Miracle-dz/UniFAR-FTUs huggingface dataset:
Pterostilbene inhibits nutrient metabolism and induces apoptosis through AMPK activation in multiple myeloma cells

Multiple myeloma (MM) cells are characterized by an abnormal nutrient metabolism that is distinct from normal plasma cells. Pterostilbene (PTE), a bioactive component of blueberries, has been demonstrated to induce apoptosis in multiple types of cancer cell. The present study evaluated whether PTE treatment affected the survival of MM cells from a metabolic perspective, and the potential mechanisms of this. It was observed that the administration of PTE induced apoptosis, which was mediated by the increased activation of AMP-activated protein kinase (AMPK). Once activated, AMPK decreased the expression and/or activity of key lipo-genic enzymes, including fatty acid synthase and acetyl-CoA carboxylase. In addition, the activation of AMPK suppressed the downstream substrate, mechanistic target of rapamycin, which dephosphorylated eukaryotic initiation factor 4E-binding protein 1, leading to a general decrease in mRNA translation. Pre-treatment with the AMPK inhibitor compound C prior to PTE treatment compromised the anti-myeloma apoptosis effect, suggesting the critical role of AMPK in mediating PTE-induced cell toxicity. Consistent results were obtained in vivo. Finally, autophagy was adaptively upregulated subsequent to PTE treatment; the pro-apoptotic efficacy of PTE was potentiated once autophagic flux was inhibited by 3-methyladenine. Taken together, these data demonstrated that PTE exerts anti-tumor effects on MM cells via AMPK-induced nutrient suppression.
Detection and Resolution of Motion Conflict in Visual Inertial Odometry

In this paper, we present a novel method to detect and resolve motion conflicts in visual-inertial odometry. Recently, it has been common to integrate an IMU sensor with visual odometry in order to improve localization accuracy and robustness. However, when a disagreement between the two sensor modalities occurs, the localization accuracy reduces drastically and leads to irreversible errors. In such conditions, multiple motion estimates based on the set of observations used are possible. This creates a conflict (motion conflict) in determining which observations to use for accurate ego-motion estimation. Therefore, we present a method to detect motion conflicts based on per-frame positional estimate discrepancy and per-landmark reprojection errors. Additionally, we also present a method to resolve motion conflicts by eliminating inconsistent IMU and landmark measurements. Finally, we implement Motion Conflict aware Visual Inertial Odometry (MC-VIO) by combining both detection and resolution of motion conflicts. We perform quantitative and qualitative evaluation of MC-VIO on visually and inertially challenging datasets. Experimental results indicate that the MC-VIO algorithm reduces the increase in absolute trajectory error by 80% and the relative pose error by 60% for scenes with motion conflict, in comparison to the state-of-the-art reference VIO algorithm.
Diabetic Retinopathy Detection Based on Deep Convolutional Neural Networks

Diabetic retinopathy is the primary cause of blindness in the working-age population of the developed world. Diagnosing the disease heavily relies on imaging studies, which is a time consuming and a manual process performed by trained clinicians. Enhancing the accuracy and speed of the detection process can potentially have a significant impact on population health via early diagnosis and intervention. Motivated by this, we propose a recognition pipeline based on deep convolutional neural networks. In our pipeline, we design lightweight networks called SI2DRNet-vl along with six methods to further boost the detection performance. Without any fine-tuning, our recognition pipeline outperforms state of the art on the Messidor dataset along with 5.26x fewer in total parameters and 2.48x fewer in total floating operations.
The Dynamic Bearing Observability Matrix Nonlinear Observability and Estimation for Multi-Agent Systems

We consider the problem of localization in multiagent formations with bearing only measurements, and analyze the fundamental observability properties for dynamic agents. The current well-established approach is based on the socalled rigidity matrix, and its algebraic properties (e.g., its rank and nullspace). This method is typically motivated using first-order derivatives, and shows, among other facts, that the global scale of the formation is not observable. This work shows that current results represent an incomplete view of the problem. In particular, we show that 1) current methods are a particular instantiation of nonlinear observability theory, 2) we can introduce the concept of the dynamic bearing observability matrix from higher order derivatives to study the observability of dynamic formations, and 3) the global scale is, in fact, generally observable when the agents move according to known inputs. We use tools from Riemannian geometry and Lie group theory to tackle, in a general and principled way, the general formulation of the localization problem with states that include both rotations and translations. Finally, we verify our theoretical results by deriving and applying, in both simulations and real experiments on UAVs, a centralized Extended Kalman Filter on Lie groups that is able to estimate the global scale of a moving formation.
Rescoring N-Best Speech Recognition List Based on One-on-One Hypothesis Comparison Using Encoder-Classifier Model

