Miracle-dz/UniFAR-FTUs huggingface dataset:
A case report and literature review

Rationale: Primary hepatic neuroendocrine tumors (PHNETs) are extremely rare and are difficult to diagnose preoperatively.We report a case of PHNET diagnosed preoperatively and successfully resected using associating liver partition and portal vein ligation for staged hepatectomy (ALPPS). Patientconcerns:A 72-year-old woman was admitted to our hospital for a hepatic mass, which was incidentally identified during a routine health checkup. The patient has no other obvious symptoms of discomfort. Diagnoses: Physical examination revealed a palpable mass in the right upper quadrant of her abdomen. Dynamic contrastenhanced abdominal computed tomography (CT) showed a low-density mass measuring 13 7 6cm in both, the right and left hepatic lobes. F-fluorodesoxyglucose positron emission tomography (F-FDG PET) and fused PET/CT showed increased uptake by the mass, which was indicative of a hepatic tumor. Interventions: We use a novel ALPPS surgical procedure to safely and radically remove primary neuroendocrine tumors. Outcomes: No postoperative bleeding and bile leakage were reported, and the patient recovered uneventfully.The patient was followed-up for a year without recurrence. Lessons:PHNETs are rare tumors, and confirming the diagnosis using the best possible preoperative examination is important. An optimal treatment plan is selected based on the patient’s condition to ensure a favorable prognosis. Tumors too large to undergo surgical removal can be resected using the ALPPS procedure, as described in this case report. Abbreviations: AFP= alpha fetoprotein, ALPPS= associating liver partition and portal vein ligation for staged hepatectomy, CD= cluster of differentation, CgA = chromogranin A, CK = cytokeratin, CT = computed tomography, HbsAg = hepatitis B surface antigen, HCV = hepatitis C virus, ICG = indocyaninegreen, NETs = neuroendocrine tumors, PET = positron emission tomography, PHNETs = primary hepatic neuroendocrine tumors, Syn = synaptophysin, TTF = thyroid transcription factor.
UnrealStereo: Controlling Hazardous Factors to Analyze Stereo Vision

A reliable stereo algorithm is critical for many robotics applications. But textureless and specular regions can easily cause failure by making feature matching difficult. Understanding whether an algorithm is robust to these hazardous regions is important. Although many stereo benchmarks have been developed to evaluate performance, it is hard to quantify the effect of hazardous regions in real images because the location and severity of these regions are unknown. In this paper, we develop a synthetic image generation tool enabling to control hazardous factors, such as making objects more specular or transparent, to produce hazardous regions at different degrees. The densely controlled sampling strategy in virtual worlds enables to effectively stress test stereo algorithms by varying the types and degrees of the hazard. We generate a large synthetic image dataset with automatically computed hazardous regions and analyze algorithms on these regions. The observations from synthetic images are further validated by annotating hazardous regions in real-world datasets Middlebury and KITTI (which gives a sparse sampling of the hazards). Our synthetic image generation tool is based on a game engine Unreal Engine 4 and will be open-source along with the virtual scenes in our experiments. Many publicly available realistic game contents can be used by our tool to provide an enormous resource for development and evaluation of algorithms.
PruNet: Class-Blind Pruning Method For Deep Neural Networks

DNNs are highly memory and computationally intensive, due to which they are unfeasible to deploy in real time or mobile applications, where power and memory resources are scarce. Introducing sparsity in the network is a way to reduce those requirements. However, systematically employing pruning under given accuracy requirements is a challenging problem. We propose a novel methodology that iteratively applies a magnitude-based Class-Blind pruning to compress a DNN for obtaining a sparse model. It is a generic methodology and can be applied to different types of DNNs. We demonstrate that retraining after pruning is essential to restore the accuracy of the network. Experimental results show that our methodology is able to reduce the model size by around two orders of magnitude, without noticeably affecting the accuracy. It requires several iterations of pruning and retraining, but can achieve up to 190x Memory Saving Ratio (for the LeNet on the MNIST dataset) when compared to the baseline model. Similar results are also obtained for more complex networks like 91x for VGG-16 on the CIFAR100 dataset. If we combine this work with an efficient coding for sparse networks, like Compressed Sparse Column (CSC) or Compressed Sparse Row (CSR), we can obtain a reduced memory footprint. Our methodology can be complemented by other compression techniques, like weight sharing, quantization or fixed-point conversion, that allows to further reduce memory and computations.
Efficiency of Shallow Cascades for Improving Deep Learning AI Systems

