Miracle-dz/UniFAR-FTUs huggingface dataset:
Technical Note: Split Algorithm in O(n) for the Vehicle Routing Problem

The Split algorithm is an essential building block for route-first cluster-second heuristics and modern genetic algorithms for vehicle routing problems. The recent survey of [Prins, Lacomme and Prodhon, Transport Res. C (40), 179-200] lists more than 70 articles that use this technique. In the vehicle routing literature, Split is usually assimilated to the search for a shortest path in a directed acyclic graph $\mathcal{G}$ and solved in $O(nB)$ using Bellman's algorithm, where $n$ is the number of delivery points and $B$ is a bound on the number of customers per route, due to the capacity constraints. Some linear-time algorithms are also known for this problem as a consequence of a Monge property of $\mathcal{G}$. In this article, we highlight a stronger property of this graph, leading to a simpler alternative algorithm in $O(n)$. Experimentally, we observe that the approach is faster than the classical Split for problem instances of practical size. We also extend the method to deal with a limited fleet and soft capacity constraints.
Head motion: the dirty little secret of neuroimaging in psychiatry.

Psychiatry is at a crossroads when choosing final samples for analysis of neuroimaging data. Many patient populations exhibit significantly increased motion in the scanner compared with healthy controls, suggesting that more patients would need to be excluded to obtain a clean sample. However, this need is often overshadowed by the extensive amount of time and effort required to recruit these valuable and uncommon samples. This commentary sheds light on the impact of motion on imaging studies, drawing examples from psychiatric patient samples to better understand how head motion can confound interpretation of clinically oriented questions. We discuss the impact of even subtle motion artifacts on the interpretation of results as well as how different levels of stringency in quality control can affect findings within nearly identical samples. We also summarize recent initiatives toward harmonization of quality-control procedures as well as tools to prospectively and retrospectively correct for motion artifacts.
A Sub-IV, 72 μW Stacked LNA-VCO for Wireless Sensor Network Applications

Wireless sensor network (WSN) and Internet-of-Things (IoT) applications demand RF transceivers with extremely low power dissipation. An ultra-low power combined LNA-VCO is presented in this work with RF performance beyond the requirements of the intended application. The LNA current is lowered by factor of 12 in comparison with a standard common-gate LNA and it achieves impedance matching to 50 Ω thanks to current reuse combined with passive gain boosting. Stacking the VCO and the LNA voltage and power efficiency are improved even further. The power consumption of combined LNA-VCO is only 72 μW under a supply voltage of 0.9V. Simulation results in 40 nm CMOS technology at 2.4 GHz show and has a phase noise of -105.8 dBc/Hz at 1 MHz offset frequency, corresponding to a FoM of 194.1 dBc/Hz.
Fabrication of cellulose nanoparticles through electrospraying.

This study reports the fabrication of cellulose nanoparticles through electrospraying the solution of cellulose in N,N-dimethylacetamide/lithium chloride solvent as well as investigating the effect of electrospraying conditions and molecular weight on the average size of electrosprayed nanoparticles. Electrospraying of cellulose was carried out with the following range for each factor, namely concentration = 1-3 wt%, voltage = 15-23 kV, nozzle-collector distance = 10-25 cm, and feed rate = 0.03-0.0875 ml/h. The smallest nanoparticles had an average size of around 40 nm. Results showed that lowering the solution concentration and feed rate, as well as increasing the nozzle-collector distance and applied voltage led to a decrease in the average size of the electrosprayed cellulose nanoparticles. Fourier transform infrared analysis proved that no chemical change had occurred in the cellulose structure after the electrospraying process. According to X-ray diffraction (XRD) results, cellulose nanoparticles showed a lower degree of crystallinity in comparison with the raw cellulose powder. XRD results also proved the absence of LiCl salt in the electrosprayed nanoparticles.
UHF RFID Sensor Tag Antenna Concept for Stable and Distance Independent Remote Monitoring

