Miracle-dz/UniFAR-FTUs huggingface dataset:
Reference Point Specification in Inverted Generational Distance for Triangular Linear Pareto Front

The hypervolume and the inverted generational distance (IGD) have been frequently used for the comparison of evolutionary multiobjective optimization algorithms. For the hypervolume, the relation between the location of a reference point and the optimal distribution of solutions has been studied in the literature. However, such a relation has not been studied for the IGD whereas IGD-based comparison results depend on the specification of reference points. Our intention is to clearly demonstrate the dependency of IGD-based comparison results on reference point specification. First, we explain difficulties of fair comparison in the following two cases: one is the use of all nondominated solutions among obtained solutions by compared algorithms as reference points, and the other is the use of a small number of uniformly sampled reference points. Discussions on these two cases show the necessity of a large number of uniformly sampled reference points on the entire Pareto front. Then, we show a bias of the IGD with such a reference point set through computational experiments. It is shown that the IGD tends to favor a solution set with much smaller diversity than a fully expanded solution set over the entire Pareto front. Finally, we propose a new specification method where reference points are uniformly sampled not only from the Pareto front but also from outside the Pareto front.
Organizing Visions in Online Social Networks: The Role of Community Heterogeneity and Real-time Engagement

New information technologies are changing the outcomes and process of innovation due to their increasing availability and use by individuals and organizations. There is a need to better understand how information systems (IS) innovations come about in terms of the socio-cognitive sensemaking through computer-mediated discourse. Yet, there is limited knowledge on how socio-cognitive sensemaking develops and influences IS innovations with organizing vision theory being a notable exception. Online social networks (OSN) is one of the primary new tools that is accelerating innovation due to its capacity for discursive social engagement. It is important for joint sensemaking which can influence diffusion and adoption of IS innovations. To date, past organizing vision research has mainly focused on traditional discursive channels. Therefore, the knowledge about how organizing visions develop within more contemporary channels such as OSNs is very limited so far. In this paper, we emphasize the importance of OSNs as a contemporary discourse channel and discuss how heterogeneity of communities, real-time engagement, and platform characteristics and practices impact organizing visions. We contribute to IS literature by offering a preliminary conceptual framework for organizing visions in OSNs and then suggesting possible opportunities to integrate social networks and big data analytics with organizing vision theory.
SentiNet: Detecting Physical Attacks Against Deep Learning Systems

—SentiNet is a novel detection framework for physical attacks on neural networks, a class of attacks that constrains an adversarial region to a visible portion of an image. Physical attacks have been shown to be robust and ﬂexible techniques suited for deployment in real-world scenarios. Unlike most other adversarial detection works, SentiNet does not require training a model or preknowledge of an attack prior to detection. This attack-agnostic approach is appealing due to the large number of possible mechanisms and vectors of attack an attack-speciﬁc defense would have to consider. By leveraging the neural network’s susceptibility to attacks and by using techniques from model interpretability and object detection as detection mechanisms, SentiNet turns a weakness of a model into a strength. We demonstrate the effectiveness of SentiNet on three different attacks— i.e., adversarial examples, data poisoning attacks, and trojaned networks—that have large variations in deployment mechanisms, and show that our defense is able to achieve very competitive performance metrics for all three threats, even against strong adaptive adversaries with full knowledge of SentiNet.
Fast movements and jerk mechanics

The purpose of this paper is to study some extended fast motions, determined by the third order Euclidean Lagrangian. We consider some dynamic situations determined by initial non-null conditions that ends in null speed and acceleration. The subject is topical because we model a less studied type of movements that can lead to interesting new developments.
Laplace One-Step Controller for Linear Scalar Systems

