Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelling of municipal mortality due to haematological neoplasias in Spain

Objectives: To study the geographical pattern of mortality caused by haematological tumours in Spain at the municipal level using three Bayesian models and to compare their goodness of fit. Methods: The fitted Bayesian hierarchical models were: (1) the Besag York and Molliè (BYM) model; (2) a model based on zero-inflated Poisson (ZIP) distribution, which allowed a large number of event-free areas; and (3) a mixture of distributions that enabled discontinuities (jumps in the pattern) to be modelled. The three models allow smoothed relative risk maps to be obtained for the all countries. The goodness of fit was evaluated using the deviance information criteria. Results: The three models yielded similar results. The ZIP model plotted a pattern almost identical with the BYM model. The goodness-of-fit criteria indicate that the mixture model is the one that best fits our data. Haematological tumours display a geographical pattern that could be partly explained by environmental determinants, as many of the highest-risk towns are located in heavily industrialised areas. Conclusions: The choice of one or another model has scant practical consequences. The pattern of distribution supports the hypothesis that differences in lifestyles, air/industrial pollution and migratory phenomena may determine the pattern of urban mortality due to these tumours.
A Wear-Leveling-Aware Fine-Grained Allocator For Non-Volatile Memory

Emerging non-volatile memories (NVMs) are promising main memory for their advanced characteristics. However, the low endurance of NVM cells makes them vulnerable to frequent fine-grained updates. This paper proposes a Wear-leveling Aware Fine-grained Allocator (WAFA) for NVM. WAFA divides pages into basic memory units to support fine-grained updates. WAFA allocates the basic memory units of a page in a rotational manner to distribute fine-grained updates evenly on memory cells. The fragmented basic memory units of each page caused by the memory allocation and deallocation operations are reorganized by reform operation. We implement WAFA in Linux kernel 4.4. 4. Experimental results show that WAFA can reduce 81.1% and 40.1% of the total writes of pages over NVMalloc and nvm_alloc, the state-of-the-art wear-conscious allocator for NVM. Meanwhile, WAFA shows 48.6% and 42.3% performance improvement over NVMalloc and nvm_alloc, respectively.
FlashGPU: Placing New Flash Next to GPU Cores

We propose FlashGPU, a new GPU architecture that tightly blends new flash (Z-NAND) with massive GPU cores. Specifically, we replace global memory with Z-NAND that exhibits ultra-low latency. We also architect a flash core to manage request dispatches and address translations underneath L2 cache banks of GPU cores. While Z-NAND is a hundred times faster than conventional 3D-stacked flash, its latency is still longer than DRAM. To address this shortcoming, we propose a dynamic page-placement and buffer manager in Z-NAND subsystems by being aware of bulk and parallel memory access characteristics of GPU applications, thereby offering high-throughput and low-energy consumption behaviors.
ABM-SpConv: A Novel Approach to FPGA-Based Acceleration of ConvolutionaI NeuraI Network Inference

Hardware accelerators for convolutional neural network (CNN) inference have been extensively studied in recent years. The reported designs tend to utilize a similar underlying architecture based on multiplier-accumulator (MAC) arrays, which has the practical consequence of limiting the FPGA-based accelerator performance by the number of available on-chip DSP blocks, while leaving other resource under-utilized. To address this problem, we consider a transformation to the convolution computation, which leads to transformation of the accelerator design space and relaxes the pressure on the required DSP resources. We demonstrate that our approach enables us to strike a judicious balance between utilization of the on-chip memory, logic, and DSP resources, due to which, our accelerator considerably outperforms state of the art. We report the effectiveness of our approach on a Stratix-V GXA7 FPGA, which shows 55% throughput improvement, while using 6.25% less DSP blocks, compared to the best reported CNN accelerator on the same device.
Analog/Mixed-Signal Hardware Error Modeling for Deep Learning Inference