This paper presents a passive radio frequency identification (RFID) sensor transponder (tag) antenna concept that is based on the detection of defined tag signal patterns at the RFID reader. By detecting tag signal patterns rather than tag signal amplitudes or phases, as shown in previous work, the sensing can be made independent of the reader/tag distance. In addition, the tag with the custom-built antenna ensures a stable reader to tag and tag to reader communication. Initial verification measurements show that similar signal patterns can be detected for different tag positions, even in harsh multipath environments.
Fuzzy SVM-Based Coding Unit Decision in HEVC

The latest video compression standard, High Efficiency Video Coding (HEVC), has greatly improved the coding efficiency compared to the predecessor H.264/AVC. However, equipped with the quadtree structure of coding tree unit partition and other sophisticated coding tools, HEVC brings a significant increase in the computational complexity. To address this issue, a coding unit (CU) decision method based on fuzzy support vector machine (SVM) is proposed for rate-distortion-complexity (RDC) optimization, where the process of CU decision is formulated as a cascaded multi-level classification task. The optimal feature set is selected according to a defined misclassification cost and a risk area is introduced for an uncertain classification output. To further improve the RDC performance, different regulation parameters in SVM are adopted and outliers in training samples are eliminated. Additionally, the proposed CU decision method is incorporated into a joint RDC optimization framework, where the width of risk area is adaptively adjusted to allocate flexible computational complexity to different CUs, aiming at minimizing computational complexity under a configurable constraint in terms of RD performance degradation. Experimental results show that the proposed approach can reduce 58.9% and 55.3% computational complexity on average with the values of Bjønteggard delta peak-signal-to-noise ratio as −0.075 dB and −0.085 dB and the values of Bjøntegaard delta bit rate as 2.859% and 2.671% under low delay ${P}$ and random access configurations, respectively, which has outperformed the state-of-the-art fast algorithms based on statistical information and machine learning.
Building Safer AGI by introducing Artificial Stupidity

Artificial Intelligence (AI) achieved super-human performance in a broad variety of domains. We say that an AI is made Artificially Stupid on a task when some limitations are deliberately introduced to match a human's ability to do the task. An Artificial General Intelligence (AGI) can be made safer by limiting its computing power and memory, or by introducing Artificial Stupidity on certain tasks. We survey human intellectual limits and give recommendations for which limits to implement in order to build a safe AGI.
FPAP: A Folded Architecture for Efficient Computing of Convolutional Neural Networks

Convolutional neural networks (CNNs) have found extensive applications in practice. However, weight/activation's sparsity and different data precision requirements across layers lead to a large amount of redundant computations. In this paper, we propose an efficient architecture for CNNs, named Folded Precision-Adjustable Processor (FPAP), to skip those unnecessary computations with ease. Computations are folded in the following two aspects to achieve efficient computing. On one hand, the dominant multiply-and-add (MAC) operations are performed bit-serially based on a bit-pair encoding algorithm so that the FPAP can adapt to different numerical precisions without using multipliers with long data width. On the other hand, a 1-D convolution is undertaken by a multi-tap transposed finite impulse response (FIR) filter, which is folded into one tap so that computations involving zero activations and weights can be easily skipped. Equipped with the precision-adjustable MAC unit and the folded FIR filter structure, a well-designed array architecture, consisting of many identical processing elements is developed, which is scalable for different throughput requirements and highly flexible for different numerical precisions. Besides, a novel genetic algorithm based kernel reallocation scheme is introduced to mitigate the load imbalance issue. Our synthesis results demonstrate that the proposed FPAP can significantly reduce the logic complexity and the critical path over the corresponding unfolded design, which only delivers slightly higher throughput when processing sparse and compact models. Our experiments also show that FPAP can scale its energy efficiency from 1.01TOP/s/W to 6.26TOP/s/W under 90nm CMOS technology when different data precisions are used.
Bufadienolides from Venenum Bufonis Inhibit mTOR-Mediated Cyclin D1 and Retinoblastoma Protein Leading to Arrest of Cell Cycle in Cancer Cells

