Miracle-dz/UniFAR-FTUs huggingface dataset:
A Map Equation with Metadata: Varying the Role of Attributes in Community Detection

Much of the community detection literature studies structural communities, communities defined solely by the connectivity patterns of the network. Often networks contain additional metadata which can inform community detection such as the grade and gender of students in a high school social network. In this work, we introduce a tuning parameter to the content map equation that allows users of the Infomap community detection algorithm to control the metadata's relative importance for identifying network structure. On synthetic networks, we show that our algorithm can overcome the structural detectability limit when the metadata are well aligned with community structure. On real-world networks, we show how our algorithm can achieve greater mutual information with the metadata at a cost in the traditional map equation. Our tuning parameter, like the focusing knob of a microscope, allows users to "zoom in" and "zoom out" on communities with varying levels of focus on the metadata.
Forecasting Individualized Disease Trajectories using Interpretable Deep Learning

Disease progression models are instrumental in predicting individual-level health trajectories and understanding disease dynamics. Existing models are capable of providing either accurate predictions of patients prognoses or clinically interpretable representations of disease pathophysiology, but not both. In this paper, we develop the phased attentive state space (PASS) model of disease progression, a deep probabilistic model that captures complex representations for disease progression while maintaining clinical interpretability. Unlike Markovian state space models which assume memoryless dynamics, PASS uses an attention mechanism to induce "memoryful" state transitions, whereby repeatedly updated attention weights are used to focus on past state realizations that best predict future states. This gives rise to complex, non-stationary state dynamics that remain interpretable through the generated attention weights, which designate the relationships between the realized state variables for individual patients. PASS uses phased LSTM units (with time gates controlled by parametrized oscillations) to generate the attention weights in continuous time, which enables handling irregularly-sampled and potentially missing medical observations. Experiments on data from a realworld cohort of patients show that PASS successfully balances the tradeoff between accuracy and interpretability: it demonstrates superior predictive accuracy and learns insightful individual-level representations of disease progression.
End-To-End Diagnosis And Segmentation Learning From Cardiac Magnetic Resonance Imaging

Cardiac magnetic resonance (CMR) is used extensively in the diagnosis and management of cardiovascular disease. Deep learning methods have proven to deliver segmentation results comparable to human experts in CMR imaging, but there have been no convincing results for the problem of end-to-end segmentation and diagnosis from CMR. This is in part due to a lack of sufficiently large datasets required to train robust diagnosis models. In this paper, we propose a learning method to train diagnosis models, where our approach is designed to work with relatively small datasets. In particular, the optimization loss is based on multi-task learning that jointly trains for the tasks of segmentation and diagnosis classification. We hypothesize that segmentation has a regularizing effect on the learning of features relevant for diagnosis. Using the 100 training and 50 testing samples available from the Automated Cardiac Diagnosis Challenge (ACDC) dataset, which has a balanced distribution of 5 cardiac diagnoses, we observe a reduction of the classification error from 32% to 22%, and a faster convergence compared to a baseline without segmentation. To the best of our knowledge, this is the best diagnosis results from CMR using an end-to-end diagnosis and segmentation learning method.
Direct Time-of-Flight TCSPC Analytical Modeling Including Dead-Time Effects

The optimization of a TCSPC system requires modeling which considers the design specifications and parameters of the target application under different operating scenarios. Since single-photon detection is fundamentally a stochastic process, extensive behavioral Monte Carlo simulations are normally used. Their accuracy depends upon computation time. However, the trend towards larger SPAD arrays and emerging complex TDC sharing architectures requires much faster simulation methods. In this paper, a simple, fast and accurate analytical model is presented to address this need. It accounts for dead time effects which result in missed photon counts through the analysis of inhomogeneous continuous time Markov chain. The effective received power and photon detection rate are determined and the corresponding analytical histogram is created. This histogram is the basis for calculating time of flight and can be used to explore architectural alternatives and accelerate design verification. Outputs of the presented analytical model match those of Monte Carlo simulations, and are produced considerably faster. The computation time improvement grows with array sizes and this enables parametric analysis of TCSPC system.
Hardware Realization of Mixed-Signal Neural Networks with Modular Synapse-Neuron arrays

