Miracle-dz/UniFAR-FTUs huggingface dataset:
Establishing a prediction model for prostate cancer bone metastasis

We collected clinical data from 308 prostate cancer (PCa) patients to investigate the clinical characteristics and independent risk factors of bone metastasis (BM) and to establish a prediction model for BM of PCa and determine the necessity of bone scans. Univariate and multivariate analyses were performed based on age, biopsy Gleason score (BGS), clinical tumor stage (cTx), total prostate specific antigen (tPSA), free prostate specific antigen (fPSA), fPSA/tPSA, prostate volume, alkaline phosphatase (ALP), serum calcium and serum phosphorus. Moreover, 80 of the 308 PCa patients had a PI-RADS v2 score and were analysed retrospectively. The univariate analysis showed that the BGS, cTx, tPSA, fPSA, prostate volume and ALP were significant. The multivariate logistic regression analysis showed significant differences among the BGS, cTx, tPSA and ALP. Four cases should be highly suspected with BM: (i) cTl-cT2, BGS ≤7, ALP >120 U/L and tPSA >90.64 ng/ml; (ii) cTl-cT2, BGS ≥8, and ALP >120 U/L; (iii) cT3-cT4, BGS ≤7, and ALP >120 U/L; and (iv) cT3-cT4 and BGS ≥8. After the PI-RADS v2 score was included in the model, the AUC of the prediction model rose from 0.884 (95% CI: 0.813-0.996) to 0.934 (95% CI: 0.883-0.986). This model may help determine the necessity of bone scans to diagnose BM for PCa patients.
Super-Resolution Digital Pathology Image Processing of Bone Marrow Aspirate and Cytology Smears and Tissue Sections

Background: Accurate digital pathology image analysis depends on high-quality images. As such, it is imperative to obtain digital images with high resolution for downstream data analysis. While hematoxylin and eosin (H&E)-stained tissue section slides from solid tumors contain three-dimensional information, these data have been ignored in digital pathology. In addition, in cytology and bone marrow aspirate smears, the three-dimensional nature of the specimen has precluded efficient analysis of such morphologic data. An individual image snapshot at a single focal distance is often not sufficient for accurate diagnoses and multiple whole-slide images at different focal distances are necessary for diagnostics. Materials and Methods: We describe a novel computational pipeline and processing program for obtaining a super-resolved image from multiple static images at different z-planes in overlapping but separate frames. This program, MULTI-Z, performs image alignment, Gaussian smoothing, and Laplacian filtering to construct a final super-resolution image from multiple images. Results: We applied this algorithm and program to images of cytology and H&E-stained sections and demonstrated significant improvements in both resolution and image quality by objective data analyses (24% increase in sharpness and focus). Conclusions: With the use of our program, super-resolved images of cytology and H&E-stained tissue sections can be obtained to potentially allow for more optimal downstream computational analysis. This method is applicable to whole-slide scanned images.
Static-eye against hand-eye visual servoing

In this paper, a comparison between static-eye and hand-eye configurations for the point-to-point visual servoing task realized with position-based and image-based control laws is presented. For these four configurations, the effect of uncertainty on intrinsic and extrinsic calibration parameters is investigated. The results show local stability for all configurations with small calibration errors and a steady state error for the hand-eye position-based one. Simulations have been carried out in order to confirm the theoretical results and evaluate the effects of the uncertainty in terms of stability region.
Efficient sparsification of dense circuit matrices in model order reduction

The integration of more components into ICs due to the ever increasing technology scaling has led to very large parasitic networks consisting of million of nodes, which have to be simulated in many times or frequencies to verify the proper operation of the chip. Model Order Reduction techniques have been employed routinely to substitute the large scale parasitic model by a model of lower order with similar response at the input/output ports. However, all established MOR techniques result in dense system matrices that render their simulation impractical. To this end, in this paper we propose a methodology for the sparsification of the dense circuit matrices resulting from Model Order Reduction, which employs a sequence of algorithms based on the computation of the nearest diagonally dominant matrix and the sparsification of the corresponding graph. Experimental results indicate that a high sparsity ratio of the reduced system matrices can be achieved with very small loss of accuracy.
Rooftop Detection using Aerial Drone Imagery