Analog/mixed-signal (AMS) computation can be more energy efficient than digital approaches for deep learning inference, but incurs an accuracy penalty from precision loss. Prior AMS approaches focus on small networks/datasets, which can maintain accuracy even with 2b precision. We analyze applicability of AMS approaches to larger networks by proposing a generic AMS error model, implementing it in an existing training framework, and investigating its effect on ImageNet classification with ResNet-50. We demonstrate significant accuracy recovery by exposing the network to AMS error during retraining, and we show that batch normalization layers are responsible for this accuracy recovery. We also introduce an energy model to predict the requirements of high-accuracy AMS hardware running large networks and use it to show that for ADC-dominated designs, there is a direct tradeoff between energy efficiency and network accuracy. Our model predicts that achieving $\lt0.4$% accuracy loss on ResNet-50 with AMS hardware requires a computation energy of at least $\sim 300$ fJ/MAC. Finally, we propose methods for improving the energy-accuracy tradeoff.
Nitrous oxide emission rate in response to plant, soil and microbial properties in marshes impacted by alien Spartina alterniflora

The effect of Spartina alterniflora (thereafter, abbreviated as Spartina) invasion on N2O emission has been debated, which is possibly related to difference in invasive areas. Therefore, the study investigated N2O emission rate using invasive gradients (native, native-Spartina coexisted and Spartina-dominated vegetations) and areas (Sanmen, Linhai and Jiaojiang) as fixed factors. Meantime, plant, soil and microbial parameters were determined for analyzing the mechanisms behind the N2O emission. The results showed that N2O emission rate significantly increased with Spartina invasion (p < 0.05), and was also impacted by invasive area (p < 0.05), as shown by two-way analysis of variance. Redundancy analysis showed that plant biomass, microbial biomass, potential denitrification rate, nitrate reductase activity, pH, soil organic carbon and NH4+-N contents correlated positively with the N2O emission rate (p < 0.05 or p < 0.01). Reversely, urease activity, total nitrogen and NO3− -N contents negatively affected the N2O emission rate (p < 0.05). These results suggest that except for Spartina invasion gradient, the invasive area is also a crucial factor in mediating soil N2O emission.
HIV upsurge in China's students

Thirty years ago, China experienced its first indigenous HIV/AIDS cases. Since then, HIV has spread from drug users and blood transfusion recipients to the general urban population, mainly through sexual routes. Particularly worrisome is the recent increase of HIV infection among college students, according to the Chinese Center for Disease Control and Prevention (CDC). The number of newly diagnosed college students has seen an annual growth rate ranging from 30 to 50% over the past several years. A proactive approach is required to spread public awareness of this trend and to promote aggressive prevention and treatment measures.
Web Tracking Technologies and Protection Mechanisms

Billions of users browse the Web on a daily basis, leaving their digital traces on millions of websites. Every such visit, every mouse move or button click may trigger a wide variety of hidden data exchanges across multiple tracking companies. As a result, these companies collect a vast amount of user's data, preferences and habits, that are extremely useful for online advertisers and profitable for data brokers, however very worrisome for the privacy of the users. In this \emph{3-hours tutorial} we will cover the vide variety of Web tracking technologies, ranging from simple cookies to advanced cross-device fingerprinting. We will describe the main mechanisms behind web tracking and what users can do to protect themselves. Moreover, we will discuss solutions Web developers can use to automatically eliminate tracking from the third-party content they include in their applications. This tutorial will be of interest to a \emph{general audience} of computer scientists, and \emph{we do not require any specific prerequisite knowledge} for attendees. We will cover the following tracking mechanisms: \begin{itemize} \item third-party cookie tracking, and other stateful tracking techniques that enables tracking across multiple websites, \item cookie respawning that is used to re-create deleted user cookies, \item cookie synching that allows trackers and ad agencies to synchronise user IDs across different companies, \item browser fingerprinting, including Canvas, WebRTC and AudioContext fingerprinting \item cross-browser device fingerprinting, allowing trackers to recognise users across several devices. \end{itemize} We will then demonstrate prevalence of such techniques on the Web, based on previous research. We will present the advertisement ecosystem and explain how Web technologies are used in advertisement, in particular in Real-Time-Bidding (RTB). We will explain how cookie synching is used in RTB and present recent analysis on how much a user's tracking data is worth. We will discuss the mechanisms the website owners use to automatically interact with the ad agencies, and explain its consequences on user's security and privacy. To help users protect themselves from Web tracking, we will give an overview of existing solutions. We'll start with the browser settings, and show that basic third-party cookie tracking is still possible even in the private browser mode of most common Web browsers. We then present privacy-protecting browser extensions and compare how efficient they are in protection from Web tracking. Then, we'll present possible protection mechanisms based on browser randomisation to protect from advanced fingerprinting techniques. Finally, we will present solutions for Web developers, who want to include third-party content in their websites, but would like to automatically remove any tracking of their users. In particular, we will discuss simple solutions that exist today for social plugins integration, and propose more advanced server-side based solutions that are a result of our own research.
Human Endogenous Retrovirus K Rec Forms a Regulatory Loop with MITF that Opposes the Progression of Melanoma to an Invasive Stage