This paper presents a technology for simple and non-iterative improvements of Multilayer and Deep Learning neural networks and Artificial Intelligence (AI) systems. The improvements are, in essence, shallow networks constructed on top of the existing Deep Learning architecture. Theoretical foundation of the technology is based on Stochastic Separation Theorems and the ideas of measure concentration. We show that, subject to mild technical assumptions on statistical properties of internal signals in Deep Learning AI, with probability close to one the technology enables instantaneous “learning away” of spurious and systematic errors. The method is illustrated with numerical examples.
Complex-Valued Deep Boltzmann Machines

Deep Boltzmann Machines (DBMs) are a type of undirected deep generative models. They are part of the class of models used for performing unsupervised pretraining of deep neural networks. This paper presents the full deduction of the learning algorithm for DBMs with values in the complex domain. Experiments done using the MNIST and FashionMNIST datasets show a better performance of complex-valued DBMs compared with real-valued DBMs, both in terms of average log-probability, and in terms of classification error for the deep neural network models initialized using complex-valued DBMs.
Interpretative Topic Categorization Via Deep Multiple Instance Learning

Given a document stream, categorizing the topic of the documents while highlighting the key information is critical to improving user reading efficiency. Based on the practical application, previous work on topic categorization which only focus on classifying each document to a given category is not enough. It is significant to promote an interpretative topic categorization to extract the characteristic words in the documents to assist user reading. Based on the instances-bag relationship between words and the document, we propose a multiple instance learning method to tackle this problem. In addition, another problem in traditional topic categorization is the deficient word representation. In this paper, we use Bi-directional Long Short Memory (Bi-LSTM) and Convolutional neural network (CNN) to capture both the local sequential feature and the global context feature to form a comprehensive word representation to solve this problem. Finally, we thus design an effective model Interpretative Topic Categorization Model (ITCM) to exploit the MIL property with deep learning to classify the documents to the predefined topics and discover the characteristic words simultaneously. We conduct two groups of experiments and prove that ITCM not only achieves convincing performance on topic categorization, but also interpreting effectively which words characterize the document category without word-level supervised learning.
Learning From Hierarchical Spatiotemporal Descriptors for Micro-Expression Recognition

Micro-expression recognition aims to infer genuine emotions that people try to conceal from facial video clips. It is a very challenging task because micro-expressions have a very low intensity and short duration, which makes micro-expressions difficult to observe. Recently, researchers have designed various spatiotemporal descriptors to describe micro-expressions. It is notable that for better capturing the low-intensity facial muscle movement, a fixed spatial division grid, $8\times 8$ for example, is commonly used to partition the facial images into a few facial blocks before extracting descriptors. However, it is hard to choose an ideal division grid for different micro-expression samples because the division grids affect the discriminative ability of spatiotemporal descriptors to distinguish micro-expressions. To address this problem, in this paper, we design a hierarchical spatial division scheme for spatiotemporal descriptor extraction. By using the proposed scheme, it would not be a problem to determine which division grid is most suitable regarding different micro-expression samples. Furthermore, we propose a kernelized group sparse learning (KGSL) model to process hierarchical scheme based spatiotemporal descriptors such that they are more effective for micro-expression recognition tasks. To evaluate the performance of the proposed micro-expression recognition method consisting of the hierarchical scheme based spatiotemporal descriptors and KGSL, extensive experiments are conducted on two public micro-expression databases: CASME II and SMIC. Compared with many recent state-of-the-art approaches, our method achieves more promising recognition results.
SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth

A key limitation of deep convolutional neural network (DCNN)-based image segmentation methods is the lack of generalizability. Manually traced training images are typically required when segmenting organs in a new imaging modality or from distinct disease cohort. The manual efforts can be alleviated if the manually traced images in one imaging modality (e.g., MRI) are able to train a segmentation network for another imaging modality (e.g., CT). In this paper, we propose an end-to-end synthetic segmentation network (SynSeg-Net) to train a segmentation network for a target imaging modality without having manual labels. SynSeg-Net is trained by using: 1) unpaired intensity images from source and target modalities and 2) manual labels only from source modality. SynSeg-Net is enabled by the recent advances of cycle generative adversarial networks and DCNN. We evaluate the performance of the SynSeg-Net on two experiments: 1) MRI to CT splenomegaly synthetic segmentation for abdominal images and 2) CT to MRI total intracranial volume synthetic segmentation for brain images. The proposed end-to-end approach achieved superior performance to two-stage methods. Moreover, the SynSeg-Net achieved comparable performance to the traditional segmentation network using target modality labels in certain scenarios. The source code of SynSeg-Net is publicly available.11https://github.com/MASILab/SynSeg-Net
Power Allocation for Energy Efficiency Optimization in Distributed Antenna Systems