In this paper, a mixed-signal current-mode structure of a feed-forward neural network is implemented. In this network, neurons are divided and distributed as sub-neurons into parallel elements composing unified synapse-neuron building blocks in combination with the synapses. Although in this brief paper a resistive sigmoidal neuron is considered, the neuron is adaptable to other forms of transfer functions. The synapse structure employs AND gates in addition to weighted current mirrors to reduce the area of the design. As a proof of concept, a 4-3-2 CMOS-based network is implemented. The average and maximum power consumptions of the network are 0.93mW and 5.81 mW respectively. The area of the entire network is measured 142299. 5μm2. The network was successfully tested with a series of sample patterns.
A new approach of contextual recommendation based on the method of Hierarchical Analysis of Processes

Recommender systems are able to estimate the user's interest for resource given from some relative information to others similar users and to propriety of the resource. In this Memory, we introduced a new contextual recommendation approach based on the AHP Process Hierarchical Analysis method. This work consisted in making a bibliographic study on the works having proposed systems of recommendation based on the context of the users in the field of films. The goal is to design and develop a new approach to recommending movies based on user context. And we relied on methods of multi-criteria decision making (MCDM) and more precisely the method of Hierarchical Process Analysis (AHP) for context integration in the recommendation process.
Characterization of the residual self-interference power in full-duplex wireless systems

Full-duplex wireless communication systems are capable of transmitting and receiving signals in the same band. An important performance indicator of full-duplex systems is the amount of uncanceled self-interference, a.k. a. residual self-interference. This work investigates the distribution of the residual self-interference (SI) power in an analog post-mixer canceler. First, we focus on the impact of the SI channel coherence time on the distribution of the residual SI power and we analyze in detail the impact of the phase-noise caused by the up/down-conversion oscillators. The results achieved in this study confirm that the coherence time of the SI channel strongly impacts the distribution of the residual SI power. Considering a SI Rayleigh channel, we show that the residual SI power only follows a Gamma distribution in the limit case, when the coherence time tends to infinity (time-invariant channel). This work is the first step to understand the stochastic properties of the residual SI power, which is an important feature when digital-domain cancellation is jointly adopted with analog-domain cancellation to account for the estimated effects of the propagation channel.
Investigation of Monaural Front-End Processing for Robust ASR without Retraining or Joint-Training

In recent years, monaural speech separation has been formulated as a supervised learning problem, which has been systematically researched and shown the dramatical improvement of speech intelligibility and quality for human listeners. However, it has not been well investigated whether the methods can be employed as the front-end processing and directly improve the performance of a machine listener, i.e., an automatic speech recognizer, without retraining or joint-training the acoustic model. In this paper, we explore the effectiveness of the independent front-end processing for the multi-conditional trained ASR on the CHiME-3 challenge. We find that directly feeding the enhanced features to ASR can make 36.40% and 11.78% relative WER reduction for the GMM-based and DNN-based ASR respectively. We also investigate the affect of noisy phase and generalization ability under unmatched noise condition.
A New Metric to Find the Most Vulnerable Node in Complex Networks

This paper addresses the problem of finding the most synchrony vulnerable node in complex networks, i.e. the node which removal has the maximum influence on synchronizability of the network. In large-scale networks, brute search techniques are often not computationally cost effective in identifying the most vulnerable node(s). Here, considering the eigenratio of the Laplacian matrix of a graph as the synchronizability metric, we propose a measure in order to approximately rank nodes based on their impact on the synchronizability. This metric is cost effective since it needs a single eigen-decomposition of the Laplacian matrix of the connection graph. Simulation results show that the proposed metric is accurate enough in predicting the most vulnerable node in synthetic networks with scale-free, Watts-Strogatz and Erdős-Rényi structures.
Thwarting Fault Attacks against Lightweight Cryptography using SIMD Instructions

