Miracle-dz/UniFAR-FTUs huggingface dataset:
Differentially Private Data Generative Models

Deep neural networks (DNNs) have recently been widely adopted in various applications, and such success is largely due to a combination of algorithmic breakthroughs, computation resource improvements, and access to a large amount of data. However, the large-scale data collections required for deep learning often contain sensitive information, therefore raising many privacy concerns. Prior research has shown several successful attacks in inferring sensitive training data information, such as model inversion, membership inference, and generative adversarial networks (GAN) based leakage attacks against collaborative deep learning. In this paper, to enable learning efficiency as well as to generate data with privacy guarantees and high utility, we propose a differentially private autoencoder-based generative model (DP-AuGM) and a differentially private variational autoencoder-based generative model (DP-VaeGM). We evaluate the robustness of two proposed models. We show that DP-AuGM can effectively defend against the model inversion, membership inference, and GAN-based attacks. We also show that DP-VaeGM is robust against the membership inference attack. We conjecture that the key to defend against the model inversion and GAN-based attacks is not due to differential privacy but the perturbation of training data. Finally, we demonstrate that both DP-AuGM and DP-VaeGM can be easily integrated with real-world machine learning applications, such as machine learning as a service and federated learning, which are otherwise threatened by the membership inference attack and the GAN-based attack, respectively.
A General Framework for Time-Changed Markov Processes and Applications

Abstract In this paper, we propose a general approximation framework for the valuation of (path-dependent) options under time-changed Markov processes. The underlying background process is assumed to be a general Markov process, and we consider the case when the stochastic time change is constructed from either discrete or continuous additive functionals of another independent Markov process. We first approximate the underlying Markov process by a continuous time Markov chain (CTMC), and derive the functional equation characterizing the double transforms of the transition matrix of the resulting time-changed CTMC. Then we develop a two-layer approximation scheme by further approximating the driving process in constructing the time change using an independent CTMC. We obtain a single Laplace transform expression. Our framework incorporates existing time-changed Markov models in the literature as special cases, such as the time-changed diffusion process and the time-changed Levy process. Numerical experiments illustrate the accuracy of our method.
Curaxin CBL0137 Exerts Anticancer Activity via Diverse Mechanisms

Chemotherapy with or without radiation remains the first choice for most cancers. However, intolerant side effects and conventional drug resistance restrict actual clinical efficacy. Curaxin CBL0137 is designed to regulate p53 and nuclear factor-κB simultaneously and to prevent the resistance caused by a single target. Functionally, CBL0137 exhibits an antitumor activity in multiple cancers, including glioblastoma, renal cell carcinoma, melanoma, neuroblastoma, and small cell lung cancer (SCLC). Mechanistically, CBL0137 is originally identified to act by facilitates chromatin transcription (FACT) complex. Further investigations reveal that several pathways, such as NOTCH1 and heat shock factor 1 (HSF1), are involved in the process. CBL0137 has been reported to target cancer stem cells (CSCs) and enhance chemotherapy/monotherapy efficacy. The translational advance of CBL0137 into clinical practice is expected to provide a promising future for cancer treatment.
GADGET SVM: A Gossip-bAseD sub-GradiEnT Solver for Linear SVMs

In the era of big data, an important weapon in a machine learning researcher's arsenal is a scalable Support Vector Machine (SVM) algorithm. SVMs are extensively used for solving classification problems. Traditional algorithms for learning SVMs often scale super linearly with training set size which becomes infeasible very quickly for large data sets. In recent years, scalable algorithms have been designed which study the primal or dual formulations of the problem. This often suggests a way to decompose the problem and facilitate development of distributed algorithms. In this paper, we present a distributed algorithm for learning linear Support Vector Machines in the primal form for binary classification called Gossip-bAseD sub-GradiEnT (GADGET) SVM. The algorithm is designed such that it can be executed locally on nodes of a distributed system. Each node processes its local homogeneously partitioned data and learns a primal SVM model. It then gossips with random neighbors about the classifier learnt and uses this information to update the model. Extensive theoretical and empirical results suggest that this anytime algorithm has performance comparable to its centralized and online counterparts.
Elastic Gossip: Distributing Neural Network Training Using Gossip-like Protocols

