Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effectiveness of Ruxolitinib for Acute/Chronic Graft-versus-Host Disease in Children: A Retrospective Study

Objective This study aimed to evaluate the effectiveness of Ruxolitinib for acute/chronic graft-versus-host disease in children. Methods This study was a retrospective trial. We analyzed the clinical characteristics of children who responded poorly to previous treatment for graft-versus-host disease (GVHD) and received ruxolitinib treatment after allogeneic hematopoietic stem cell transplantation (allo-HSCT) as an additional or replacement therapy. Results A total of 53 patients were analyzed: aGVHD and cGVHD. The overall response rate (ORR) to ruxolitinib was 75.5%. The ORR was 64.7% (11/17) in the aGVHD group including 6, 5, and 6 patients with partial responses (PRs), complete responses (CRs), and treatment failure, respectively. The ORR was 80.6% (29/36) in the cGVHD group including 10 with CRs and 19 with PRs. Five and 2 patients showed no response and treatment failure, respectively. Four and 14 patients were GVHD recurrence in aGVHD and cGVHD respectively. A total of 14 patients (39%) discontinued steroids and 8 patients (22.2%) reduced steroids. The incidence of obvious adverse events was 94.1% (16/17) in the aGVHD group, which was higher than that in the cGVHD group. Meanwhile, the prognosis of children with cGVHD was superior to that of children with aGVHD after treatment with ruxolitinib. During the ruxolitinib treatment, only 1 patient suffered a relapse of the primary tumor. Eleven patients also suffered transplantation-associated thrombotic microangiopathy (TA-TMA) after allo-HSCT. Conclusion Pediatric patients with GVHD (especially cGVHD) responded well to ruxolitinib treatment. Ruxolitinib can also be used as an alternative treatment for patients with TMA.
Identification and Validation of New Cancer Stem Cell-Related Genes and Their Regulatory microRNAs in Colorectal Cancerogenesis

Significant progress has been made in the last decade in our understanding of the pathogenetic mechanisms of colorectal cancer (CRC). Cancer stem cells (CSC) have gained much attention and are now believed to play a crucial role in the pathogenesis of various cancers, including CRC. In the current study, we validated gene expression of four genes related to CSC, L1TD1, SLITRK6, ST6GALNAC1 and TCEA3, identified in a previous bioinformatics analysis. Using bioinformatics, potential miRNA-target gene correlations were prioritized. In total, 70 formalin-fixed paraffin-embedded biopsy samples from 47 patients with adenoma, adenoma with early carcinoma and CRC without and with lymph node metastases were included. The expression of selected genes and microRNAs (miRNAs) was evaluated using quantitative PCR. Differential expression of all investigated genes and four of six prioritized miRNAs (hsa-miR-199a-3p, hsa-miR-335-5p, hsa-miR-425-5p, hsa-miR-1225-3p, hsa-miR-1233-3p and hsa-miR-1303) was found in at least one group of CRC cancerogenesis. L1TD1, SLITRK6, miR-1233-3p and miR-1225-3p were correlated to the level of malignancy. A negative correlation between miR-199a-3p and its predicted target SLITRK6 was observed, showing potential for further experimental validation in CRC. Our results provide further evidence that CSC-related genes and their regulatory miRNAs are involved in CRC development and progression and suggest that some them, particularly miR-199a-3p and its SLITRK6 target gene, are promising for further validation in CRC.
THE EFFECT OF RHEOLOGICAL COMPLEXITIES ON POWER CONSUMPTION IN AN AERATED, AGITATED VESSEL