This paper proposes a new model for accurately rescoring (reranking) $N$-best speech recognition hypothesis lists. The model is based on state-of-the-art neural networks (NNs) and provides the minimum necessary functionality to perform $N$-best rescoring, i.e. one-on-one hypothesis comparison on a given $N$-best list in terms of word error rates (WERs). The model is composed of a long short-term memory (LSTM)-based encoder network followed by a fully-connected feedforward NN-based binary-class classifier network. Given the feature vector sequences of two hypotheses to be compared, this encoder-classifier (EC) model encodes these features and outputs binary-class probabilities that indicate which hypothesis has the lower WER. Then, depending on the output, the ranks of these hypotheses can be swapped. By repeating this one-on-one hypothesis comparison, a reranked $N$-best list can be obtained. In $N$-best rescoring experiments using a large scale speech corpus, the proposed EC model steadily outperforms an LSTM-based language model (LSTMLM), which is a strong and widely-used competitor. In addition, by incorporating the LSTMLM scores as an additional feature vector dimension, the $N$-best rescoring performance of the EC model is further improved. The improved EC model achieves a 10% relative WER reduction from the LSTMLM baseline.
Cerebral Blood Flow and Cognitive Functioning in a Community-Based, Multi-Ethnic Cohort: The SABRE Study

Introduction: Lower cerebral blood flow (CBF) is associated with cardiovascular disease and vascular risk factors, and is increasingly acknowledged as an important contributor to cognitive decline and dementia. In this cross-sectional study, we examined the association between CBF and cognitive functioning in a community-based, multi-ethnic cohort. Methods: From the SABRE (Southall and Brent Revisited) study, we included 214 European, 151 South Asian and 87 African Caribbean participants (71 ± 5 years; 39%F). We used 3T pseudo-continuous arterial spin labeling to estimate whole-brain, hematocrit corrected CBF. We measured global cognition and three cognitive domains (memory, executive functioning/attention and language) with a neuropsychological test battery. Associations were investigated using linear regression analyses, adjusted for demographic variables, vascular risk factors and MRI measures. Results: Across groups, we found an association between higher CBF and better performance on executive functioning/attention (standardized ß [stß] = 0.11, p < 0.05). Stratification for ethnicity showed associations between higher CBF and better performance on memory and executive functioning/attention in the white European group (stß = 0.14; p < 0.05 and stß = 0.18; p < 0.01 respectively), associations were weaker in the South Asian and African Caribbean groups. Conclusions: In a multi-ethnic community-based cohort we showed modest associations between CBF and cognitive functioning. In particular, we found an association between higher CBF and better performance on executive functioning/attention and memory in the white European group. The observations are consistent with the proposed role of cerebral hemodynamics in cognitive decline.
Recurrent Transformer Network for Remote Sensing Scene Categorisation

Remote sensing scene categorisation is a task to distinguish the basic level scene images in accordance with the contents of the subordinate level feature representations. This gives rise to a signiﬁcant semantic gap between subordinate level features and the basic level scene contents. In this work, we propose recurrent transformer networks (RTN) to mitigate the above problem. RTN incorporates learning transformation-invariant regions with transformer based attention mechanism, thus reducing the semantic gap efﬁciently. It can learn the canonical appearance for the most relevant regions based on the subordinate level contents of the remote sensing scene images. The predictions of both transformation parameters and classiﬁcation score are derived from the bilinear CNN pooling regression. The whole network is differentiable and can be learned end-by-end by only acquiring the basic level labels. As shown in Figure 1, our RTN framework composes of three major parts which are recurrent warp operation, bilinear pooling operation
Translational Retinal Research and Therapies

The following review summarizes the state of the art in representative aspects of gene therapy/translational medicine and evolves from a symposium held at the School of Veterinary Medicine, University of Pennsylvania on November 16, 2017 honoring Dr. Gustavo Aguirre, recipient of ARVO's 2017 Proctor Medal. Focusing on the retina, speakers highlighted current work on moving therapies for inherited retinal degenerative diseases from the laboratory bench to the clinic.
VisualBackProp: Efficient Visualization of CNNs for Autonomous Driving

This paper proposes a new method, that we call VisualBackProp, for visualizing which sets of pixels of the input image contribute most to the predictions made by the convolutional neural network (CNN). The method heavily hinges on exploring the intuition that the feature maps contain less and less irrelevant information to the prediction decision when moving deeper into the network. The technique we propose is dedicated for CNN-based systems for steering self-driving cars and is therefore required to run in real-time. This makes the proposed visualization method a valuable debugging tool which can be easily used during both training and inference. We justify our approach with theoretical arguments and confirm that the proposed method identifies sets of input pixels, rather than individual pixels, that collaboratively contribute to the prediction. We utilize the proposed visualization tool in the NVIDIA neural-network-based end-to-end learning system for autonomous driving, known as PilotNet. We demonstrate that VisualBackProp determines which elements in the road image most influence PilotNet's steering decision and indeed captures relevant objects on the road. The empirical evaluation furthermore shows the plausibility of the proposed approach on public road video data as well as in other applications and reveals that it compares favorably to the layer-wise relevance propagation approach, i.e. it obtains similar visualization results and achieves order of magnitude speed-ups.
Unconstrained Control of Feature Map Size Using Non-integer Strided Sampling