Uncertainties in many physical systems have impulsive properties poorly modeled by Gaussian distributions. Building on work to develop Cauchy controllers, a Laplace controller is explored as a heavier-tailed alternative to the Gaussian. Whereas the Cauchy density has no moments, the Laplace density has finite moments of all orders as the Gaussian density.For a scalar discrete linear system with additive Laplace process and measurement noises, the one-step optimal control problem is considered, where the conditional expectation of the cost criterion is determined as a function of the measurements and the control in closed form. The optimal control is determined numerically for different values of noise parameters and cost criterion weightings, and its properties are examined.
Disruption in Brain Phospholipid Content in a Humanized Tau Transgenic Model Following Repetitive Mild Traumatic Brain Injury

Repetitive mild traumatic brain injury (mTBI) is a risk factor for the development of neurodegenerative diseases such as chronic traumatic encephalopathy typified by immunoreactive tau aggregates in the depths of the sulci. However, the underlying neurobiological mechanisms involved have not been largely explored. Phospholipids are important molecules which form membrane lipid bilayers; they are ubiquitous to every cell in the brain, and carry out a host of different functions. Imbalance in phospholipid metabolism, signaling and transport has been documented in some neurological conditions. However, not much is currently known about their roles in repetitive mTBI and how this may confer risk for the development of age-related neurodegenerative diseases. To address this question, we designed a longitudinal study (24 h, 3, 6, 9, and 12 months post-injury) to comprehensively investigate mTBI dependent brain phospholipid profiles compared to sham counterparts. We use our established mouse model of repetitive mTBI that has been extensively characterized up to 1-year post-injury in humanized tau (hTau) mice, which expresses all six human tau isoforms, on a null murine background. Our data indicates a significant increase in sphingomyelin, phosphatidylethanolamine (PE), phosphatidylcholine (PC), and derivative lysoPE and lysoPC at acute and/or sub-acute time points post-injury within the cortex and hippocampus. There was also a parallel increase at early time points in monounsaturated, polyunsaturated and saturated fatty acids. Omega-6 (arachidonic acid) to omega-3 (docosahexaenoic acid) fatty acid ratio for PE and PC species was increased also at 24 h and 3 months post-injury in both hippocampus and cortex. The long-term consequences of these early changes in phospholipids on neuronal and non-neuronal cell function is unclear, and warrants further study. Understanding phospholipid metabolism, signaling and transport following TBI could be valuable; they may offer novel targets for therapeutic intervention not only in TBI but other neurodegenerative diseases.
Acute Kidney Injury after Cardiac Surgery in Patients Without Chronic Kidney Disease

Introduction Among patients undergoing cardiac surgery, the occurrence of acute renal injury appears to be associated with worse prognosis and increased mortality. The objective of this study was to evaluate risk factors and the impact this complication on mortality and survival after cardiac surgery among patients without chronic kidney disease. Methods In this retrospective study, we reviewed the medical records of 142 patients who underwent elective coronary artery bypass grafting, valve replacement (single or multiple), or both (simultaneously) at a tertiary care hospital. Results Among the 142 patients evaluated, the mean age was 58.28±13.87 years and 80 (56.33%) were female. The postoperative incidence of acute renal injury was 43.66%. Univariate analysis between the groups with and without acute renal injury revealed no significant differences, whereas multivariate analysis showed that risk factors for acute renal injury included valve replacement (OR=4.7, P=0.002, 95% CI=1.76-12.62, age (OR=1.044, P=0.012, 95% CI=1.01-1.07), previous cardiac surgery (OR=36.1, P=0.015, 95% CI=1.99-653.85), postoperative use of the vasoactive drug norepinephrine (OR=3.32, P=0.013, 95% CI=1.29-8.58) and dobutamine (OR=5.3, P=0.019, 95% CI=1.32-21.64). In our sample, there were 30 deaths, of which 25 had acute kidney injury. Survival was also lower among the patients with this complication, especially those who had required hemodialysis (OR=2.60, P<0.001, 95% CI=1.01-6.70) or had previously undergone cardiac surgery (OR=3.68, P<0.001, 95% CI=1.09-12.37). Conclusion Our findings underscore the importance of identifying risk factors for developing acute renal injury after cardiac surgery, which can further the development of effective renoprotective strategies.
Analysis of the Patient Information Quality and Readability on Esophagogastroduodenoscopy (EGD) on the Internet