Abstract Power for agitation has been measured under aerated and unaerated conditions in a 0.29 m vessel of Rushton dimensions at specific powers up to 18 W/kg. The fluids studied were water. Newtonian solutions up to 19 m Pas and non-Newtonian shear thinning fluids some of which also exhibited a yield stress and some of which were viscoelastic. For the unaerated case, the power number-Reynolds number plot is in good agreement with the literature. For the aerated case, the result can conveniently be divided into three Reynolds number regions. At Re > ∼900, the data obtained for the solutions is not markedly different to that for water except that a higher impeller speed is required to achieve complete gas dispersion. For ∼ 10 < Re < ∼900. the power drawn is independent of gassing rate and greater levels of elasticity and the presence of a yield stress leads to the lowest power numbers. For Re < ∼10, the gassed and ungassed power numbers are equal.
Exploration and Incentives in Reinforcement Learning

How do you incentivize self-interested agents to explore when they prefer to exploit? We consider complex exploration problems, where each agent faces the same (but unknown) Markov decision process (MDP). In contrast with traditional formulations of reinforcement learning (RL), agents control the choice of policies, whereas an algorithm can only issue recommendations. However, the algorithm controls the flow of information, and can incentivize the agents to explore via information asymmetry. We design an algorithm which explores all reachable states in the MDP. We achieve provable guarantees similar to those for incentivizing exploration in static, stateless exploration problems studied previously. From the RL perspective, we design RL mechanisms, that is, RL algorithms that interact with self-interested agents and are compatible with their incentives. This is the first paper on RL mechanisms, that is, the first paper on any scenario that combines RL and incentives, to the best of our knowledge.
Privacy-Preserving Distributed SVD via Federated Power

Singular value decomposition (SVD) is one of the most fundamental tools in machine learning and statistics. The modern machine learning community usually assumes that data come from and belong to small-scale device users. The low communication and computation power of such devices, and the possible privacy breaches of users’ sensitive data make the computation of SVD challenging. Federated learning (FL) is a paradigm enabling a large number of devices to jointly learn a model in a communication-efficient way without data sharing. In the FL framework, we develop a class of algorithms called FedPower for the computation of partial SVD in the modern setting. Based on the well-known power method, the local devices alternate between multiple local power iterations and one global aggregation to improve communication efficiency. In the aggregation, we propose to weight each local eigenvector matrix with Orthogonal Procrustes Transformation (OPT). Considering the practical stragglers’ effect, the aggregation can be fully participated or partially participated, where for the latter we propose two sampling and aggregation schemes. Further, to ensure strong privacy protection, we add Gaussian noise whenever the communication happens by adopting the notion of differential privacy (DP). We theoretically show the convergence bound for FedPower. The resulting bound is interpretable with each part corresponding to the effect of Gaussian noise, parallelization, and random sampling of devices, respectively. We also conduct experiments to demonstrate the merits of FedPower. In particular, the local iterations not only improve communication efficiency but also reduce the chance of privacy breaches.
Simultaneous Tactile Exploration and Grasp Refinement for Unknown Objects

This letter addresses the problem of simultaneously exploring an unknown object to model its shape, using tactile sensors on robotic fingers, while also improving finger placement to optimise grasp stability. In many situations, a robot will have only a partial camera view of the near side of an observed object, for which the far side remains occluded. We show how an initial grasp attempt, based on an initial guess of the overall object shape, yields tactile glances of the far side of the object which enable the shape estimate and consequently the successive grasps to be improved. We propose a grasp exploration approach using a probabilistic representation of shape, based on Gaussian Process Implicit Surfaces. This representation enables initial partial vision data to be augmented with additional data from successive tactile glances. This is combined with a probabilistic estimate of grasp quality to refine grasp configurations. When choosing the next set of finger placements, a bi-objective optimisation method is used to mutually maximise grasp quality and improve shape representation during successive grasp attempts. Experimental results show that the proposed approach yields stable grasp configurations more efficiently than a baseline method, while also yielding improved shape estimate of the grasped object.
Network Growth From Global and Local Influential Nodes