An energy efficiency (EE) maximization problem with multiple distributed antennas is considered in this paper. Besides the constraint on the total transmit power and per-antenna transmit power constraints, an additional constraint dealing with total weighted power is introduced. Moreover, a Quality-of-Service (QoS) constraint is introduced. Because the original problem is a fractional convex one, Dinkelbach's algorithm is implemented to reformulate the problem. By investigating the Karush- Kuhn- Tucker (KKT) conditions, the mathematical properties of the globally optimal solution are studied and proved, which provides a deeper understanding of the structure of power allocation among antennas. Numerical results demonstrate the effect of QoS on EE and also the structure of power allocation.
Mixing Habits and Planning for Multi-Step Target Reaching Using Arbitrated Predictive Actor-Critic

Internal models are important when agents make decisions based on predictions of future states and their utilities. However, using internal models for planning can be time consuming. Therefore, it can be useful to use a habitual system for repetitive tasks that can be executed faster and with reduced algorithmic resources. Current evidence suggests that the brain uses both control systems, planning and habitual systems for behavioural control, which then requires an arbitration between these two systems. In our previous work [1], we proposed an Arbitrated Predictive Actor-Critic (APAC), which is a neural architecture demonstrating cooperative mechanisms of planning and habitual control systems for one step mapping. The present study tests the ability of such a model to control a simulated twojoints robotic arm during multiple reaching tasks with movement limitations that require multiple steps to solve the task. Our results show that APAC can learn the multi-step learning under various conditions. Interestingly, the APAC tends to shift from planning to habits by taking actions predicted by a habitual controller over the training time.
Satellite-Based Estimation of Hourly PM2.5 Concentrations Using a Vertical-Humidity Correction Method from Himawari-AOD in Hebei

Particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5) is related to various adverse health effects. Ground measurements can yield highly accurate PM2.5 concentrations but have certain limitations in the discussion of spatial-temporal variations in PM2.5. Satellite remote sensing can obtain continuous and long-term coverage data, and many previous studies have demonstrated the relationship between PM2.5 and AOD (aerosol optical depth) from theoretical analysis and observation. In this study, a new aerosol product with a high spatial-temporal resolution retrieved from the AHI (the Advance Himawari Imager) was obtained using a vertical-humidity correction method to estimate hourly PM2.5 concentrations in Hebei. The hygroscopic growth factor (f(RH)) was fitted at each site (in a total of 137 matched sites). Meanwhile, assuming that there was little change in f(RH) at a certain scale, the nearest f(RH) of each pixel was determined to calculate PM2.5 concentrations. Compared to the correlation between AOD and PM2.5, the relationship between the “dry” mass extinction efficiency obtained by vertical-humidity correction and the ground-measured PM2.5 significantly improved, with r coefficient values increasing from 0.19–0.47 to 0.61–0.76. The satellite-estimated hourly PM2.5 concentrations were consistent with the ground-measured PM2.5, with a high r (0.8 ± 0.07) and a low RMSE (root mean square error, 30.4 ± 5.5 μg/m3) values, and the accuracy in the afternoon (13:00–16:00) was higher than that in the morning (09:00–12:00). Meanwhile, in a comparison of the daily average PM2.5 concentrations of 11 sites from different cities, the r values were approximately 0.91 ± 0.03, and the RMSEs were between 13.94 and 31.44 μg/m3. Lastly, pollution processes were analyzed, and the analysis indicated that the high spatial-temporal resolution of the PM2.5 data could continuously and intuitively reflect the characteristics of regional pollutants (such as diffusion and accumulation), which is of great significance for the assessment of regional air quality.
CTF-PSF: Coupled Tensor Factorization with Partially Shared Factors

Coupled matrix-tensor factorization has been suc- cessfully applied in various fields in the processing of coupled data. However, the unshared components between coupled data tend to make the joint decomposition inaccurate. In order to solve this problem, in this work, we propose a method to improve the traditional method by combining individual decomposition and coupled decomposition to analyze the shared and unshared components. Numerical experiments are given to illustrate the advantages of the proposed method compared to the existing approaches.
Machine Common Sense Concept Paper

This paper summarizes some of the technical background, research ideas, and possible development strategies for achieving machine common sense. Machine common sense has long been a critical-but-missing component of Artificial Intelligence (AI). Recent advances in machine learning have resulted in new AI capabilities, but in all of these applications, machine reasoning is narrow and highly specialized. Developers must carefully train or program systems for every situation. General commonsense reasoning remains elusive. The absence of common sense prevents intelligent systems from understanding their world, behaving reasonably in unforeseen situations, communicating naturally with people, and learning from new experiences. Its absence is perhaps the most significant barrier between the narrowly focused AI applications we have today and the more general, human-like AI systems we would like to build in the future. Machine common sense remains a broad, potentially unbounded problem in AI. There are a wide range of strategies that could be employed to make progress on this difficult challenge. This paper discusses two diverse strategies for focusing development on two different machine commonsense services: (1) a service that learns from experience, like a child, to construct computational models that mimic the core domains of child cognition for objects (intuitive physics), agents (intentional actors), and places (spatial navigation); and (2) service that learns from reading the Web, like a research librarian, to construct a commonsense knowledge repository capable of answering natural language and image-based questions about commonsense phenomena.
Genetic Polymorphisms and Zinc Status: Implications for Supplementation in Metabolic Diseases.