We present a rooftop detection algorithm using aerial RGBD and near infrared data which uses lower computational resources than algorithms requiring GPUs. The depth data is extracted from multiview images captured by drones using photogrammetry. Our approach is cost effective as compared to LIDAR surveying and has lower edge blurring. It is also novel in terms of segmenting clutter due to objects such as overhead water tanks on roofs. This helps is determining the actual free roof area that would be available for applications like solar panel deployment. The algorithm was evaluated on the aerial imagery of rooftops on a hill slope of size 12876×10533 pixels and an F1 score of 88.27% was obtained. The algorithm ran in under 2 minutes on a Google Cloud instance with Intel Xeon E5 processor.
Scalable design for field-coupled nanocomputing circuits

Field-coupled Nanocomputing (FCN) technologies are considered as a solution to overcome physical boundaries of conventional CMOS approaches. But despite ground breaking advances regarding their physical implementation as e.g. Quantum-dot Cellular Automata (QCA), Nanomagnet Logic (NML), and many more, there is an unsettling lack of methods for large-scale design automation of FCN circuits. In fact, design automation for this class of technologies still is in its infancy - heavily relying either on manual labor or automatic methods which are applicable for rather small functionality only. This work presents a design method which - for the first time - allows for the scalable design of FCN circuits that satisfy dedicated constraints of these technologies. The proposed scheme is capable of handling around 40000 gates within seconds while the current state-of-the-art takes hours to handle around 20 gates. This is confirmed by experimental results on the layout level for various established benchmarks libraries.
Efficient Budget Allocation and Task Assignment in Crowdsourcing

Requesters in crowdsourcing marketplaces would like to efficiently allocate a fixed budget, among the set of tasks to be completed, which are of varying difficulty levels. The uncertainty in the arrival and departure of workers and the diversity in their skill levels add to the challenge, as minimizing the overall completion time is also an important concern. Current literature focuses on sequential allocation of tasks, i.e., task assignment to one worker at a time, or assumes the task difficulties to be known in advance. In this paper, we study the problem of efficient budget allocation under dynamic worker pool in crowdsourcing. Specifically, we consider binary labeling tasks for which the budget allocation problem can be cast as one of finding the optimal policy for a Markov decision process. We present a mathematical framework for modeling the problem and propose a class of algorithms for obtaining its solution. Experiments on simulated as well as real data demonstrate the capability of these algorithms to achieve performance very close to sequential allocation in much less time and their superiority over naive allocation strategies.
AxDNN: towards the cross-layer design of approximate DNNs

Thanks for the inborn error resistance of neural networks, approximate computing has become a promising and hardware friendly technique to improve the energy efficiency of DNNs. From the layer of algorithms, architectures, to circuits, there are many possibilities to implement approximate DNNs. However, the complicated interaction between major design concerns, e.g., power performance, and the lack of an efficient simulator cross multiple design layers have generated suboptimal solutions of approximate DNNs through the conventional design method. In this paper, we present a systematical framework towards the cross-layer design of approximation DNNs. By introducing hardware imperfection to the training phase, the accuracy of DNN models can be recovered by up to 5.32% when the most aggressive approximate multiplier has been used. Integrated with the techniques of activation pruning and voltage scaling, the energy efficiency of the approximate DNN accelerator can be improved by 52.5% on average. We also build a pre-RTL simulation environment where we can easily express accelerator architectures, try the combination of different approximate strategies, and evaluate the power consumption. Experiments demonstrate the pre-RTL simulation has achieved ~20X speed up compared with traditional RTL method when evaluating the same target. The convenient pre-RTL simulation helps us to quickly figure out the trade-off between accuracy and energy at the design stage for an approximate DNN accelerator.
A spatially explicit integrative model for estimating the risk of wildfire impacts in New-Caledonia