In graph theory and network analysis, node degree is defined as a simple but powerful centrality to measure the local influence of node in a complex network. Preferential attachment based on node degree has been widely adopted for modeling network growth. However, there is many evidence showing deviation of real network growth from what a pure degree-based model suggests. It seems that node degree is not a reliable measure for predicting the preference of newcomers in attaching to the network, or at least, it does not tell the whole story. In this paper, we argue that there is another dimension to network growth, one that we call node “coreness”. The new dimension gives insights on the global influence of nodes, in comparison to the local view the degree metric provides. We found that the probability of existing nodes attracting new nodes generally follows an exponential dependence on node coreness, while at the same time, follows a power-law dependence on node degree. That is to say, high-coreness nodes are more powerful than high-degree nodes in attracting newcomers. The new dimension further discloses some hidden phenomena that happen in the process of network growth. The power of node degree in attracting newcomers increases over time while the power of coreness decreases, and finally, they reach a state of equilibrium in the growth. All these theories have been tested on real-world networks.
A Soft Method for Outliers Detection at the Edge of the Network

The combination of the Internet of Things and the Edge Computing gives many opportunities to support innovative applications close to end users. Numerous devices present in both infrastructures can collect data upon which various processing activities can be performed. However, the quality of the outcomes may be jeopardized by the presence of outliers. In this paper, we argue on a novel model for outliers detection by elaborating on a `soft' approach. Our mechanism is built upon the concepts of candidate and confirmed outliers. Any data object that deviates from the population is confirmed as an outlier only after the study of its sequence of magnitude values as new data are incorporated into our decision making model. We adopt the combination of a sliding with a landmark window model when a candidate outlier is detected to expand the sequence of data objects taken into consideration. The proposed model is fast and efficient as exposed by our experimental evaluation while a comparative assessment reveals its pros and cons.
TEC: Tensor Ensemble Classifier for Big Data

Tensor (multidimensional array) classification problem has become very popular in modern applications such as image recognition and high dimensional spatio-temporal data analysis. Support Tensor Machine (STM) classifier, which is extended from the support vector machine, takes CANDECOMP / Parafac (CP) form of tensor data as input and predicts the data labels. The distribution-free and statistically consistent properties of STM highlight its potential in successfully handling wide varieties of data applications. Training a STM can be computationally expensive with high-dimensional tensors. However, reducing the size of tensor with a random projection technique can reduce the computational time and cost, making it feasible to handle large size tensors on regular machines. We name an STM estimated with randomly projected tensor as Random Projection-based Support Tensor Machine (RPSTM). In this work, we propose a Tensor Ensemble Classifier (TEC), which aggregates multiple RPSTMs for big tensor classification. TEC utilizes the ensemble idea to minimize the excessive classification risk brought by random projection, providing statistically consistent predictions while taking the computational advantage of RPSTM. Since each RPSTM can be estimated independently, TEC can further take advantage of parallel computing techniques and be more computationally efficient. The theoretical and numerical results demonstrate the decent performance of TEC model in high-dimensional tensor classification problems. The model prediction is statistically consistent as its risk is shown to converge to the optimal Bayes risk. Besides, we highlight the trade-off between the computational cost and the prediction risk for TEC model. The method is validated by extensive simulation and a real data example. We prepare a python package for applying TEC, which is available at our GitHub.
Distortion in Social Choice Problems: The First 15 Years and Beyond

The notion of distortion in social choice problems has been defined to measure the loss in efficiency---typically measured by the utilitarian social welfare, the sum of utilities of the participating agents---due to having access only to limited information about the preferences of the agents. We survey the most significant results of the literature on distortion from the past 15 years, and highlight important open problems and the most promising avenues of ongoing and future work.
Siamese Labels Auxiliary Network(SiLaNet)

