Miracle-dz/UniFAR-FTUs huggingface dataset:
Provable Gaussian Embedding with One Observation

The success of machine learning methods heavily relies on having an appropriate representation for data at hand. Traditionally, machine learning approaches relied on user-defined heuristics to extract features encoding structural information about data. However, recently there has been a surge in approaches that learn how to encode the data automatically in a low dimensional space. Exponential family embedding provides a probabilistic framework for learning low-dimensional representation for various types of high-dimensional data. Though successful in practice, theoretical underpinnings for exponential family embeddings have not been established. In this paper, we study the Gaussian embedding model and develop the first theoretical results for exponential family embedding models. First, we show that, under mild condition, the embedding structure can be learned from one observation by leveraging the parameter sharing between different contexts even though the data are dependent with each other. Second, we study properties of two algorithms used for learning the embedding structure and establish convergence results for each of them. The first algorithm is based on a convex relaxation, while the other solved the non-convex formulation of the problem directly. Experiments demonstrate the effectiveness of our approach.
Gamified services: How gameful experiences drive customer commitment

: Digital service providers are increasingly gamifying their services (i.e., enriching non-game services with game elements) to maintain a loyal customer base. In this research, the authors aim at examining whether gamification actually translates into user behavior that is beneficial for the firm (i.e., customer commitment). In viewing gamification as a co-creation process between service providers and users, this research provides novel insight on the impact of four generic gameful experiences (i.e., the internal and subjective user responses arising while interacting with game elements during gamified service use) on customer commitment. Findings reveal that gameful experiences can increase commitment but can also harm it. For instance, self-development represents the strongest driver, suggesting that it triggers commitment when individuals have feelings of being effective in their actions. When experiences of self-development coincide with social comparison, this commitment enhancing effect is leveraged even more. However, social comparison and expressive freedom should not be evoked at the same time as they yield a negative interplay, undermining customer commitment.
A Capacitance-to-Digits Readout Circuit for Integrated Humidity Sensors for Monitoring the In-Package Humidity of Ultra-Small Medical Implants

There remains a need for satisfactory integrated sensors that can monitor humidity inside small active implanted devices in which delicate electronics are operating. This paper describes a simple capacitance-to-digits readout circuit for a capacitive humidity sensor for ultra-small medical implants. The proposed readout circuit provides a larger dynamic range than conventional capacitance readout circuits by removing the parasitic capacitance from the measured capacitive input. The circuit was designed in XFAB's 0.6-μm CMOS technology and provides 1 % resolution in measuring relative humidity.
Textually Guided Ranking Network for Attentional Image Retweet Modeling

Retweet prediction is a challenging problem in social media sites (SMS). In this paper, we study the problem of image retweet prediction in social media, which predicts the image sharing behavior that the user reposts the image tweets from their followees. Unlike previous studies, we learn user preference ranking model from their past retweeted image tweets in SMS. We first propose heterogeneous image retweet modeling network (IRM) that exploits users' past retweeted image tweets with associated contexts, their following relations in SMS and preference of their followees. We then develop a novel attentional multi-faceted ranking network learning framework with textually guided multi-modal neural networks for the proposed heterogenous IRM network to learn the joint image tweet representations and user preference representations for prediction task. The extensive experiments on a large-scale dataset from Twitter site shows that our method achieves better performance than other state-of-the-art solutions to the problem.
Minimizing ESOP Expressions for Fully Homomorphic Encryption

With the rapid growth of cloud computing services, fully homomorphic encryption (FHE) has attracted much attention because a homomorphic evaluation can be directly performed on ciphertexts to ensure data privacy. This work explores the constraints and an associated minimization algorithm for exclusive sum-of-products (ESOP) expressions based on a homomorphism map and features of FHE. An efficient ESOP minimization algorithm based on the Sierpinski gasket and the triangle rule is proposed to reduce the maximum degree of the ESOP expression, thus relaxing the need to perform the recryption operation on ciphertexts. Experimental results show that about 23% of all candidate 4-variable functions can be further improved by the proposed algorithm to minimize the maximum degree of ESOP as compared with the results obtained using the traditional minimization algorithm.
SRAM Cell with Data-Aware Power-Gating Write-Asist for Near-Threshold Operation