Convolutional neural network (CNN) is mainly composed of convolution, pooling, and non-linear activation layers. Nowadays, almost all networks use only 2 × 2 max pooling or convolution layers with stride of 2 for downsampling. This technique is known to be good at extracting good feature, but it also has the constraint that feature map size is always reduced dramatically to half. In this work, we propose a simple new sampling technique that we call non-integer strided sampling (NSS), which enables free feature map size change so that it is not always reduced to half. Using this NSS layer, we design a new type of network architecture, GradualNet , which makes the feature map size change more smoothly than it is in existing networks. Our results show that NSS can improve the performance of networks without having more parameters. Moreover, we propose other interesting possibilities for a CNN architecture based on the NSS layer. This result reveals that previous networks have been stuck in a stereotype, and this could be an important discovery in CNN architecture that has the potential to resolve this stereotype.
Sampled Policy Gradient for Learning to Play the Game Agar.io

In this paper, a new offline actor-critic learning algorithm is introduced: Sampled Policy Gradient (SPG). SPG samples in the action space to calculate an approximated policy gradient by using the critic to evaluate the samples. This sampling allows SPG to search the action-Q-value space more globally than deterministic policy gradient (DPG), enabling it to theoretically avoid more local optima. SPG is compared to Q-learning and the actor-critic algorithms CACLA and DPG in a pellet collection task and a self play environment in the game this http URL. The online game this http URL has become massively popular on the internet due to intuitive game design and the ability to instantly compete against players around the world. From the point of view of artificial intelligence this game is also very intriguing: The game has a continuous input and action space and allows to have diverse agents with complex strategies compete against each other. The experimental results show that Q-Learning and CACLA outperform a pre-programmed greedy bot in the pellet collection task, but all algorithms fail to outperform this bot in a fighting scenario. The SPG algorithm is analyzed to have great extendability through offline exploration and it matches DPG in performance even in its basic form without extensive sampling.
Trust-Based System to Defend Against the Spectrum Sensing Data Falsification Attack in Cognitive Radio Ad Hoc Network

The underutilization of the spectrum band has led to the problems of spectrum shortage. While some spectrum bands remain unutilized, some radio devices are overcrowded in their spectrum bands. Cognitive radio networks address the challenges of spectrum underutilization by allowing the secondary devices to utilize the vacant spectrum band of the licensed devices opportunistically. The secondary devices have to incorporate spectrum sensing cooperatively. Due to cooperative spectrum sensing, security challenges like the spectrum sensing data falsification (SSDF) attack have emerged. The SSDF attack is an attack whereby malicious secondary users report incorrect spectrum information to their neighbouring nodes which can cause interference to licensed users. This paper proposes a trust-based system known as the reputation-based system to mitigate the effects of the SSDF attack in cognitive radio ad hoc networks. Results are presented in forms of graphs simulated using Matlab installed in Windows 10 operating system.
Fast and Adaptive Blind Audio Source Separation Using Recursive Levenberg-Marquardt Synchrosqueezing

This paper revisits the Degenerate Unmixing Estimation Technique (duet) for blind audio separation of an arbitrary number of sources given two mixtures through a recursively computed and adaptive time-frequency representation. Recently, synchrosqueezing was introduced as a promising signal disentangling method which allows to compute reversible and sharpen time-frequency representations. Thus, it can be used to reduce overlaps between the sources in the time-frequency plane and to improve the sources' sparsity which is often exploited by source separation techniques. Furthermore, synchrosqueezing can also be extended using the Levenberg-Marquardt algorithm to allow a user to adjust the energy concentration of a time-frequency representation which can be efficiently implemented without the FFT algorithm. Hence, we show that our approach can improve the quality of the source separation process while remaining suitable for real-time applications.
Why grit requires perseverance and passion to positively predict performance