Objective Bufadienolides, the main components in Venenum Bufonis secreted from toads, have been proved to be with significant anticancer activity aside from the positive inotropic action as cardenolides. Here an underlying anticancer mechanism was further elucidated for an injection made from Venenum Bufonis containing nine bufadienolides. Methods One solution reagent and cell cycle analyses were for determining effect of bufadienolides on cancer cells. Western blotting was used for protein expression. Results Bufadienolides inhibit cell proliferation and arrest cells in G1 phase. Bufadienolides also inhibit the mammalian target of rapamycin (mTOR) signaling pathway, which is evidenced by the data that bufadienolides inhibit type I insulin-like growth factor- (IGF-1-) activated phosphorylation of mTOR by a concentration- and time-dependent way, as well as phosphorylation of p70 S6 kinase 1 (S6K1) and eukaryotic initiation factor 4E (eIF4E) binding protein 1 (4E-BP1). Subsequent results indicated that cyclin D1 expression and phosphorylation of retinoblastoma protein (Rb)—two characterized regulators in cell cycle of G1—are also inhibited and the process is dependent on mTOR pathway. Conclusion Bufadienolides inhibit proliferation partially due to arresting cell cycle in G1 phase, which is mediated by inhibiting mTOR-cyclin D1/Rb signal pathway.
Deep Learning Based Speech Separation via NMF-Style Reconstructions

Deep learning based speech separation usually uses a supervised algorithm to learn a mapping function from noisy features to separation targets. These separation targets, either ideal masks or magnitude spectrograms, have prominent spectro-temporal structures. Nonnegative matrix factorization (NMF) is a well-known representation learning technique that is capable of capturing the basic spectral structures. Therefore, the combination of deep learning and NMF as an organic whole is a smart strategy. However, previous methods typically use deep neural networks (DNN) and NMF for speech separation in a separate manner. In this paper, we propose a jointly combinatorial scheme to concentrate the strengths of both DNN and NMF for speech separation. NMF is used to learn the basis spectra that then are integrated into a DNN to directly reconstruct the magnitude spectrograms of speech and noise. Instead of predicting activation coefficients inferred by NMF, which is used as an intermediate target by the previous methods, DNN directly optimizes an actual separation objective in our system, so that the accumulated errors could be alleviated. Moreover, we explore a discriminative training objective with sparsity constraints to suppress noise and preserve more speech components further. Systematic experiments show that the proposed models are competitive with the previous methods.
A Multiline Multimaterial Calibration Method for Liquid Characterization

A multiline and multimaterial calibration method is proposed, which is aimed at replacing state-of-the-art two-tier microwave calibration techniques in liquid characterization process. The technique combines a general “line–line” trace method and an equivalent impedance algorithm, which enables to use unknown materials loaded on various lengths of transmission lines in the calibration standards. The validation measurements are performed on coplanar waveguide structures due to the convenient fabrication and easy integration with polymers. Measurement comparison with the previous calibration approach verifies its capability in high-frequency applications.
Sequence Labeling: A Practical Approach

We take a practical approach to solving sequence labeling problem assuming unavailability of domain expertise and scarcity of informational and computational resources. To this end, we utilize a universal end-to-end Bi-LSTM-based neural sequence labeling model applicable to a wide range of NLP tasks and languages. The model combines morphological, semantic, and structural cues extracted from data to arrive at informed predictions. The model's performance is evaluated on eight benchmark datasets (covering three tasks: POS-tagging, NER, and Chunking, and four languages: English, German, Dutch, and Spanish). We observe state-of-the-art results on four of them: CoNLL-2012 (English NER), CoNLL-2002 (Dutch NER), GermEval 2014 (German NER), Tiger Corpus (German POS-tagging), and competitive performance on the rest.
Morphological and Biophysical Determinants of the Intracellular and Extracellular Waveforms in Nigral Dopaminergic Neurons: A Computational Study