A growing number of connected objects, with their high performance and low-resources constraints, are embedding lightweight ciphers for protecting the confidentiality of the data they manipulate or store. Since those objects are easily accessible, they are prone to a whole range of physical attacks, one of which are fault attacks against which countermeasures are usually expensive to implement, especially on off-the-shelf devices. For such devices, we propose a new generic software countermeasure, using SIMD instructions available in almost any off-the-shelf devices, to thwart most fault attacks while preserving the performances of the targeted cipher.
Benchmarking Analogue Performance of Emerging Random Access Memory Technologies

In this work we present an evaluation routine aimed towards assessing the multibit capability of Resistive Random Access Memory (RRAM) technologies. We illustrate a characterization methodology for the maximum possible exploitation of the resistive states of a RRAM cell. Our characterization routine consists of a three phase algorithm: during the first it infers the polarity needed to induce a change in the device's conductance; the second stabilizes the resistive states of the device into a baseline resistance and during the third a sequence of pulses of increasing amplitude is used to determine the actual resistive states. This technology-agnostic methodology allows for efficient and high resolution partitioning of the cell's resistive operating range allowing them to operate in a truly analogue fashion. Demonstrating the maximum potential of RRAM cells in terms of closely packed resistive states can open new avenues for research in non-volatile memories, reconfigurable electronics and neuromorphic applications.
Pediatric Size Swallowable Glass Pill for Digestive Motility Analysis

A glass pill for digestive motility tracking with wireless communication is presented. The pill encapsulates three magnetic transmitter coils with a 125 kHz signal, the circuitry required to control the magnetic coil output and the battery. The system provides real time location of the pill inside the digestive tract of the patient using an external detector housed in a wearable jacket. The custom integrated circuit for this application reduces bill-of-materials (BOM) count and significant reduction in power consumption therefore resulting in further size reduction by adapting a smaller battery package. The outcome of the circuit integration enables the glass pill for pediatric use while providing a total pill diameter of 6 mm. The glass pill system operates continuously for 48 hours with a 1.55 V 8 mAh coin cell battery with an average power consumption of 250 microwatts.
Prostate imaging features that indicate benign or malignant pathology on biopsy

Accurate diagnosis of clinically significant prostate cancer is essential in identifying patients who should be offered treatment with curative intent. Modifications to the Gleason grading system in recent years show that accurate grading and reporting at needle biopsy can improve identification of clinically significant prostate cancers. Extracapsular extension of prostate cancer has been demonstrated to be an adverse prognostic factor with greater risk of metastatic spread than organ-confined disease. Tumor volume may be an independent prognostic factor and should be considered in conjunction with other factors. Multi-parametric magnetic resonance imaging (MP-MRI) has become an increasingly important tool in the diagnosis and characterization of prostate cancer. MP-MRI allows T2-weighted (T2W) anatomical imaging to be combined with functional and physiological assessment. Diffusion-weighted imaging (DWI) has shown greater sensitivity, specificity and negative predictive value compared to prostate specific antigen (PSA) testing and T2W imaging alone and has a more positive correlation with Gleason score and tumour volume. Dynamic gadolinium contrast-enhanced (DCE) imaging can exhibit difficulties in distinguishing prostatitis from malignancy in the peripheral zone, and between benign prostatic hyperplasia (BPH) and malignancies in the transition zone (TZ). Computer aided diagnosis utilizes software to aid radiologists in detecting and diagnosing abnormalities from diagnostic imaging. New techniques of quantitative MRI, such as VERDICT MRI use tissue-specific factors to delineate different cellular and microstructural phenotypes, characterizing tissue properties with greater detail. Proton MR spectroscopic imaging (MRSI) is a more technically challenging imaging modality than DCE and DWI MRI. Over the last decade, choline and prostate-specific membrane antigen (PSMA) positron emission tomography (PET) have developed as better tools for staging than conventional imaging. While hyperpolarized MRI shows promise in improving the imaging and differentiation of benign and malignant lesions there is further work required. Accurate reading and interpretation of diagnostic investigations is key to accurate identification of abnormal areas requiring biopsy, sparing those in whom benign or indolent disease can be managed by non-invasive means. Embracing and advancing existing technologies is essential in furthering this process.
Active Ranking with Subset-wise Preferences

