyTorch backend. PyTorch Image Quality is an open source software: https://github.com/photosynthesis-team/piq/.
Failed Goal Aware Hindsight Experience Replay

In multi-goal reinforcement learning for a given environment, agents learn policies to achieve multiple goals by using experiences gained from interactions with the environment. One of the key challenges in this setting is training agents using sparse binary rewards, which can be difficult due to a lack of successful experiences. To address this challenge, hindsight experience replay (HER) generates successful experiences from unsuccessful experiences. However, the process of generating successful experiences from uniformly sampled ones can be inefficient. In this paper, a novel approach called Failed goal Aware HER (FAHER) is proposed to enhance the sampling efficiency. The approach exploits the property of achieved goals in relation to failed goals that are defined as the original goals not achieved. The proposed method involves clustering episodes with different achieved goals using a cluster model and subsequently sampling experiences in the manner of HER. The cluster model is generated by applying a clustering algorithm to failed goals. The proposed method is validated by experiments with three robotic control tasks of the OpenAI gym. The results of experiments demonstrate that the proposed method is more sample efficient and achieves improved performance over baseline approaches.
Regulatory motifs: structural and functional building blocks of genetic computation

Developing and maintaining life requires a lot of computation. This is done by gene regulatory networks. But we have little understanding of how this computation is organized. I show that there is a direct correspondence between the structural and functional building blocks of regulatory networks, which I call regulatory motifs. I derive a simple bound on the range of function that these motifs can perform, in terms of the local network structure. I prove that this range is a small fraction of all possible functions, which severely constrains global network behavior. Part of this restriction is due to redundancy in the function that regulatory motifs can achieve - there are many ways to perform the same task. Regulatory motifs help us understanding how genetic computation is organized and what it can achieve.
Bayesian Optimization-based Combinatorial Assignment

We study the combinatorial assignment domain, which includes combinatorial auctions and course allocation. The main challenge in this domain is that the bundle space grows exponentially in the number of items. To address this, several papers have recently proposed machine learning-based preference elicitation algorithms that aim to elicit only the most important information from agents. However, the main shortcoming of this prior work is that it does not model a mechanism's uncertainty over values for not yet elicited bundles. In this paper, we address this shortcoming by presenting a Bayesian optimization-based combinatorial assignment (BOCA) mechanism. Our key technical contribution is to integrate a method for capturing model uncertainty into an iterative combinatorial auction mechanism. Concretely, we design a new method for estimating an upper uncertainty bound that can be used to define an acquisition function to determine the next query to the agents. This enables the mechanism to properly explore (and not just exploit) the bundle space during its preference elicitation phase. We run computational experiments in several spectrum auction domains to evaluate BOCA's performance. Our results show that BOCA achieves higher allocative efficiency than state-of-the-art approaches.
ELSR: Extreme Low-Power Super Resolution Network For Mobile Devices

With the popularity of mobile devices, e.g., smartphone and wearable devices, lighter and faster model is crucial for the application of video super resolution. However, most previous lightweight models tend to concentrate on reducing lantency of model inference on desktop GPU, which may be not energy efficient in current mobile devices. In this paper, we proposed Extreme Low-Power Super Resolution (ELSR) network which only consumes a small amount of energy in mobile devices. Pretraining and finetuning methods are applied to boost the performance of the extremely tiny model. Extensive experiments show that our method achieves a excellent balance between restoration quality and power consumption. Finally, we achieve a competitive score of 90.9 with PSNR 27.34 dB and power 0.09 W/30FPS on the target MediaTek Dimensity 9000 plantform, ranking 1st place in the Mobile AI&AIM 2022 Real-Time Video Super-Resolution Challenge.
Dual Representation Learning for One-Step Clustering of Multi-View Data

Multi-view data are commonly encountered in data mining applications. Effective extraction of information from multi-view data requires specific design of clustering methods to cater for data with multiple views, which is non-trivial and challenging. In this paper, we propose a novel one-step multi-view clustering method by exploiting the dual representation of both the common and specific information of different views. The motivation originates from the rationale that multi-view data contain not only the consistent knowledge between views but also the unique knowledge of each view. Meanwhile, to make the representation learning more specific to the clustering task, a one-step learning framework is proposed to integrate representation learning and clustering partition as a whole. With this framework, the representation learning and clustering partition mutually benefit each other, which effectively improve the clustering performance. Results from extensive experiments conducted on benchmark multi-view datasets clearly demonstrate the superiority of the proposed method.
Notes on Propagation of 3D Buoyant Fluid-Driven Cracks

Magma-driven fractures are the main mechanism for magma emplacement in the crust. A fundamental question is how the released fluid controls the propagation dynamics and fracture geometry (depth and breadth) in three dimensions. Analog experiments in gelatin have shown that fracture breadth remains nearly stationary when the process in the fracture head (where breadth is controlled) is dominated by solid toughness, whereas viscous fluid dissipation is dominant in the fracture tail. We model propagation of the resulting buoyant, finger-like fracture of stationary breadth with a slowly varying opening along the crack length. The elastic response to fluid loading in a horizontal cross-section is local and can be treated similarly to the classical Perkins-Kern-Nordgren (PKN) model of hydraulic fracturing. The propagation condition for a finger-like crack is based on balancing the global energy release rate due to a unit crack extension with the rock fracture toughness. It allows us to relate the net fluid pressure at the tip to the fracture breadth and rock toughness. Unlike laterally propagating PKN fracture, where breadth is known a priori, the final breadth of a finger-like vertically ascending fracture is a result of processes in the fracture head. Because the head is much more open than the tail, viscous pressure drop in the head can be neglected leading to a 3D analog of Weertman hydrostatic pulse. This requires relaxing the local elasticity assumption of the PKN model in the fracture head. As a result, we resolve the breadth, and then match the viscosity-dominated tail with the three dimensions, toughness-dominated head to obtain a complete closed-form solution. We then analyze the buoyancy-driven fracture propagation in conditions of either continuous injection or finite volume release for sets of parameters representative of low viscosity magma diking.