Objective Patients are increasingly using the Internet to inform themselves of health-related topics and procedures, including EGD. We analyzed the quality of information and readability of websites after a search on 3 different search engines. Methods We used an assessment tool for website quality analysis that we developed in addition to using validated instruments for website quality, Global Quality Score (GQS) and Health on Net (HON) certification. The readability was assessed using Flesch-Kincaid Reading Ease (FRE) and Flesch-Kincaid Grade level (FKG). 30 results of each search terms ‘EGD' and ‘Upper Endoscopy' from Google and 15 each from Bing and Yahoo were analyzed. A total of 45 websites were included from 100 URLs after removing duplicates, video links, and journal articles. Results Only 3 websites were found to have good quality and comprehensive and authentic information. These websites were https://www.healthline. com, https://www.uptodate. com, and https://www.emedicine. medscape.com. There were additional 13 sites with moderate quality of information. The mean Flesch-Kincaid Reading Ease (FRE) score was 46.92 (range 81.6-6.5). The mean Flesch-Kincaid Grade level (FKG) was 11th grade, with a range of 6th grade to 12th grade and above making them difficult to read. Conclusions Our study shows that there are quite a few websites with moderate quality content. We recommend 3 comprehensive and authentic websites out of 45 URLs analyzed for information on Internet for EGD. In addition, the readability of the websites was consistently at a higher level than recommended by AMA at 11th grade level. In addition, we identified 3 websites with moderate quality content written at 8th grade and below readability level. We feel that gastroenterologists can help their patients better understand this procedure by directing them to these comprehensive websites.
Stable Persistent Homology Features of Dynamic Metric Spaces

Characterizing the dynamics of time-evolving data within the framework of topological data analysis (TDA) has been attracting increasingly more attention. Popular instances of time-evolving data include flocking/swarming behaviors in animals and social networks in the human sphere. A natural mathematical model for such collective behaviors is a dynamic point cloud, or more generally a dynamic metric space (DMS). In this paper we extend the Rips filtration stability result for (static) metric spaces to the setting of DMSs. We do this by devising a certain three-parameter "spatiotemporal" filtration of a DMS. Applying the homology functor to this filtration gives rise to multidimensional persistence module derived from the DMS. We show that this multidimensional module enjoys stability under a suitable generalization of the Gromov-Hausdorff distance which permits metrizing the collection of all DMSs. On the other hand, it is recognized that, in general, comparing two multidimensional persistence modules leads to intractable computational problems. For the purpose of practical comparison of DMSs, we focus on both the rank invariant or the dimension function of the multidimensional persistence module that is derived from a DMS. We specifically propose to utilize a certain metric d for comparing these invariants: In our work this d is either (1) a certain generalization of the erosion distance by Patel, or (2) a specialized version of the well known interleaving distance. We also study the computational complexity associated to both choices of d.
Metformin sensitizes hypoxia-induced gefitinib treatment resistance of HNSCC via cell cycle regulation and EMT reversal

Objectives The objectives of this study were to explore the mechanisms of metformin sensitization to hypoxia-induced gefitinib treatment in resistant head and neck squamous cell carcinoma (HNSCC) and evaluate the effects of this combined treatment strategy. Methods The effects of gefitinib treatment on HNSCC were measured under normoxic and hypoxic conditions. The relationship between hypoxia and cell cycle and epithelial–mesenchymal transition (EMT) in tumor cells were analyzed. Palbociclib and LY294002 were used in combination with gefitinib to evaluate the effects on HNSCC cell cytotoxicity during hypoxia. Finally, metformin was used to evaluate the sensitizing effects of gefitinib treatment on HNSCC in vivo and in vitro. Results Cell viability and apoptosis assays demonstrated a significant difference in HNSCC cells treated with gefitinib between the normoxia and hypoxia groups. Hypoxia induced the expression of cyclin D1, decreased the percentage of cells in G1, and promoted the EMT of tumor cells. Both palbociclib and LY294002 enhanced gefitinib-induced cytotoxicity of HNSCC cells under hypoxic conditions. Encouragingly, metformin sensitized HNSCC to gefitinib treatment in vivo and in vitro. Conclusion Hypoxia promotes G1–S cell cycle progression and EMT in HNSCC, resulting in gefitinib treatment resistance. Metformin sensitizes HNSCC to gefitinib treatment, which might serve as a novel combined treatment strategy.
Enabling NFV Elasticity Control With Optimized Flow Migration