We consider the problem of probably approximately correct (PAC) ranking $n$ items by adaptively eliciting subset-wise preference feedback. At each round, the learner chooses a subset of $k$ items and observes stochastic feedback indicating preference information of the winner (most preferred) item of the chosen subset drawn according to a Plackett-Luce (PL) subset choice model unknown a priori. The objective is to identify an $\epsilon$-optimal ranking of the $n$ items with probability at least $1 - \delta$. When the feedback in each subset round is a single Plackett-Luce-sampled item, we show $(\epsilon, \delta)$-PAC algorithms with a sample complexity of $O\left(\frac{n}{\epsilon^2} \ln \frac{n}{\delta} \right)$ rounds, which we establish as being order-optimal by exhibiting a matching sample complexity lower bound of $\Omega\left(\frac{n}{\epsilon^2} \ln \frac{n}{\delta} \right)$---this shows that there is essentially no improvement possible from the pairwise comparisons setting ($k = 2$). When, however, it is possible to elicit top-$m$ ($\leq k$) ranking feedback according to the PL model from each adaptively chosen subset of size $k$, we show that an $(\epsilon, \delta)$-PAC ranking sample complexity of $O\left(\frac{n}{m \epsilon^2} \ln \frac{n}{\delta} \right)$ is achievable with explicit algorithms, which represents an $m$-wise reduction in sample complexity compared to the pairwise case. This again turns out to be order-wise unimprovable across the class of symmetric ranking algorithms. Our algorithms rely on a novel {pivot trick} to maintain only $n$ itemwise score estimates, unlike $O(n^2)$ pairwise score estimates that has been used in prior work. We report results of numerical experiments that corroborate our findings.
A Batteryless and Single-Inductor DC-DC Boost Converter for Thermoelectric Energy Harvesting Application with 190mV Cold-Start Voltage

This paper presents a batteryless DC-DC boost converter for thermoelectric energy harvesting application. With a stepping-up architecture and by inductor sharing, only one off-chip inductor is employed. Fabricated in 0. 18μm CMOS process, the chip can be cold-started at 190mV and sustain operation with a minimum input voltage of 50mV. Zero-current switching (ZCS) and maximum power point tracking (MPPT) techniques are utilized to enhance the measured peak efficiency to 60%. And the output voltage can be regulated from IV to 1.6V.
Frequency Fluctuations in CMOS-MEMS Oscillators: Towards the Thermomechanical Limit

We measured the frequency stability of fully integrated self-sustained CMOS-MEMS oscillators and compared it with the theoretical thermomechanical limit. Experimental data obtained from a fully CMOS compatible CC-Beam MEMS resonator with on-chip capacitive readout and electrostatic excitation in open- and closed-loop configuration is provided. Our results show that the frequency stability is 1.8 ppm in ambient air conditions for an integration time of 40ms, and 0.63 ppm in vacuum, being only two times larger than the ultimate limit set by the thermomechanical vibrations.
Comparison of the Clinical Performance of Refractive Rotationally Asymmetric Multifocal IOLs with Other Types of IOLs: A Meta-Analysis