Distributing Neural Network training is of particular interest for several reasons including scaling using computing clusters, training at data sources such as IOT devices and edge servers, utilizing underutilized resources across heterogeneous environments, and so on. Most contemporary approaches primarily address scaling using computing clusters and require high network bandwidth and frequent communication. This thesis presents an overview of standard approaches to distribute training and proposes a novel technique involving pairwise-communication using Gossip-like protocols, called Elastic Gossip. This approach builds upon an existing technique known as Elastic Averaging SGD (EASGD), and is similar to another technique called Gossiping SGD which also uses Gossip-like protocols. Elastic Gossip is empirically evaluated against Gossiping SGD using the MNIST digit recognition and CIFAR-10 classification tasks, using commonly used Neural Network architectures spanning Multi-Layer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs). It is found that Elastic Gossip, Gossiping SGD, and All-reduce SGD perform quite comparably, even though the latter entails a substantially higher communication cost. While Elastic Gossip performs better than Gossiping SGD in these experiments, it is possible that a more thorough search over hyper-parameter space, specific to a given application, may yield configurations of Gossiping SGD that work better than Elastic Gossip.
A Brief History on the Theoretical Analysis of Dense Small Cell Wireless Networks

This article provides dives into the fundamentals of dense and ultra-dense small cell wireless networks, discussing the reasons why dense and ultra-dense small cell networks are fundamentally different from sparse ones, and why the well-known linear scaling law of capacity with the base station (BS) density in the latter does not apply to the former. In more detail, we review the impact of the following factors on ultradense networks (UDNs), (i) closed-access operations and line-of-sight conditions, (ii) the near-field effect, (iii) the antenna height difference between small cell BSs and user equipments (UEs), and (iv) the surplus of idle-mode-enabled small cell BSs with respect to UEs. Combining all these network characteristics and features, we present a more realistic capacity scaling law for UDNs, which indicates (i) the existence of an optimum BS density to maximise the area spectral efficiency (ASE) for a given finite UE density, and (ii) the existence of an optimum density of UEs that can be simultaneously scheduled across the network to maximise the ASE for a given finite BS density.
Layer Flexible Adaptive Computational Time for Recurrent Neural Networks

Deep recurrent neural networks perform well on sequence data and are the model of choice. It is a daunting task to decide the number of layers, especially considering different computational needs for tasks within a sequence of different difficulties. We propose a layer flexible recurrent neural network with adaptive computation time, and expand it to a sequence to sequence model. Contrary to the adaptive computation time model, our model has a dynamic number of transmission states which vary by step and sequence. We evaluate the model on a financial data set and Wikipedia language modeling. Experimental results show the performance improvement of 8% to 12% and indicate the model's ability to dynamically change the number of layers.
Representing pictures with emotions

Modern research in content-based image retrieval systems (CIBR) has become progressively more focused on the richness of human semantics. Several approaches may be used to reduced the 'semantic gap' between the high-level human experience and the low level visual features of pictures. Object ontology, among others, is one of the methods. In this paper we investigate the use of a codified emotion ontology over global color features of images to annotate the images at a high semantic level. In order to speed up the annotation process the images are sampled so that each digital image is represented by a random subset of its content. We test within controlled conditions how this random subset may represent the adequate high level emotional concept presented in the image. We monitor this information reducing process with entropy measures, showing that controlled random sampling can capture with significant relevance high level concepts for picture representation.
Micromixing Study of a Clustered Countercurrent-Flow Micro-Channel Reactor and Its Application in the Precipitation of Ultrafine Manganese Dioxide