This paper proposes a FinFET-based SRAM cell with data-aware power-gating write-assist to achieve both high read stability and write ability by using read-decoupled access transistors and power-gating PMOSs, respectively, for near-threshold operation. By adaptively cutting off the power-gating PMOS depending on the written data, the write disturbance from power supply can be eliminated, which facilitates more reliable write operation without any additional write assist circuit. Bit-interleaving scheme can be implemented in the proposed SRAM for soft error immunity while ensuring sufficient hold stability in half-selected cells during write operation. The proposed SRAM achieves read stability yield of 8. 4σ and write ability yield of 6. 1σ and consumes 0.47 pJ energy per operation at supply voltage of 0.4 V, a near-threshold voltage, in a 22-nm FinFET technology.
The relationship of Charlson comorbidity index with stent restenosis and extent of coronary artery disease

Objectives The objective of this study is to investigate the effect of comorbid conditions [Charlson comorbidity index (CCI)] on stent restenosis who underwent coronary angioplasty earlier. Methods Patients were divided into two groups; patients with critical restenosis [recurrent diameter stenosis >50% at the stent segment or its edges (5-mm segments adjacent to the stent) (Group 1; n = 53, mean age: 63.8 ± 9.9 years)] and patients with no critical restenosis [<50% obstruction (Group 2; n = 94, mean age: 62.1 ± 9.1 years)]. The CCI and modified CCI were used for the presence of comorbid conditions. The Gensini scoring system was used to assess the extent of coronary artery disease (CAD). Results Group 1 had a significantly greater CCI and modified CCI score compared to Group 2 (7.1 ± 3.7 vs. 5.6 ± 1.6, p = 0.006; 6.9 ± 3.6 vs. 4.5 ± 1.5, p = 0.008, respectively). There was a weak correlation, albeit significant, between the modified CCI score and restenosis percentage (r = 0.29, p < 0.001; r = 0.25, p = 0.003, respectively). Conclusions In conclusion, the CCI score is greater among patients with stent restenosis than those without. CCI score is higher among patients with a more diffuse CAD than with a milder disease extent.
Elevated Soluble Fas and FasL in Cerebrospinal Fluid and Serum of Patients With Anti-N-methyl-D-aspartate Receptor Encephalitis

Objective: Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is a severe autoimmune disorder that mainly affects children and young women. The Fas system contains both membrane-bound versions of Fas (mFas) and Fas ligand (mFasL), and soluble versions (sFas and sFasL), which play important roles in apoptosis and regulation of the immune system. Both the levels of sFas and sFasL and the role they play in anti-NMDAR disease pathogenesis remain unclear. Methods: Forty-eight pairs of cerebrospinal fluid (CSF) and serum were collected from patients with anti-NMDAR encephalitis, encephalitis of other causes or peripheral neuropathy. The CSF and serum concentrations of sFas and sFasL were determined with enzyme-linked immunosorbent assay. Results: CSF concentrations of sFas and sFasL were both increased in anti-NMDAR encephalitis patients compared with controls patients. Serum sFas levels were also elevated in anti-NMDAR encephalitis patients relative to controls. sFas and sFasL concentrations in CSF positively correlated with the modified Rankin scale (mRS) both at onset and 6-months follow-up. Conclusion: CSF sFas and sFasL levels were elevated in anti-NMDAR encephalitis patients, and reflect the disease severity of anti-NMDAR encephalitis.
A Clock-Tuned Discrete-Time Negative Capacitor Implemented Using Analog Samplers