Network function virtualization (NFV) together with software defined networking (SDN) offers the potential for enhancing service delivery flexibility and reducing overall costs. Based on the capability of dynamic creation and destruction of network function (NF) instances, NFV provides great elasticity in NF control, such as NF scaling out, scaling in, and load balancing. To realize NFV elasticity control, network traffic flows need to be redistributed across NF instances. However, deciding which flows are suitable for migration is a critical problem for efficient NFV elasticity control. In this paper, we propose to build an innovative flow migration controller, OFM controller, to achieve optimized flow migration for NFV elasticity control. We identify the trigger conditions and control goals for different situations, and carefully design models and algorithms to address three major challenges including buffer overflow avoidance, migration cost calculation, and effective flow selection for migration. We implement the OFM controller on top of NFV and SDN environments. Our evaluation results show that OFM controller is efficient to support optimized flow migration in NFV elasticity control.
LQ Optimal Tracking with Unbounded Cost for Unknown Dynamics: An Adaptive Dynamic Programming Approach

In case of unknown system dynamics, we consider linear quadratic optimal tracking on infinite horizons with generally unbounded cost. For the first time, we deal with this problem in the framework of adaptive dynamic programming. So far, existing methods require bounded costs which essentially limits the applicability and achievable performance. Thus, we develop a new algorithm that yields a strongly overtaking optimal control which is an adequate solution. After collecting measurement data in an exploration phase, the algorithm implicitly solves the necessary and sufficient algebraic equations in [3], but without knowledge of the dynamics. Then, implementing the control results in an optimal transition to an optimal stationary trajectory. A simulation example of almost exact tracking for an over-actuated system demonstrates a highly efficient saving of input-energy in contrast to state-of-the-art approaches.
Singular Value Thresholding based Adaptive Approach for Hybrid Beamforming in MmWave Massive MIMO-OFDM Transmitters

Thanks to the high spectral efficiency and the abundant bandwidths, millimeter-wave (mmWave) communication combined with massive multiple-input multiple-output (MIMO) technology have been considered as a promising candidate for next generation mobile communication systems. As a mmWave massive MIMO system requires a huge number of radio frequency (RF) chains, hybrid analog/digital (A/D) beamforming (BF) is addressed as the best solution to surpass digital BF limitations and reach lower cost and power consumption. Therefore, new specific A/D BF algorithms for mmWave massive MIMO systems are required. This paper considers the problem of BF design in a hybrid mmWave massive MIMO-OFDM system using the sub-array structure which reduces the number of phase shifters since each RF chain should be only connected to a specific sub-array. For this structure, we propose an adaptive hybrid Am BF design based on singular value thresholding algorithm. Then, our proposal is evaluated in terms of spectral efficiency, outperforming the semidefinite relaxation (SDR) based algorithm and providing an important reduction in terms of computational complexity.
Improved Pairwise Embedding for High-Fidelity Reversible Data Hiding

Pairwise reversible data hiding (RDH) restricts the embedding to 3 combinations of bits per pixel pair (“00”, “01”, “10”), by eliminating the embedding of “1” into both pixels. The gain in quality is significant and the loss in embedding bitrate is compensated by embedding into previously shifted pairs. This restriction requires a special coding procedure to format the encrypted hidden data. This paper proposes a new set of embedding equations for pairwise RDH. The proposed approach inserts either one or two data bits into each pair based on its type, bypassing the need for special coding. The proposed equations can be easily integrated in most pairwise reversible data hiding frameworks. They also provide more room for data embedding than their classic counterparts at the low embedding distortion required for high-fidelity RDH.
Inception Donut Convolution for Top-down Semantic Segmentation