A clustered countercurrent-flow micro-channel reactor (C-CFMCR) has been assembled by the numbering-up of its single counterpart (S-CFMCR). Its micromixing performance was then studied experimentally using a competitive parallel reaction system, and the micromixing time was calculated as the micromixing performance index. It was found that the micromixing time of C-CFMCR was ranged from 0.34 to 10 ms according to its numbering-up times and the operating conditions of the reactor, and it was close to that of S-CFMCR under the same operating conditions, demonstrating a weak scaling-up effect from S-CFMCR to C-CFMCR. The C-CFMCR was then applied to prepare ultrafine manganese dioxide in a continuous manner at varying micromixing time. It showed that the micromixing time had a major effect on the particle structure. More uniform and smaller MnO2 particles were obtained with intensified micromixing. By building a typical three electrode system to characterize their performance as a supercapacitor material, the MnO2 particles prepared by both S-CFMCR and C-CFMCR under optimal conditions displayed a specific capacitance of ~175 F·g−1 at the current density of 1 A·g−1, with a decline of ~10% after 500 charge-discharge cycles. This work showed that C-CFMCR will have a great potential for the continuous and large-scale preparation of ultrafine particles.
A Software Defined Wireless Networking Approach for Managing Handoff in IEEE 802.11 Networks

In Wireless Local Area Networks (WLAN) with more than one access point (AP), the handoff process plays a crucial role to guarantee the user service continuity. Usually initiated by the client’s equipment, it occurs smoothly on the order of seconds. However, despite being functional and well-established, this process can be inadequate in scenarios where users are executing multimedia applications, such as real-time video streaming or VoIP. For these applications, those few seconds may cause loss of packets, resulting in loss of essential information. Because of that, this study proposes a Software Defined Wireless Networking (SDWN) approach, in which a controller decides when to initiate the handoff process and chooses the AP the client’s device must connect. This approach was implemented in a testbed scenario and the results have shown its efficiency by decreasing the handoff delay and providing more stability to the process.
Transmission Strategy Design and Resource Allocation in D2D Multicast Cooperative Communications with SWIPT

This paper proposes a new transmission strategy for device-to-device (D2D) multicast cooperative communication systems based on Simultaneous Wireless Information and Power Transfer (SWIPT) technology. The transmission block is divided into two slots. In the first slot, the source user transmits the information and energy to the help user by SWIPT. In the second slot, the help user uses the cellular spectrum and forwards the information to multiple receivers by using harvested energy. In this paper, we aim to maximize the total system rate, and to tackle the problem, we propose a two-step scheme: In the first step, the resource allocation problem is solved by linear programming. In the second step, the power-splitting coefficient value is obtained by taking the benefit of help user into account. Numerical results show that the proposed strategy not only effectively improves the overall throughput and spectrum efficiency but also motivates the cooperation.
An IoT Architecture for Assessing Road Safety in Smart Cities

The Safe System (SS) approach to road safety emphasizes safety-by-design through ensuring safe vehicles, road networks, and road users. With a strong motivation from the World Health Organization (WHO), this approach is increasingly adopted worldwide. Considerations in SS, however, are made for the medium-to-long term. Our interest in this work is to complement the approach with a short-to-medium term dynamic assessment of road safety. Toward this end, we introduce a novel, cost-effective Internet of Things (IoT) architecture that facilitates the realization of a robust and dynamic computational core in assessing the safety of a road network and its elements. In doing so, we introduce a new, meaningful, and scalable metric for assessing road safety. We also showcase the use of machine learning in the design of the metric computation core through a novel application of Hidden Markov Models (HMMs). Finally, the impact of the proposed architecture is demonstrated through an application to safety-based route planning.
Learning curve of totally laparoscopic distal gastrectomy for gastric cancer: a single teaching hospital study