The recent introduction of digital non-Foster circuits offers new methods for implementing negative capacitance and inductance, but may require a high-speed high-resolution digital signal processor, analog-to-digital converter, and digital-to-analog converter. Therefore, an alternative discrete-time design approach is presented, where a clock-tuned negative capacitor is implemented using analog samplers. The resulting design requires only two samplers, a differential amplifier, and an operational transconductance amplifier, eliminating the need for a digital signal processor and converters. In addition, it is shown that the negative capacitance can be tuned by the digital clock and is theoretically proportional to the clock period. Experimental results for a prototype demonstrate a tunable capacitance from −2.1 nF to −5.5 nF with |Q| > 2 for signal frequencies below approximately one-tenth of the clock frequency.
Behavioral Modeling of SAR ADCs in Simulink

This paper presents a toolbox for the behavioral simulation of SAR ADCs in Simulink®. The models include the most limiting circuit effects such as sampled thermal noise, capacitor mismatch, finite settling, comparator noise and offset. A user friendly interface is also included to allow study and high-level design of SAR ADCs, which is illustrated by means of a design example. It is also shown that the proposed toolbox is several orders of magnitude faster than electrical simulators, while keeping a high accuracy.
Deep CNNs for microscopic image classification by exploiting transfer learning and feature concatenation

Deep convolutional neural networks (CNNs) have become one of the state-of-the-art methods for image classification in various domains. For biomedical image classification where the number of training images is generally limited, transfer learning using CNNs is often applied. Such technique extracts generic image features from nature image datasets and these features can be directly adopted for feature extraction in smaller datasets. In this paper, we propose a novel deep neural network architecture based on transfer learning for microscopic image classification. In our proposed network, we concatenate the features extracted from three pretrained deep CNNs. The concatenated features are then used to train two fully-connected layers to perform classification. In the experiments on both the 2D-Hela and the PAP-smear datasets, our proposed network architecture produces significant performance gains comparing to the neural network structure that uses only features extracted from single CNN and several traditional classification methods.
Micropower Design of an Energy Autonomous RF Tag for UWB Localization Applications

This paper describes the architecture and the micropower design criteria of a battery-less, energy autonomous, individually addressable RF tag for UWB localization applications, with a focus on baseband circuitry. The tag includes a UHF rectifier, power conversion and management circuits, an addressable wake-up radio module, a microcontroller-based control unit, and circuits for UWB localization. The proposed circuit is suitable for UWB localization either by using passive backscattering of received UWB pulses, or by using active UWB pulses generators. Power for operation is scavenged from a modulated UHF carrier also used for addressing purposes. The circuit is implemented on discrete components in a 3.12 cm2 PCB area. The circuit can wake-up from fully discharged states and operates at distances as high as 10.8 m from a 2W-ERP source in the UHF 865–868 MHz RFID band with a +1.8 dBi receiving antenna. The quiescent power consumption of the tag is 3.88 μW, and the average power consumption at an addressing and activation rate of one time per second is 4.7 μW. The effectiveness of UWB localization was tested in a localization system based on time-difference-of-arrival (TDOA) estimations, consisting of multiple UWB readers and UHF transmitters.
Multi-Bit Pulsed-Latch Based Low Power Synchronous Circuit Design

Pulsed-latches emerge as an ideal sequencing element for low power digital circuit design, serving as an alternative of flip-flops. In this paper, low power multi-bit pulsed-latches are proposed to construct pipeline stages in synchronous digital circuits. A method of integrating the proposed multi-bit pulsed-latches in the commercial design flows is also introduced. With the multi-bit pulsed-latches, up to 45% power savings are achieved for a variety of ITC benchmark circuits and an ARM Cortex-M0 as compared to the flip-flop based designs in an industrial 28-nm FDSOI CMOS technology. Furthermore, the power consumption of the clock distribution network and the layout area are reduced by up to 83% and 16%, respectively, with the proposed multi-bit pulsed-latches as compared to the flip-flop based designs.
Efficient Inter-Task Communication in Tiled Many-Core System-on-Chip Architectures