Action potentials (APs) in nigral dopaminergic neurons often exhibit two separate components: the first reflecting spike initiation in the dendritically located axon initial segment (AIS) and the second the subsequent dendro-somatic spike. These components are separated by a notch in the ascending phase of the somatic extracellular waveform and in the temporal derivative of the somatic intracellular waveform. Still, considerable variability exists in the presence and magnitude of the notch across neurons. To systematically address the contribution of AIS, dendritic and somatic compartments to shaping the two-component APs, we modeled APs of previously in vivo electrophysiologically characterized and 3D-reconstructed male mouse and rat dopaminergic neurons. A parsimonious two-domain model, with high (AIS) and lower (dendro-somatic) Na+ conductance, reproduced the notch in the temporal derivatives, but not in the extracellular APs, regardless of morphology. The notch was only revealed when somatic active currents were reduced, constraining the model to three domains. Thus, an initial AIS spike is followed by an actively generated spike by the axon-bearing dendrite (ABD), in turn followed mostly passively by the soma. The transition from being a source compartment for the AIS spike to a source compartment for the ABD spike satisfactorily explains the extracellular somatic notch. Larger AISs and thinner ABD (but not soma-to-AIS distance) accentuate the AIS component. We conclude that variability in AIS size and ABD caliber explains variability in AP extracellular waveform and separation of AIS and dendro-somatic components, given the presence of at least three functional domains with distinct excitability characteristics. SIGNIFICANCE STATEMENT Midbrain dopamine neurons make an important contribution to circuits mediating motivation and movement. Understanding the basic rules that govern the electrical activity of single dopaminergic neurons is therefore essential to reveal how they ultimately contribute to movement and motivation as well as what goes wrong in associated disorders. Our computational study focuses on the generation and propagation of action potentials and shows that different morphologies and excitability characteristics of the cell body, dendrites and proximal axon can explain the diversity of action potentials shapes in this population. These compartments likely make differential contributions both to normal dopaminergic signaling and could potentially underlie pathological dopaminergic signaling implicated in addiction, schizophrenia, Parkinson's disease, and other disorders.
Enhanced Multi-Level Signal Recovery in Mobile Fronthaul Network Using DNN Decoder

A deep neural network (DNN) decoder which combines the function of both equalization and decoding is proposed and experimentally demonstrated for mobile fronthaul (MFH) transmission. This DNN consists of one input layer, one output layer, and several hidden layers. Adamax algorithm is implemented for finding the global minima while dropout mechanism and early stopping are utilized to avoid overfitting. The DNN accepts the received samples as the input and output the decoded samples directly to recover the transmitted samples. Using this DNN decoder, a record-high data-rate transmission-distance product at 1800-Gb/s $\cdot $ km based on the directly modulated laser (DML) with intensity-modulation direct-detection is obtained. Besides, the PAM8 modulation with 3-bps/Hz spectral efficiency is implemented. Due to the smaller source spectrum bandwidth compared with traditionally widely used PAM4 and OOK at a certain data rate, the power fading limited transmission distance is extended by 1.5 and three times, respectively. The DML is a low-cost device and this simple transmission system eliminates the need of single-side-band modulation or dispersion compensation, which makes this system an ideal candidate for a low-cost enhanced MFH network.
Cough peak flow decline rate predicts survival in patients with amyotrophic lateral sclerosis

Introduction: In this study we investigated the prognostic significance of cough peak flow (CPF) decline rate in patients with amyotrophic lateral sclerosis (ALS). Methods: Thirty‐four patients with ALS participated in our investigation. We examined CPF, forced vital capacity (FVC), body mass index, and the revised ALS Functional Rating Scale (ALSFRS‐R), and followed patients to death, tracheostomy, or non‐invasive ventilator dependency. We analyzed the relationship between the rate of decline of each parameter and survival prognosis. Results: The CPF decline rate significantly correlated with the decline rates of the ALSFRS‐R bulbar score (P < 0.0001) and FVC (P < 0.0001). Patients with a CPF decline rate ≥25% had shorter overall survival (P < 0.0001). Cox model multivariate analyses showed that the CPF decline rate was an independent prognostic factor for survival (P = 0.021). Discussion: The CPF decline rate reflects the progression of bulbar and respiratory dysfunction and predicts survival. Muscle Nerve 59:168–173, 2019
Application of the Polyhedral Conic Functions Method in the Text Classification and Comparative Analysis