The HML2 subfamily of HERV-K (henceforth HERV-K) represents the most recently endogenized retrovirus in the human genome. While the products of certain HERV-K genomic copies are expressed in normal tissues, they are upregulated in a number of pathological conditions, including various tumours. It remains unclear whether HERV-K(HML2)-encoded products overexpressed in cancer contribute to disease progression or are merely by-products of tumorigenesis. Here, we focus on the regulatory activities of the Long Terminal Repeats (LTR5_Hs) of HERV-K and on the potential role of the HERV-K-encoded Rec in melanoma. Our regulatory genomics analysis of LTR5_Hs loci indicates that Melanocyte Inducing Transcription Factor (MITF) binds to a canonical E-box motif (CA(C/T)GTG) within these elements in proliferative type of melanoma, and that depletion of MITF results in reduced HERV-K expression. In turn, experimentally depleting Rec in a proliferative melanoma cell line leads to lower mRNA levels of MITF and its predicted target genes. Furthermore, Rec knockdown leads to an upregulation of epithelial-to-mesenchymal associated genes and to an enhanced invasion phenotype of proliferative melanoma cells. Together these results suggest the existence of a regulatory loop between MITF and Rec that may modulate the transition from proliferative to invasive stages of melanoma. Because HERV-K(HML2) elements are restricted to hominoid primates, these findings might explain certain species-specific features of melanoma progression and point to some limitations of animal models in melanoma studies.
In Hardware We Trust: Gains and Pains of Hardware-assisted Security

Data processing and communication in almost all electronic systems are based on Central Processing Units (CPUs). In order to guarantee confidentiality and integrity of the software running on a CPU, hardware-assisted security architectures are used. However, both the threat model and the non-functional platform requirements, i.e. performance and energy budget, differ when we go from high-end desktop computers and servers to low-end embedded devices that populate the internet of things (IoT). For high-end platforms, a relatively large energy budget is available to protect software against attacks. However, measures to optimize performance give rise to microarchitectural side-channel attacks. IoT devices, in contrast, are constrained in terms of energy consumption and do not incorporate the performance enhancements found in high-end CPUs. Hence, they are less likely to be susceptible to microarchitectural attacks, but give rise to physical attacks, exploiting, e.g., leakage in power consumption or through fault injection. Whereas previous work mostly concentrates on a specific architecture, this paper covers the whole spectrum of computing systems, comparing the corresponding hardware architectures, and most relevant threats.
A Configurable Multi-Precision CNN Computing Framework Based on Single Bit RRAM