Multicore system-on-chip architectures are dominant in all domains, such as desktop and server computers, smartphones and embedded devices. In tiled many-core systemon-chip architectures a large number of processor cores are replicated in a regular structure. Tiles contain one or multiple processor cores and other resources such as memories. The inter-tile communication is based on the network-on-chip methodology. The interface between the tiles and the network-on-chip is the network adapter. The very basic functionality of the network adapter is the packetization of data and protocol bridging. Sometimes the network adapter implements flow control above the network level or synchronization primitives. Generally, the work related to on-chip network adapters focuses on improvements on the level of the network-on-chip. Anyhow, complex software stacks become increasingly important on the computing side of network adapters. Those stacks add for example high-level message passing which abstract from the underlying hardware, and device sharing by software tasks running on top of an operating system. The network adapter shares similarities with network interface cards (NIC), especially in high performance computing (HPC). This thesis investigates the adoption of concepts from inter-node communication in HPC to on-chip communication and the co-optimization of the many-core architecture and programming model. The central contribution is the concept of a Network Adapter for Message Passing (NAMP). Motivated by the demands of aforementioned complex software stacks, the work focuses on features in the following areas: 1) Offloading higher-level protocol processing into the network adapter, 2) bypassing the operating system by virtualization, and 3) efficient event notification to the tasks. The NAMP addresses those areas with an offload of the message passing protocol handling into hardware, including the capability for collective communication between multiple tiles. It can be shared transparently by multiple task as a self-virtualized device. Furthermore, the NAMP concept includes a novel idea for efficient task notification, hardware-based operating system queue manipulation (HW-OSQM), that allows for elimination of all overhead by interrupts and can be easily generalized for arbitrary devices. Finally, the thesis contributes a concept for the migration of the communication channels during task migration as an integral part
Sevoflurane Inhibits the Th2 Response and NLRP3 Expression in Murine Allergic Airway Inflammation

Background Our colleagues have demonstrated an impressive therapeutic role of sevoflurane in a murine allergic airway inflammation model, but the mechanisms underlying this effect remain undefined. In this study, we tried to investigate the effect of sevoflurane on the resolution of allergic airway inflammation and to assess whether NLRP3 or the NLRP3 inflammasome is involved in this process. Methods Female (C57BL/6) mice were sensitized and challenged with ovalbumin (OVA). Then, some of the mice received MCC950 (10 mg/kg; i.p.) or 3% sevoflurane. Total and differential inflammatory cell numbers, proinflammatory cytokines in bronchoalveolar lavage fluid (BALF), the peribronchial inflammation density, and mucus production were evaluated. In addition, we analysed the protein levels of NLRP3, the apoptosis-associated speck-like protein containing the caspase activation and recruitment domain (ASC), pro-caspase-1, and caspase-1 in the lung tissue. Results We found that OVA-induced inflammatory cell recruitment to peribronchial regions, goblet cell hyperplasia, the serum levels of IgE, inflammatory cells, and the Th2 cytokine secretion in BALF was potently suppressed by sevoflurane with an efficacy comparable with that suppressed by MCC950 treatment. Furthermore, sevoflurane, similar to MCC950, clearly inhibited the OVA-induced activity of NLRP3 in the lungs. In addition, we found that OVA challenge failed to increase the expression of ASC, pro-caspase-1, and caspase-1 in the lungs and the levels of IL-18 and IL-1β in BALF. Conclusion Taken together, our data showed that sevoflurane ameliorated allergic airway inflammation by inhibiting Th2 responses and NLRP3 expression. The NLRP3 independent of inflammasomes participated in the pathogenesis of allergic asthma in this model.
A High Slew Rate CMOS OTA with Dynamic Current Boosting Paths

A high slew rate operational transconductance amplifier (OTA) based on the dynamic current boosting paths is presented. Two flipped voltage-follower (FVF) cells adaptively bias the input drivers and provide a class AB operation with capability of adding new input to output paths. In addition to the reduction of the settling time of the step response, these paths operate as new drivers that increase the small signal transconductance of the proposed amplifier. Simulation results in a TSMC 90 nm complementary metal oxide semiconductor (CMOS) technology show an 83 % decrease in the settling time and a 14.5 dB increase in the DC gain of the proposed class AB amplifier versus the conventional class A amplifier.
Averaging Techniques for the Analysis of Event Driven Models of All Digital PLLs