One of recent trends in network architecture design confirms that the inception-block convolutional group is efficient, since it can aggregate spatial context information in lower dimensions without causing significant loss in representative capabilities. We believe that not only the strong correlation between adjacent cells, multi-scale feature extraction also plays a vital role in this novel module. In this paper, we extend the profits of the block to a top-down donut convolutional network for semantic segmentation task. Our network automatically learns rich convolution kernels to capture more structure prior. In the inception-block design, it overcomes the limitations in larger kernel size and adaptively captures different object-scales contexts without chain sampling. Our experiments demonstrate that the proposed inception-block donut convolutional network is orthogonal and can further improve the performance of most off-the-shelf bottom-up based methods.
Automatic Audio Chord Recognition With MIDI-Trained Deep Feature and BLSTM-CRF Sequence Decoding Model

With the advances of machine learning technologies, data-driven feature extraction and sequence modeling approaches are being widely explored for automatic chord recognition tasks. Currently, there is a bottleneck in the amount of enough annotated data for training robust acoustic models, as hand-annotating time-synchronized chord labels requires professional musical skills and considerable labor. To cope with this limitation, in this paper, we propose a convolutional neural network (CNN) based deep feature extractor, which is trained on a large set of time, synchronized musical instrument digital interface audio data pairs and can robustly estimate pitch class activations of real-world music audio recordings. The CNN feature extractor plus a bidirectional long short-term memory conditional random field decoding model forms the proposed hybrid system for automatic chord recognition. Experiments show that the proposed model is compatible for both regular major/minor triad chord classification and larger vocabulary chord recognition, and outperforms other state-of-the-art chord recognition systems.
Novelty and Diversity: Remixing with Human-based Search Algorithms

Remixing is used as a method to harness collective intelligence in many online innovation communities. In these remixing communities, users can reuse previous work to produce innovative outcomes. However, some users tend to make remix decisions based on what is immediately visible to them. Because of this they miss opportunities to further explore the design space. Can crowd members be guided to conduct collective exploration in a systematic way to better cover the design space? In this paper an exploratory study compares the collective exploration of ideas generated by four human-based remixing algorithms: deepening, widening, depth-first, and breadth-first. The distances between ideas that are generated by these remixing algorithms are calculated and compared. The results suggest that the deepening and hybrid algorithms can encourage users to better cover the design space. Potential ways to further improve the algorithms are discussed, with the goal of encouraging collective exploration through remixing.
Enhancing Digital Zoom in Mobile Phone Cameras by Low Complexity Super-Resolution

In this paper, we present an enhancement method for improvement of digital zoom feature in mobile phone cameras. The enhancement approach is based on the state-of-the-art deep learning-based super-resolution. We introduce an input super-pixel pooling layer in the enhancement framework, that down-scales the input data without losing information, and results in a 96% complexity reduction of the enhancement framework, when compared with the baseline approach. The presented model is also capable of being trained simultaneously for several scaling factors and different levels of enhancement, without affecting the complexity of the approach.
Getting rid of anti-solvents: gas quenching for high performance perovskite solar cells

As the field of perovskite optoelectronics developed, a plethora of strategies has arisen to control their electronic and morphological characteristics for the purpose of producing high efficiency devices. Unfortunately, despite this wealth of deposition approaches, the community experiences a great deal of irreproducibility between different laboratories, batches and preparation methods. Aiming to address this issue, we developed a simple deposition method based on gas quenching that yields smooth films for a wide range of perovskite compositions, in single, double, triple and quadruple cation varieties, and produces planar heterojunction devices with competitive efficiencies, so far up to 20%.