Convolutional Neural Networks (CNNs) play a vital role in machine learning. Emerging resistive random-access memories (RRAMs) and RRAM-based Processing-In-Memory architectures have demonstrated great potentials in boosting both the performance and energy efficiency of CNNs. However, restricted by the immature process technology, it is hard to implement and fabricate a CNN accelerator chip based on multi-bit RRAM devices. In addition, existing single bit RRAM based CNN accelerators only focus on binary or ternary CNNs which have more than 10% accuracy loss compared with full precision CNNs. This paper proposes a configurable multi-precision CNN computing framework based on single bit RRAM, which consists of an RRAM computing overhead aware network quantization algorithm and a configurable multi-precision CNN computing architecture based on single bit RRAM. The proposed method can achieve equivalent accuracy as full precision CNN but also with lower storage consumption and latency via multiple precision quantization. The designed architecture supports for accelerating the multi-precision CNNs even with various precision among different layers. Experiment results show that the proposed framework can reduce 70% computing area and 75% computing energy on average, with nearly no accuracy loss. And the equivalent energy efficiency is 1.6$\sim 8.6\times$ compared with existing RRM based architectures with only 1.07% area overhead.
LAcc: Exploiting Lookup Table-based Fast and Accurate Vector Multiplication in DRAM-based CNN Accelerator

PIM (Processing-in-memory)-based CNN (Convolutional neural network) accelerators leverage the characteristics of basic memory cells to enable simple logic and arithmetic operations so that the bandwidth constraint can be effectively alleviated. However, it remains a major challenge to support multiplication operations efficiently on PIM accelerators, in particular, DRAM-based PIM accelerators. This has prevented PIM-based accelerators from being immediately adopted for accurate CNN inference.In this paper, we propose LAcc, a DRAM-based PI M accelerator to support LUT-(lookup table) based fast and accurate multiplication. By enabling LUT based vector multiplication in DRAM, LAcc effectively decreases LUT size and improve its reuse. LAcc further adopts a hybrid mapping of weights and inputs to improve the hardware utilization rate. LAcc achieves 95 FPS at 5.3 W for Alexnet and 6.3× efficiency improvement over the state-of-the-art.
Mapping Quantum Circuits to IBM QX Architectures Using the Minimal Number of SWAP and H Operations

The recent progress in the physical realization of quantum computers (the first publicly available ones—IBM’s QX architectures—have been launched in 2017) has motivated research on automatic methods that aid users in running quantum circuits on them. Here, certain physical constraints given by the architectures which restrict the allowed interactions of the involved qubits have to be satisfied. Thus far, this has been addressed by inserting SWAP and H operations. However, it remains unknown whether existing methods add a minimum number of SWAP and H operations or, if not, how far they are away from that minimum—an NP-complete problem. In this work, we address this by formulating the mapping task as a symbolic optimization problem that is solved using reasoning engines like Boolean satisfiability solvers. By this, we do not only provide a method that maps quantum circuits to IBM’s QX architectures with a minimal number of SWAP and H operations, but also show by experimental evaluation that the number of operations added by IBM’s heuristic solution exceeds the lower bound by more than 100% on average. An implementation of the proposed methodology is publicly available at http://iic.jku. at/eda/research/ibm_qx_mapping.
System-level hardware failure prediction using deep learning

Disk and memory faults are the leading causes of server breakdown. A proactive solution is to predict such hardware failure at the runtime and then isolate the hardware at risk and backup the data. However, the current model-based predictors are incapable of using the discrete time-series data, such as the values of device attributes, which conveys high-level information of the device behavior. In this paper, we propose a novel deep-learning based prediction scheme for system-level hardware failure prediction. We normalize the distribution of samples’ attributes from different vendors to make use of diverse training sets. We propose a temporal Convolution Neural Network based model that is insensitive to the noise in the time dimension. Finally, we design a loss function to train the model with extremely imbalanced samples effectively. Experimental results from an open S.M.A.R. T data set and an industrial data set show the effectiveness of the proposed scheme.
Acceleration of DNN Backward Propagation by Selective Computation of Gradients