ABSTRACT In this study, we present a spatially explicit bayesian model which is able to predict the distribution of potential fire ignition at a grid resolution of over the entire main island of New-Caledonia. This statistical model, when used in conjunction with a mechanistic fire model (FlamMap), is able to estimate the comparative and spatialized risk of fire impact on specific areas for specific issues such as biodiversity or erosion. The input data includes variables related to the physical environment such as the topography, climate, and some geographical indicators relating to human influences such as population density and type of land property. Flowchart computation for the specific cell i. The grey rectangles denote the input data, the trapezoids denote the intermediate indices and the rounded corners rectangles denote the integrated wildfire risk indices. The connection legends indicates the corresponding equations and below certain nodes a drawing describes an example related to the main variables and the associated area used. Graphical Abstract
Root Rank: A Relational Operator for KWS Result Ranking

A popular approach to hosting Keyword Search Systems (KWS) on relational DBMS platforms is to employ the Candidate Network framework. The quality of a Candidate Network-based search is critically dependent on the scoring function used to rank the relevant answers. In this paper, we first demonstrate, through a detailed empirical study, that the Labrador scoring function provides the best user relevance among contemporary Candidate Network scoring functions. Efficiently incorporating the Labrador function, however, is rendered difficult due to its Result Set Dependent (RSD) characteristic, wherein the distribution of keywords in the query results influences the ranking. In this paper, we investigate addressing the RSD challenge through inclusion of custom operators within the database engine. Specifically, we propose and evaluate an operator called Root Rank, which performs result ranking in the root of the query execution plan. The Root Rank operator has been implemented on a PostgreSQL codebase, and its performance profiled over real-world data sets, including DBLP and Wikipedia. Our experimental observations indicate that the Root Rank operator is highly successful in delivering processing times that are comparable to, or better than, those of non-RSD implementations. We expect these results to aid in the organic hosting of KWS functionality on database systems.
Software Deﬁned Architectures for Data Analytics

Data analytics applications increasingly are complex workﬂows composed of phases with very diﬀerent program behaviors (e.g., graph algorithms and machine learning, algorithms operating on sparse and dense data structures, etc). To reach the levels of eﬃ-ciency required to process these workﬂows in real time, upcoming architectures will need to leverage even more workload specialization. If, at one end, we may ﬁnd even more heterogenous processors composed by a myriad of specialized processing elements, at the other end we may see novel reconﬁgurable architectures, composed of sets of functional units and memories interconnected with (re)conﬁgurable on-chip networks, able to adapt dynamically to adapt the workload characteristics. Field Programmable Gate Arrays are more and more used for accelerating various workloads and, in particular,inferencing in machine learning, providing higher eﬃciency than other solutions. However, their ﬁne-grained nature still leads to issues for the design software and still makes dynamic reconﬁguration impractical. Future, more coarse-grained architectures could oﬀer the features to execute diverse workloads at high eﬃciency while providing better reconﬁguration mechanisms for dynamic adaptability. Nevertheless, we argue that the challenges for reconﬁgurable computing remain in the software. In this position paper, we describe a possible toolchain for reconﬁgurable architectures targeted at data analytics.
Analyzing Interaction Dynamics in Social Networks through Social Yield

Determination of the social determinants that can influence the interaction dynamics of an individual is a challenge. Understanding the interaction dynamics among individuals has widespread applications in computational journalism, recommendation systems etc. In this paper, we intend to investigate the social determinants that can influence the interaction dynamics of an individual. We introduce social yield as a measure of the recent success that an individual attains from his recent interactions. We, further, attempt to empirically investigate the relationship between the interaction dynamics of an individual and his social yield. Our preliminary findings indicate that the change in interaction patterns of an individual in both scientific collaboration network and Twitter can be explained through social yield of the user.
GraphSAR: a sparsity-aware processing-in-memory architecture for large-scale graph processing on ReRAMs