Introduction Totally laparoscopic distal gastrectomy (TLDG) for gastric cancer has gradually gained popularity. However, the learning curve of TLDG is rarely reported. Aim To determine the learning curve of TLDG for gastric cancer. Material and methods We retrospectively reviewed and analyzed the medical records of 80 patients with gastric cancer who underwent TLDG with lymph node dissection from January 2016 to December 2017. We divided the patients into four groups based on when they underwent TLDG: group A (cases 1–20), group B (cases 21–40), group C (cases 41–60), and group D (cases 61–80). Comparative analyses of clinical data, including clinicopathologic characteristics, operative data, and postoperative course, were performed for these groups. Results No significant difference was observed between the groups in various clinicopathologic characteristics. Total operative time for group A (168.3 ±14.6 min) was significantly longer than for groups B (152.5 ±10.5 min), C (154.2 ±11.6 min), and D (155.3 ±10.8 min), but there was no significant difference between groups B, C, and D. Anastomosis time for group A (27.5 ±12.4 min) was significantly longer than for groups B (15.3 ±4.6 min), C (16.6 ±5.7 min), and D (15.4 ±4.5 min), but there was no significant difference between groups B, C, and D. Non-anastomosis time, estimated blood loss, retrieved lymph nodes, time to first flatus, time to first oral intake, and postoperative hospital stay and complications showed no difference between the four groups. Conclusions An experience of approximately 20 cases of TLDG was required to complete the learning curve.
dynnode2vec: Scalable Dynamic Network Embedding

Network representation learning in low dimensional vector space has attracted considerable attention in both academic and industrial domains. Most real-world networks are dynamic with addition/deletion of nodes and edges. The existing graph embedding methods are designed for static networks and they cannot capture evolving patterns in a large dynamic network. In this paper, we propose a dynamic embedding method, dynnode2vec, based on the well-known graph embedding method node2vec. Node2vec is a random walk based embedding method for static networks. Applying static network embedding in dynamic settings has two crucial problems: 1) Generating random walks for every time step is time consuming 2) Embedding vector spaces in each timestamp are different. In order to tackle these challenges, dynnode2vec uses evolving random walks and initializes the current graph embedding with previous embedding vectors. We demonstrate the advantages of the proposed dynamic network embedding by conducting empirical evaluations on several large dynamic network datasets.
Assigning a Grade: Accurate Measurement of Road Quality Using Satellite Imagery

Roads are critically important infrastructure to societal and economic development, with huge investments made by governments every year. However, methods for monitoring those investments tend to be time-consuming, laborious, and expensive, placing them out of reach for many developing regions. In this work, we develop a model for monitoring the quality of road infrastructure using satellite imagery. For this task, we harness two trends: the increasing availability of high-resolution, often-updated satellite imagery, and the enormous improvement in speed and accuracy of convolutional neural network-based methods for performing computer vision tasks. We employ a unique dataset of road quality information on 7000km of roads in Kenya combined with 50cm resolution satellite imagery. We create models for a binary classification task as well as a comprehensive 5-category classification task, with accuracy scores of 88 and 73 percent respectively. We also provide evidence of the robustness of our methods with challenging held-out scenarios, though we note some improvement is still required for confident analysis of a never before seen road. We believe these results are well-positioned to have substantial impact on a broad set of transport applications.
Autonomous Transmission Power Decision Strategy for Energy Efficient Operation of a Dense Small Cell Network

Smart interference management methods are required to enhance the throughput, coverage, and energy efficiency of a dense small cell network. In this paper, we propose a transmit power control for energy efficient operation of a dense small cell network. We cast the power control problem as a noncooperative game to satisfy the design requirement that small cells do not need any information exchange among them. We analyze the sufficient condition for the existence of a Nash equilibrium (NE) state of the proposed game. We also analyze that the NE state is unique by transforming the original nonlinear fractional programming problem into a nonlinear parametric programming problem. Through simulation studies, we verify our analysis results. In addition, we show that the proposed method achieves higher energy efficiency of a network and balances the energy efficiency among cells more evenly than the methods based on the AIMD (additive increase and multiplicative decrease) algorithm.
Macrophage LRP1 Promotes Diet-Induced Hepatic Inflammation and Metabolic Dysfunction by Modulating Wnt Signaling