The training process of a deep neural network commonly consists of three phases: forward propagation, backward propagation, and weight update. In this paper, we propose a hardware architecture to accelerate the backward propagation. Our approach applies to neural networks that use rectified linear unit. Considering that the backward propagation results in a zero activation gradient when the corresponding activation is zero, we can safely skip the gradient calculation. Based on this observation, we design an efficient hardware accelerator for training deep neural networks by selectively computing gradients. We show the effectiveness of our approach through experiments with various network models. CCS CONCEPTS• Computer systems organization $\rightarrow$ Neural networks;
Identification of two distinct pathways of human myelopoiesis

Distinct myeloid differentiation pathways give rise to either neutrophil/monocyte or basophil/mast cell/eosinophil lineages. Goodbye CMPs Advances in single-cell analyses continue to profoundly change our understanding of hematopoiesis. Here, using single-cell RNA sequencing in conjunction with functional studies, Drissen et al. studied the differentiation potentials of human common myeloid progenitor (CMP) cells. They report that CMPs that have been defined on the basis of cell surface markers are a mixture of at least two cell types with the potential to give rise to either mast cells/basophils/eosinophils or neutrophils/monocytes. In other words, they report that CMPs having the potential to give rise to all myeloid cell types do not exist. Furthermore, they found that the cells in the former group retain the potential to give rise to megakaryocytes and erythrocytes, whereas cells in the latter groups retain lymphoid potential. Human myelopoiesis has been proposed to occur through oligopotent common myeloid progenitor (CMP) and lymphoid-primed multipotent progenitor (LMPP) populations. However, other studies have proposed direct commitment of multipotent cells to unilineage fates, without specific intermediary lineage cosegregation patterns. We here show that distinct human myeloid progenitor populations generate the neutrophil/monocyte and mast cell/basophil/eosinophil lineages as previously shown in mouse. Moreover, we find that neutrophil/monocyte potential selectively cosegregates with lymphoid lineage and mast cell/basophil/eosinophil potentials with megakaryocyte/erythroid potential early during lineage commitment. Furthermore, after this initial commitment step, mast cell/basophil/eosinophil and megakaryocyte/erythroid potentials colocalize at the single-cell level in restricted oligopotent progenitors. These results show that human myeloid lineages are generated through two distinct cellular pathways defined by complementary oligopotent cell populations.
BitBlade: Area and Energy-Efficient Precision-Scalable Neural Network Accelerator with Bitwise Summation

Deep Neural Networks (DNNs) have various performance requirements and power constraints depending on applications. To maximize the energy-efficiency of hardware accelerators for different applications, the accelerators need to support various bit-width configurations. When designing bit-reconfigurable accelerators, each PE must have variable shift-addition logic, which takes a large amount of area and power. This paper introduces an area and energy efficient precision-scalable neural network accelerator (BitBlade), which reduces the control overhead for variable shift-addition using bitwise summation method. The proposed BitBlade, when synthesized in a 28nm CMOS technology, showed reduction in area by 41% and in energy by 36-46% compared to the state-of-the-art precision-scalable architecture [14].
The road to independence: lived experiences of youth with chronic conditions and their parents compared.

PURPOSE To gain insight into the development of young persons with chronic conditions towards independence by comparing their lived experiences to those of their parents. METHODS Semi-structured interviews were held with 16 young persons (7 males, 9 females; 15-22 years) and one of their parents (n=16), asking about the young persons' daily lives and their development towards adulthood. Themes were deductively derived from the Skills for Growing Up framework, i.e. agency, living and daily activities, social and intimate relationships, education, work, leisure activities, transportation, and healthcare. Parents also reflected on how they dealt with the child's chronic condition. A paired thematic analysis was conducted. RESULTS Parents were often less convinced that their children could act independently than the young persons themselves. They were concerned about them and tended to interfere with their daily lives, often to the annoyance of their children. Also, parents often perceived more barriers in social participation, while young persons were more positive. CONCLUSION The perceptions of young persons and their parents clashed on living independently, intimate relationships, leisure activities, and healthcare. Young people might benefit from professional support in these domains to help them strengthen their autonomy and to prevent child-parent conflict and negative outcomes.