Large-scale graph processing has drawn great attention in recent years. The emerging metal-oxide resistive random access memory (ReRAM) and ReRAM crossbars have shown huge potential in accelerating graph processing. However, the sparse feature of natural graphs hinders the performance of graph processing on ReRAMs. Previous work of graph processing on ReRAMs stored and computed edges separately, leading to high energy consumption and long latency of transferring data. In this paper, we present GraphSAR, a sparsity-aware processing-in-memory large-scale graph processing accelerator on ReRAMs. Computations over edges are performed in the memory, eliminating overheads of transferring edges. Moreover, graphs are divided considering the sparsity. Subgraphs with low densities are further divided into smaller ones to minimize the waste of memory space. According to our extensive experimental results, GraphSAR achieves 4.43x energy reduction and 1.85x speedup (8.19x lower energy-delay product, EDP) against previous graph processing architecture on ReRAMs (GraphR [1]).
Log-quantized stochastic computing for memory and computation efficient DNNs

For energy efficiency, many low-bit quantization methods for deep neural networks (DNNs) have been proposed. Among them, logarithmic quantization is being highlighted showing acceptable deep learning performance. It also simplifies high-cost multipliers as well as reducing memory footprint drastically. Meanwhile, stochastic computing (SC) was proposed for low-cost DNN acceleration and the recently proposed SC multiplier improved the accuracy and latency significantly which are main drawbacks of SC. However, in their binary-interfaced system which yet costs much less than storing all stochastic stream, quantization is basically linear as same as conventional fixed-point binary. We applied logarithmically quantized DNNs to the state-of-the-art SC multiplier and studied how it can benefit. We found that SC multiplication on logarithmically quantized input is more accurate and it can help fine-tuning process. Furthermore, we designed the much low-cost SC-DNN accelerator utilizing the reduced complexity of inputs. Finally, while logarithmic quantization benefits data flow, proposed architecture achieves 40% and 24% less area and power consumption than the previous SC-DNN accelerator. Its area X latency product is smaller even than the shifter based accelerator.
Relational-like Query Language for Archetypes in Standardized Electronic Health Records Databases

The standardized Electronic Health Records (EHRs) database aims to support a web based data management system activity, at global level. The standardized EHRs (archetype-based systems) will have portable queries. At the present stage, a number of tools and interfaces are being proposed. However, it is important to develop a suitable query capability that matches the level of a database query language, such as SQL or XQuery. We demonstrate a high-level query language interface for healthcare workers based on our earlier studies. The development involves two phases. The first phase is dynamic generation of graphical user interface from archetypes. The second phase is providing high-level querying functionality. The system has been developed using free software and has been tested for sample set of fifteen queries for relational database operations using NoSQL persistence (MongoDB). The proposal is successful in enhancing the ease of use for hospital workers in medical and healthcare domain.
Cell division: weight bit-width reduction technique for convolutional neural network hardware accelerators

The datapath bit-width of hardware accelerators for convolutional neural network (CNN) inference is generally chosen to be wide enough, so that they can be used to process upcoming unknown CNNs. Here we introduce the cell division technique, which is a variant of function-preserving transformations. With this technique, it is guaranteed that CNNs that have weights quantized to fixed-point format of arbitrary bit-widths, can be transformed to CNNs with less bit-widths of weights without any accuracy drop (or any accuracy change). As a result, CNN hardware accelerators are released from the weight bit-width constraint, which has been preventing them from having narrower datapaths. In addition, CNNs that have wider weight bit-widths than those assumed by a CNN hardware accelerator can be executed on the accelerator. Experimental results on LeNet-300-100, LeNet-5, AlexNet, and VGG-16 show that weights can be reduced down to 2--5 bits with 2.5X--5.2X decrease in weight storage requirement and of course without any accuracy drop.
Feature Subset Selection using Adaptive Differential Evolution: An Application to Banking