Significance Grit has captured the public imagination and crept into educational policy throughout the United States. However, because prior studies linking grit and performance are beset by contradictory evidence, commentators increasingly state that grit is overhyped. We propose that the inconsistency between grit’s initial promise and its subsequent lack of empirical support has occurred because grit’s measurement has not matched its definition. Although grit is defined as the combination of perseverance and passion, its measurement has focused on perseverance and has not adequately captured passion. In a metaanalysis of 127 studies and two field studies, we show that passion is a key component of grit. The current theory and results suggest that perseverance without passion isn’t grit, but merely a grind. Prior studies linking grit—defined as perseverance and passion for long-term goals—to performance are beset by contradictory evidence. As a result, commentators have increasingly declared that grit has limited effects. We propose that this inconsistent evidence has occurred because prior research has emphasized perseverance and ignored, both theoretically and empirically, the critical role of passion, which we define as a strong feeling toward a personally important value/preference that motivates intentions and behaviors to express that value/preference. We suggest that combining the grit scale—which only captures perseverance—with a measure that assesses whether individuals attain desired levels of passion will predict performance. We first metaanalyzed 127 studies (n = 45,485) that used the grit scale and assessed performance, and found that effect sizes are larger in studies where participants were more passionate for the performance domain. Second, in a survey of employees matched to supervisor-rated job performance (n = 422), we found that the combination of perseverance, measured through the grit scale, and passion attainment, measured through a new scale, predicted higher performance. A final study measured perseverance and passion attainment in a sample of students (n = 248) and linked these to their grade-point average (GPA), finding that the combination of perseverance and passion attainment predicted higher GPAs in part through increased immersion. The present results help resolve the mixed evidence of grit’s relationship with performance by highlighting the important role that passion plays in predicting performance. By adequately measuring both perseverance and passion, the present research uncovers grit’s true predictive power.
Scene Image Classification Using Reduced Virtual Feature Representation in Sparse Framework

In this paper, we address the task of scene image classification in sparse framework. Recent scene image datasets consist of thousands of different size images with size of the order of 106 pixels. Motivated by the fact that every image has a different size, we propose a dynamic kernel1 which works over set of feature maps obtained for an image from last convolutional pooling layer of a pre-trained CNN. The size of feature maps depends on the input image size leading to the requirement of a dynamic kernel to compute similarity score between feature maps of different images. The kernel matrix obtained by using a dynamic kernel is large in size owing to the large number of training examples. To handle this we propose to use the concept of reduced virtual features (RVFs) obtained by diagonalizing the kernel matrix. RVF is a fixed length representation of a scene image irrespective of its true size. Classification is done in sparse framework by applying block sparsity constraint over sparse coefficients using dictionary built from RVFs. The proposed approach tested over standard datasets like Vogel-Schiele, MIT-8, MIT-67 and SUN-397 yields good results.
Exploring Motor Imagery Eeg Patterns for Stroke Patients with Deep Neural Networks

Studies show that motor imagery based Brain-Computer Interface (BCI) systems can be utilized therapeutically in stroke rehabilitation. Efficient decoding of subjects' motor intentions is essential in BCI-based rehabilitation systems to manipulate a neural prosthesis or other devices for motor relearning. However, due to cortical reorganization, the desynchro-nization potential evoked by the motor imagery of patients with brain lesions is quite different from that evoked by the motor imagery of normal subjects. These differences can be attributed to active cortex regions, frequency bands and amplitude. In this paper, we use a deep learning method to explore the EEG patterns of key channels and the frequency band for stroke patients. The EEG data is bandpass filtered into multiple sub-bands split by a sliding window strategy. Under these sub-bands, diverse spatial-spectral features are extracted and fed into a deep neural network for classification, uncovering the spectral patterns (bandpass filters) and spatial patterns (spatial weights). Experimental results from five stroke patients show that our method has higher classification accuracy than several state-of-the-art approaches. By tracking gradual changes in EEG patterns during rehabilitation, we try to uncover the neurophysiological plasticity mechanism in the impaired cortexes of stroke patients.
Balanced and positively worded personality short-forms: Mini-IPIP validity and cross-cultural invariance

Background The Mini-IPIP scales (Donellan et al., 2006) are possibly one of the most commonly used short inventories for measuring the Big Five Factors of personality. In this study, we aimed to investigate the psychometric properties of two Mini-IPIP Spanish short forms, one balanced and one positively wording (PW). Method Two samples, one from native Spanish speakers and another from native English speakers, made up a total of 940 participants in this study. The short forms were translated and adapted based on international guidelines. Reliability (internal and composite) and validity analyses (construct ESEM, concurrent, predictive and cross-cultural invariance through multi-group factorial models) were performed. Results For both the balanced scale and the PW one, modeling a method factor was not relevant. The reliability and validity indices of both forms were according to theory and prior studies’ findings: (a) personality factors were medium-high related to affective factors; (b) personality factors were less related to life satisfaction than affective factors; (c) life satisfaction was medium-high related to affective factors; (d) neuroticism appeared mainly related to all criteria variables; and (e) an acceptable level of invariance was achieved with regard to the English version. Discussion This study contributes to research on personality assessment by providing the first evidence regarding the psychometric properties of a PW short measure. These results suggest that PW short scales of personality used after data screening techniques may be appropriate for future studies (e.g., cross-cultural, content validity).