Objective To compare the clinical performance of refractive rotationally asymmetric multifocal intraocular lens (IOLs) with spherical monofocal, accommodating, and bifocal IOLs. Methods A comprehensive literature search of PubMed, EMBASE, Cochrane Controlled Trials Register, and Web of Science up to February 2017 was performed to identify randomized controlled trials (RCT) and comparative cohort studies. Main outcomes were uncorrected distance visual acuity (UDVA), uncorrected intermediate visual acuity (UIVA), uncorrected near visual acuity (UNVA), higher-order aberrations (HOAs), MTF, Strehl ratio, and residual sphere and cylinder. Results Mplus provided significantly worse UDVA than spherical monofocal IOLs (WMD: 0.13, P=0.008), but significantly better UIVA than high-add bifocal IOLs (WMD: −0.19, P < 0.00001), spherical monofocal IOLs (WMD: −0.12, P < 0.0001), and accommodating IOLs (WMD: −0.21, P < 0.00001). Mplus provided significantly worse UNVA than high-add bifocal IOLs (WMD: 0.07, P < 0.00001), but significantly better UNVA than spherical monofocal IOLs (WMD: −0.19, P < 0.00001). Mplus resulted in significantly higher HOAs than high-add bifocal IOLs (WMD: 0.38, P < 0.00001) and spherical monofocal IOLs (WMD: 0.51, P=0.0004). Mplus provided a significantly lower MTF cut-off and Strehl ratio than other type of IOLs. Conclusion The Mplus IOLs perform best regarding intermediate visual acuity whereas they lack in distance visual acuity compared to monofocal IOLs and near visual acuity compared to bifocal IOLs. These results may be due to structure of Mplus IOLs resulting in higher-order aberrations.
Playing on a Level Field: Sincere and Sophisticated Players in the Boston Mechanism with a Coarse Priority Structure

Who gains and who loses from a manipulable school choice mechanism? We examine this question with a focus on the outcomes for sincere and sophisticated students, and present results concerning their absolute and relative gains under the manipulable Boston Mechanism (BM) as compared with the strategy-proof Deferred Acceptance (DA). The absolute gain of a student of a certain type is the difference between her expected utility under (an equilibrium of) BM and her utility under (the dominant-strategy equilibrium of) DA. Holding everything else constant, one type of a player has relative gain with respect to another type if her absolute gain is higher. Prior theoretical works presented inconclusive results regarding the absolute gains of both types of students, and predicted (or assumed) positive relative gains for sophisticated types compared to sincere types. The empirical evidence is also mixed, with different markets exhibiting very different behaviors. We extend the previous results and explain the inconsistent empirical findings using a large random market approach. We provide robust and generic results of the "anything goes" variety for markets with a coarse priority structure. That is, in such markets there are many sincere and sophisticated students who prefer BM to DA (positive absolute gain), and vice versa (negative absolute gain). Furthermore, some populations may even get a relative gain from being sincere (and being perceived as such). We conclude by studying market forces that can influence the choice between the two mechanisms.
Integrated Stretch Processed S-Band Digital Radar System

A stretch processed radar system is presented in this paper. The radar system operates at S-band and employs stretch processing. Stretch processing helps a radar system to process wideband signals to increase its resolution, while relaxing its analog-to-digital converter's sampling rate requirement. The reported radar system exhibits a high dynamic range which enables it to be used in variety of applications such as surveillance systems. Furthermore, the radar system architecture comprises of multiple receiver channels which enable the radar to detect objects of interests at certain angular locations and reject jammers. The digital radar testbed was built using microwave components. Moreover, the radar system is integrated on a single die chip using IBM 180 nm CMOS process with a low power consumption, small form factor, and high performance.
Persistence semantics for weak memory: integrating epoch persistency with the TSO memory model

Emerging non-volatile memory (NVM) technologies promise the durability of disks with the performance of volatile memory (RAM). To describe the persistency guarantees of NVM, several memory persistency models have been proposed in the literature. However, the formal semantics of such persistency models in the context of existing mainstream hardware has been unexplored to date. To close this gap, we integrate the buffered epoch persistency model with the 'total-store-order' (TSO) memory model of the x86 and SPARC architectures. We thus develop the PTSO ('persistent' TSO) model and formalise its semantics both operationally and declaratively. We demonstrate that the two characterisations of PTSO are equivalent. We then formulate the notion of persistent linearisability to establish the correctness of library implementations in the context of persistent memory. To showcase our formalism, we develop two persistent implementations of a queue library, and apply persistent linearisability to show their correctness.