BACKGROUND Zinc is an essential component for all living organisms, representing the second most abundant trace element, after iron. This element is widely distributed in the tissues of a human body where it is involved in normal growth, reproduction and several biological functions including immunity, energy metabolism and antioxidant processes. Because of its essential role, zinc levels in the human body must remain constant, independently of dietary intake fluctuations. The homeostasis of zinc is a well-regulated cellular process and has been reported to be chiefly mediated by the expression and activity of zinc-binding proteins such as metallothioneins and zinc transporters. Genes encoding for these proteins are subjected to genetic variants. METHODS We performed a multi-database electronic search to provide an overview on the relationship between specific polymorphisms (SNP) of genes encoding for metallothioneins and zinc transporters and their relationship with zinc status, immune function and some non-communicable diseases. RESULTS A number of SNP are implicated in a range of metabolic disease. Some SNP may affect the impact of zinc supplementation on immune function, diabetes, and obesity. CONCLUSION New studies are needed to clarify the interaction between individual genetic profile and zinc status. Moreover, there is a need for a better interaction between the scientific bodies and health professionals to allow better dietary and behavioural recommendations to promote human health, with particular concern to elderly people.
Maelstrom: Mitigating Datacenter-level Disasters by Draining Interdependent Traffic Safely and Efficiently

We present Maelstrom, a new system for mitigating and recovering from datacenter-level disasters. Maelstrom provides a trafﬁc management framework with modular, reusable primitives that can be composed to safely and ef-ﬁciently drain the trafﬁc of interdependent services from one or more failing datacenters to the healthy ones. Maelstrom ensures safety by encoding inter-service dependencies and resource constraints. Maelstrom uses health monitoring to implement feedback control so that all speciﬁed constraints are satisﬁed by the trafﬁc drains and recovery procedures executed during disaster mitigation. Maelstrom exploits parallelism to drain and restore independent trafﬁc sources efﬁciently. We verify the correctness of Maelstrom’s disaster mitigation and recovery procedures by running large-scale tests that drain production trafﬁc from entire datacenters and then retore the trafﬁc back to the datacenters. These tests (termed drain tests) help us gain a deep understanding of our complex systems, and provide a venue for continually improving the reliability of our infrastructure. Maelstrom has been in production at Facebook for more than four years, and has been successfully used to mitigate and recover from 100+ datacenter outages.
Competitive achievement may be predicted by executive functions in junior tennis players: An 18-month follow-up study

ABSTRACT This study evaluates whether high levels of executive function predict competition results 18 months later in junior tennis players. Forty junior tennis players (20 girls, 20 boys; 9–15 years old) who regularly participate in prefecture tennis tournaments were recruited. All participants underwent executive function evaluations (the Design Fluency Task) in July 2015 and their prefecture junior rankings in August 2015 and February 2017 were recorded. As a result, after controlling for age and gender, the future ranking (February 2017) was significantly predicted by performance in the Design Fluency Task (β = -.30, p = .02; ΔR2 = .08), whereas the prediction for August 2015 rank as the baseline was not significant (β = -.19, p = .17; ΔR2 = .03). After controlling for age, gender, and ranking in the baseline, the change in ranking was significantly predicted by performance in the Design Fluency Task (β = -.14, p = .02; ΔR2 = .02). This suggests that childhood executive function may play a significant role in success later in life. This study highlights how executive function predicts future success in a specific sport; as such, supporting the development of executive function may contribute to higher competition results.
Analysing rotation-invariance of a log-polar transformation in convolutional neural networks

Applications in computer vision have the challenge of handling objects in images with different orientations and other visual transformations. However, for many tasks, the ideal input feature would be space-invariant regarding geometric transformations, such as angle change, rotation, framing, scale, among others. The simplest way to get invariance to an input class is to train a neural network with augmented data, which does not always capture all changes. In this paper, we propose an architecture of a convolutional neural network that exploits the inherent space-invariance characteristics of the log-polar transformation, which is inspired by the human visual system. We performed experiments on the object classification task and evaluated using several datasets. Our results, employing accuracy metric, show our architecture has the advantage on rotated images, which may be interesting for object detection tasks.