In this paper, we introduce a statistical approach for studying a special class of nonlinear dynamical systems such as ADPLLs and ADPLL networks, where the process driving the adjustment of the DCO frequency can be seen as ∑Δ modulation. We showed that, by applying the Frobenius-Perron operator to the governing equation, it is possible to find the invariant probability density which is valid for dynamically changing input of ∑Δ modulator. By using this, we show that the average behaviour of the corresponding complex system can be dramatically simplified and studied analytically.
Multi-objective Fuzzy Pattern Trees

This paper describes a multi-objective system for induction of fuzzy classifiers based on Fuzzy Pattern Trees. Fuzzy Pattern Trees is a tree-based hierarchical model, having as internal nodes, fuzzy logical operators and their leaves are composed by the combination of fuzzy terms and input attributes. For each class present in the problem a tree is created. Each tree will be a "logical class description" allowing the interpretation of the result. The construction method of the original trees was replaced by the Cartesian Genetic Programming, to explore the search space better. Two objectives guide the search: the accuracy of the classification and the interpretability. Therefore the NSGA II multi-objective algorithm was used for search solutions that contemplate both. The proposed Classifier was compared with Random Forests, Support Vector Machines and K Nearest Neighbors in several databases of the UCI Machine Learning Repository presenting a competitive and interpretable result. Another comparison made was the proposed model, and the Fuzzy Pattern Trees initially proposed. The proposed model showed competitive results and smaller trees.
Pathologic Replication-Independent Endogenous DNA Double-Strand Breaks Repair Defect in Chronological Aging Yeast

Reduction of physiologic replication-independent endogenous DNA double strand breaks (Phy-RIND-EDSBs) in chronological aging yeast increases pathologic RIND-EDSBs (Path-RIND-EDSBs). Path-RIND-EDSBs can occur spontaneously in non-dividing cells without any inductive agents, and they must be repaired immediately otherwise their accumulation can lead to senescence. If yeasts have DSB repair defect, retention of Path-RIND-EDSBs can be found. Previously, we found that Path-RIND-EDSBs are not only produced but also retained in chronological aging yeast. Here, we evaluated if chronological aging yeasts have a DSB repair defect. We found a significant accumulation of Path-RIND-EDSBs around the same level in aging cells and caffeine treated cells and at a much higher level in the DSB repair mutant cells. Especially in the mutant, some unknown sequence was found inserted at the breaks. In addition, % difference of cell viability between HO induced and non-induced cells was significantly greater in aging cells. Our results suggested that RIND-EDSBs repair efficiency declines, but is not absent, in chronological aging yeast which might promote senescence phenotype. When a repair protein is deficient, an alternative pathway might be employed or an end modification process might occur as inserted sequences at the breaks were observed. Restoring repair defects might slow down the deterioration of cells from chronological aging.
An Excitation Time Model for General-purpose Memristance Tuning Circuit

This paper presents a way to realize a simple yet accurate excitation time model of memristor-based circuits that are in the form of voltage dividers. According to the supported circuit structure, this model is compatible with a wide range of memristor applications, such as delay elements and analog memories. Memristance tuning based on excitation time estimation, i.e. pulse width, instead of using comparators, helps to save area and power. A general-purpose memristance tuning (GMET) circuit is proposed in order to demonstrate the simplicity of the proposed model and to evaluate its accuracy. Our model estimates are compared against the simulation results for the GMET circuit with VTEAM model of the memristor. The results show that the whole-range memristance shifts can be estimated with the worst case average error of 5.49%. They also show the worst case maximum error of 13.25%, which reduces to less than 7% when the operated memristance is higher than 2. 5kΩ.