In direct proportion to the heavy increase of online information data, the attention to text categorization (classification) has also increased. In text categorization problem, namely, text classification, the goal is to classify the documents into predefined classes (categories or labels). Recently various methods in data mining have been experienced for text classification in literature except polyhedral conic function (PCF) methods. In this paper, PCFs are used to classify the documents. The separation algorithms via PCFs which include linear programming subproblems with inequality constraints are presented. Numerical experiments are done on real-world text datasets. Comparisons are made between state-of-the-art methods by presenting obtained tenfold cross-validation results, accuracy values, and running times in tables. The results verify that in text classification PCF methods are as effective in terms of accuracy values as state-of-the-art methods.
A Fully Fail-Safe Capacitive-Based Charge Metering Method for Active Charge Balancing in Deep Brain Stimulation

Related to safety issues, charge balancing is a major concern in neural and functional electrical stimulation. This paper presents a capacitive-based charge metering method as a low-power and precise charge balancing method used in Deep Brain Stimulation (DBS). In contrast to the previously presented capacitive-based charge metering methods, the proposed method does not need any precise and high-speed comparator for net-zero charge detection. It is proven that this method is insensitive to the delay and the offset of its components. Consequently, using ultra-low power components in the charge balancer is feasible. Furthermore, the proposed method properly supports any stimulation mode and waveform. The proposed approach along with voltage and current mode pulse generators was validated in a 0.9% saline solution by using a DBS lead.
Silicon Photonic Four-Channel Optical Add-Drop Multiplexer Enabled by Subwavelength Grating Waveguides

Wavelength-division multiplexing over optical links provides an effective solution for the bandwidth challenge of off-chip and on-chip communications. We demonstrate a compact silicon photonic four-channel optical add-drop multiplexer enabled by subwavelength-grating-based contradirectional couplers. Passbands of the device show on-chip insertion losses below 1.8 dB with wide 3 dB bandwidth of ∼6.7 nm suitable for coarse wavelength-division Multiplexing in short-reach optical interconnect applications. Transmission of 10 Gb/s data stream through different channels of the multiplexer results in negligible power penalties, whereas interferometric crosstalk-induced power penalties are below 2.8 dB.
Xuefu Zhuyu decoction improves neurological dysfunction by increasing synapsin expression after traumatic brain injury

Xuefu Zhuyu decoction has been used for treating traumatic brain injury and improving post-traumatic dysfunction, but its mechanism of action needs further investigation. This study established rat models of traumatic brain injury by controlled cortical impact. Rat models were intragastrically administered 9 and 18 g/kg Xuefu Zhuyu decoction once a day for 14 or 21 days. Changes in neurological function were assessed by modified neurological severity scores and the Morris water maze. Immunohistochemistry, western blot assay, and reverse-transcription polymerase chain reaction were used to analyze synapsin protein and mRNA expression at the injury site of rats. Our results showed that Xuefu Zhuyu decoction visibly improved neurological function of rats with traumatic brain injury. These changes were accompanied by increased expression of synaptophysin, synapsin I, and postsynaptic density protein-95 protein and mRNA in a dose-dependent manner. These findings indicate that Xuefu Zhuyu decoction increases synapsin expression and improves neurological deficits after traumatic brain injury.
A Multitemporal Approach for Cloud Removal from Satellite Images

Clouds are an obstruction for land surface observation, which results in regional information being blurred or lost. An efficient and effective cloud removal approach is proposed in this paper. The approach removes cloud contaminated portion of a satellite image and then reconstructs the information of missing data by taking advantage of the temporal correlation of multi-temporal images. Patch based information reconstruction is mathematically formulated as a Modified Poisson equation. The proposed approach was tested on images acquired by Landsat-7 Enhanced Thematic Plus sensor and the experiments verify that the proposed approach can achieve better results in terms of radiometric accuracy and consistency compared to other related approaches. In addition, quantitative and qualitative analysis on simulated data with single reference image and multiple reference images are conducted using quality index and visual inspection, respectively, to demonstrate the advantage of utilizing multi-temporal reference images. The significance of size of the patch used is also tested Keywords—cloud removal; multitemporal images; patch based; Modified Poisson equation.