Auxiliary information attracts more and more attention in the area of machine learning. Attempts so far to include such auxiliary information in state-of-the-art learning process have often been based on simply appending these auxiliary features to the data level or feature level. In this paper, we intend to propose a novel training method with new options and architectures. Siamese labels, which were used in the training phase as auxiliary modules. While in the testing phase, the auxiliary module should be removed. Siamese label module makes it easier to train and improves the performance in testing process. In general, the main contributions can be summarized as, 1) Siamese Labels are firstly proposed as auxiliary information to improve the learning efficiency; 2) We establish a new architecture, Siamese Labels Auxiliary Network (SilaNet), which is to assist the training of the model; 3) Siamese Labels Auxiliary Network is applied to compress the model parameters by 50% and ensure the high accuracy at the same time. For the purpose of comparison, we tested the network on CIFAR-10 and CIFAR100 using some common models. The proposed SilaNet performs excellent efficiency both on the accuracy and robustness.
COVID-19: Detecting depression signals during stay-at-home period

The new coronavirus outbreak has been officially declared a global pandemic by the World Health Organization. To grapple with the rapid spread of this ongoing pandemic, most countries have banned indoor and outdoor gatherings and ordered their residents to stay home. Given the developing situation with coronavirus, mental health is an important challenge in our society today. In this paper, we discuss the investigation of social media postings to detect signals relevant to depression. To this end, we utilize topic modeling features and a collection of psycholinguistic and mental-well-being attributes to develop statistical models to characterize and facilitate representation of the more subtle aspects of depression. Furthermore, we predict whether signals relevant to depression are likely to grow significantly as time moves forward. Our best classifier yields F-1 scores as high as 0.8 and surpasses the utilized baseline by a considerable margin, 0.173. In closing, we propose several future research avenues.
Incorporating Causal Graphical Prior Knowledge into Predictive Modeling via Simple Data Augmentation

Causal graphs (CGs) are compact representations of the knowledge of the data generating processes behind the data distributions. When a CG is available, e.g., from the domain knowledge, we can infer the conditional independence (CI) relations that should hold in the data distribution. However, it is not straightforward how to incorporate this knowledge into predictive modeling. In this work, we propose a model-agnostic data augmentation method that allows us to exploit the prior knowledge of the CI encoded in a CG for supervised machine learning. We theoretically justify the proposed method by providing an excess risk bound indicating that the proposed method suppresses overfitting by reducing the apparent complexity of the predictor hypothesis class. Using real-world data with CGs provided by domain experts, we experimentally show that the proposed method is effective in improving the prediction accuracy, especially in the small-data regime.
BRANCHED METAL NANOPARTICLES: A REVIEW ON WET-CHEMICAL SYNTHESIS AND BIOMEDICAL APPLICATIONS

Branched metal nanoparticles present a promising new class of materials, which have great potential as sensors, catalysts, drug carriers, and imaging agents, owing to their unique nanostructures, physicochemical properties, optical properties, and other characteristics. Many efforts have recently been devoted to the wet-chemical synthesis of branched metal nanoparticles. Seed-mediated growth and seedless growth are two main routes for producing branched metal nanoparticles. Most particle synthesis methods can be modified for different metal systems. In this review, various synthesis methods for the fabrication of branched monometallic, bimetallic, and multimetallic nanoparticles and also branched polymer core-metal nanoshell composite nanoparticles are summarized, catagorized, and discussed. The relevance and performance of such nanostructured materials with regard to their optical properties which arise from localized surface plasmon resonances are summarized, and their potential as excellent substrates for surface enhanced Raman scattering (SERS) is reviewed. Other applications of branched nanoparticles such as drug delivery vehicle, medical imaging agent, catalysis, and magnetism are briefly introduced.
Dataset Lifecycle Framework and its applications in Bioinformatics