In this paper, we developed a feature subset selection method by employing Adaptive Differential Evolution as a wrapper. The proposed wrapper utilizes four independent classifiers namely Logistic Regression, Probabilistic Neural Network, Naive Bayes and Support Vector Machine. We employed the Matthews Correlation Coefficient (MCC) as the fitness function or evaluation measure. In order to demonstrate the efficacy of the proposed method, we tested on three datasets, of which two are related to credit scoring and one is related to financial statement fraud. Our proposed method yielded better results than other standard methods in the literature as well as Differential Evolution. We also performed a statistical significance test i.e. t-test at 1% level of significance, which infers that some of the proposed wrappers are statistically one and the same.
MalReG: Detecting and Analyzing Malicious Retweeter Groups

Given a retweeter network in Twitter for any event, how can we detect the group of users that collude to retweet together maliciously? A large number of retweets of a post often indicates the virality of the post. It also helps increase the visibility and volume of hashtags, topics or URLs, to promote the event associated with it. Our primary hunch is that there is synchronization or indicative pattern in the behavior of such users. In this paper, we propose (i) MalReG, a novel algorithm to detect retweeter groups, and (ii) a set of 23 group-based features (entropy-based and temporal-based) to train a supervised model to identify malicious retweeter groups (MRG). We present experiments on three real-world datasets with more than 10 million retweets crawled from Twitter. MalReG identifies 1, 017 retweeter groups present in our dataset. We train a supervised learning model to detect MRG which achieves 0.921 ROC AUC using Random Forest, outperforming the baseline by 7.97% higher AUC. Additionally, we perform geographical location-based and temporal analysis of these groups. Interestingly, we find the presence of the same group, retweeting different political events that took place in different continents at different times. We also discover masquerading techniques used by MRG to evade detection.
MDP-trees: multi-domain macro placement for ultra large-scale mixed-size designs

In this paper, we present a new hybrid representation of slicing trees and multi-packing trees, called multi-domain-packing trees (MDP-trees), for macro placement to handle ultra large-scale multi-domain mixed-size designs. A multi-domain design typically consists of a set of mixed-size domains, each with hundreds/thousands of large macros and (tens of) millions of standard cells, which is often seen in modern high-end applications (e.g., 4G LTE products and upcoming 5G ones). To the best of our knowledge, there is still no published work specifically tackling the domain planning and macro placement simultaneously. Based on binary trees, the MDP-tree is very efficient and effective for handling macro placement with multiple domains. Previous works on macro placement can handle only single-domain designs, which do not consider the global interactions among domains. In contrast, our MDP-trees plan domain regions globally, and optimize the interconnections among domains and macro/cell positions simultaneously. The placement area of each domain is well reserved, and the macro displacement is minimized from initial macro positions of the design prototype. Experimental results show that our approach can significantly reduce both the average half-perimeter wirelength and the average global routing wirelength.
Fault tolerance in neuromorphic computing systems

Resistive Random Access Memory (RRAM) and RRAM-based computing systems (RCS) provide energy-efficient technology options for neuromorphic computing. However, the applicability of RCS is limited by reliability problems that arise from the immature fabrication process. In order to take advantage of RCS in practical applications, fault-tolerant design is a key challenge. We present a survey of fault-tolerant designs for RRAM-based neuromorphic computing systems. We first describe RRAM-based crossbars and training architectures in RCS. Following this, we classify fault models into different categories, and review post-fabrication testing methods. Subsequently, online testing methods are presented. Finally, we present various fault-tolerant techniques that were designed to tolerate different types of RRAM faults. The methods reviewed in this survey represent recent trends in fault-tolerant designs of RCS, and are expected motivate further research in this field.