Hepatic inflammation is associated with the development of insulin resistance, which can perpetuate the disease state and may increase the risk of metabolic syndrome and diabetes. Despite recent advances, mechanisms linking hepatic inflammation and insulin resistance are still unclear. The low-density lipoprotein receptor-related protein 1 (LRP1) is a large endocytic and signaling receptor that is highly expressed in macrophages, adipocytes, hepatocytes, and vascular smooth muscle cells. To investigate the potential role of macrophage LRP1 in hepatic inflammation and insulin resistance, we conducted experiments using macrophage-specific LRP1-deficient mice (macLRP1−/−) generated on a low-density lipoprotein receptor knockout (LDLR−/−) background and fed a Western diet. LDLR−/−; macLRP1−/− mice gained less body weight and had improved glucose tolerance compared to LDLR−/− mice. Livers from LDLR−/−; macLRP1−/− mice displayed lower levels of gene expression for several inflammatory cytokines, including Ccl3, Ccl4, Ccl8, Ccr1, Ccr2, Cxcl9, and Tnf, and reduced phosphorylation of GSK3α and p38 MAPK proteins. Furthermore, LRP1-deficient peritoneal macrophages displayed altered cholesterol metabolism. Finally, circulating levels of sFRP-5, a potent anti-inflammatory adipokine that functions as a decoy receptor for Wnt5a, were elevated in LDLR−/−; macLRP1−/− mice. Surface plasmon resonance experiments revealed that sFRP-5 is a novel high affinity ligand for LRP1, revealing that LRP1 regulates levels of this inhibitor of Wnt5a-mediated signaling. Collectively, our results suggest that LRP1 expression in macrophages promotes hepatic inflammation and the development of glucose intolerance and insulin resistance by modulating Wnt signaling.
Attention Boosted Sequential Inference Model

Attention mechanism has been proven effective on natural language processing. This paper proposes an attention boosted natural language inference model named aESIM by adding word attention and adaptive direction-oriented attention mechanisms to the traditional Bi-LSTM layer of natural language inference models, e.g. ESIM. This makes the inference model aESIM has the ability to effectively learn the representation of words and model the local subsentential inference between pairs of premise and hypothesis. The empirical studies on the SNLI, MultiNLI and Quora benchmarks manifest that aESIM is superior to the original ESIM model.
Current Imaging Techniques for Lymph Node Staging in Prostate Cancer: A Review

Introduction: Lymph node metastases (LNM) represent a proven prognostic factor for biochemical recurrence (BCR)-free survival, metastatic free survival and overall survival in prostate cancer (PCa). Although pelvic node dissection remains the gold standard for the detection of LNM, novel imaging techniques are entering clinical practice, in the effort to improve LNM detection and spare unnecessary surgeries. Aim of the current review is to describe such imaging techniques and explore their advantages and limitations. Evidence Acquisition: The National Library of Medicine Database was searched for relevant articles published between January 2013 and August 2018. A wide search was performed including the combination of following words: "Prostate" and "Cancer" and "staging" and "Lymph Node" and "imaging" and ("MRI" or "PET"). The initial list of selected papers was enriched by individual suggestions of the authors of the present review. Evidence Synthesis: DWI-MRI in detection of lymph node invasion has a sensitivity and specificity of 41 and 94%, respectively. For SPIO MRI using ferumoxtran-10, the sensitivity for detection of LNM with short axis diameter of 5-10 mm is reported at 96.4%, compared to 28.5% with MRI alone. PSMA PET/CT is growing exponentially, both in the initial detection of LNM and for BCR evaluation. Fluciclovine PET could improve detection of subcentimetric pathologic lymph nodes. Sentinel lymph node techniques remain experimental and not validated in the field of PCa. Conclusions: Molecular imaging, particularly PSMA ligand PET imaging, present interesting diagnostic accuracy in LN diagnosis even in subcentimetric LN. DWI-MRI yields good results in LN involvement evaluation and the use of contrast agent such SPIO may improve the detection rate. The SLN technique is limited to experimental protocols and for intermediate or high-risk PCa. Prospective trials are awaited to evaluate the true clinical impact of these imaging techniques on PCa oncologic outcomes.