Bioinformatics pipelines depend on shared POSIX filesystems for its input, output and intermediate data storage. Containerization makes it more difficult for the workloads to access the shared file systems. In our previous study, we were able to run both ML and non-ML pipelines on Kubeflow successfully. However, the storage solutions were complex and less optimal. This is because there are no established resource types to represent the concept of data source on Kubernetes. More and more applications are running on Kubernetes for batch processing. End users are burdened with configuring and optimising the data access, which is what we have experienced before. In this article, we are introducing a new concept of Dataset and its corresponding resource as a native Kubernetes object. We have leveraged the Dataset Lifecycle Framework which takes care of all the low-level details about data access in Kubernetes pods. Its pluggable architecture is designed for the development of caching, scheduling and governance plugins. Together, they manage the entire lifecycle of the custom resource Dataset. We use Dataset Lifecycle Framework to serve data from object stores to both ML and non-ML pipelines running on Kubeflow. With DLF, we make training data fed into ML models directly without being downloaded to the local disks, which makes the input scalable. We have enhanced the durability of training metadata by storing it into a dataset, which also simplifies the set up of the Tensorboard, separated from the notebook server. For the non-ML pipeline, we have simplified the 1000 Genome Project pipeline with datasets injected into the pipeline dynamically. In addition, our preliminary results indicate that the pluggable caching mechanism can improve the performance significantly.
Technical Report for A Joint User Scheduling and Trajectory Planning Data Collection Strategy for the UAV-assisted WSN

Unmanned aerial vehicles (UAVs) are usually dispatched as mobile sinks to assist data collection in large-scale wireless sensor networks (WSNs). However, when considering the limitations of UAV's mobility and communication capabilities in a large-scale WSN, some sensor nodes may run out of storage space as they fail to offload their data to the UAV for an extended period of time. To minimize the data loss caused by the above issue, a joint user scheduling and trajectory planning data collection strategy is proposed in this letter, which is formulated as a non-convex optimization problem. The problem is further divided into two sub-problems and solved sequentially. Simulation results show that the proposed strategy is more effective in minimizing data loss rate than other strategies.
Luttinger sum rules and spin fractionalization in the SU(
N
) Kondo lattice

We show how Oshikawa's theorem for the Fermi surface volume of the Kondo lattice can be extended to the SU$(N)$ symmetric case. By extending the theorem, we are able to show that the mechanism of Fermi surface expansion seen in the large $N$ mean-field theory is directly linked to the expansion of the Fermi surface in a spin-$1/2$ Kondo lattice. This linkage enables us to interpret the expansion of the Fermi surface in a Kondo lattice as a fractionalization of the local moments into heavy electrons. Our method allows extension to a pure U(1) spin liquid, where we find the volume of the spinon Fermi surface by applying a spin-twist, analogous to Oshikawa's flux insertion. Lastly, we discuss the possibility of interpreting the FL$^*$ phase characterised by a small Fermi surface in the absence of symmetry breaking, as a non-topological coexistence of such a U(1) spin liquid and an electronic Fermi liquid.
A simple method for improving the accuracy of Chung-Lu random graph generation

Random graph models play a central role in network analysis. The Chung-Lu model, which connects nodes based on their expected degrees is of particular interest. It is widely used to generate null-graph models with expected degree sequences as well as implicitly define network measures such as modularity. Despite its popularity, practical methods for generating instances of Chung-Lu model-based graphs do relatively poor jobs in terms of accurately realizing many degree sequences. We introduce a simple method for improving the accuracy of Chung-Lu graph generation. Our method uses a Poisson approximation to define a linear system describing the expected degree sequence to be output from the model using standard generation techniques. We then use the inverse of this system to determine an appropriate input corresponding to the desired output. We give a closed form expression for this inverse and show that it may be used to drastically reduce error for many degree distributions.
Graphs with Minimal Strength

For any graph G of order p, a bijection f:V(G)→{1,2,…,p} is called a numbering of G. The strength strf(G) of a numbering f of G is defined by strf(G)=max{f(u)+f(v)|uv∈E(G)}, and the strength str(G) of a graph G is str(G)=min{strf(G)|f is a numbering of G}. In this paper, many open problems are solved, and the strengths of new families of graphs are determined.