Miracle-dz/UniFAR-FTUs huggingface dataset:
RecycleNet: Intelligent Waste Sorting Using Deep Neural Networks

Waste management and recycling is the fundamental part of a sustainable economy. For more efficient and safe recycling, it is necessary to use intelligent systems instead of employing humans as workers in the dump-yards. This is one of the early works demonstrating the efficiency of latest intelligent approaches. In order to provide the most efficient approach, we experimented on well-known deep convolutional neural network architectures. For training without any pre-trained weights, Inception-Resnet, Inception-v4 outperformed all others with 90% test accuracy. For transfer learning and fine-tuning of weight parameters using ImageNet, DenseNet121 gave the best result with 95% test accuracy. One disadvantage of these networks, however, is that they are slightly slower in prediction time. To enhance the prediction performance of the models we altered the connection patterns of the skip connections inside dense blocks. Our model RecycleNet is carefully optimized deep convolutional neural network architecture for classification of selected recyclable object classes. This novel model reduced the number of parameters in a 121 layered network from 7 million to about 3 million.
Impact of In-Pharmacy Education on Patients’ Knowledge and Attitudes About Clinical Trials

The lack of public awareness and understanding about clinical research has long hindered the efficiency and speed in recruiting patients to participate in clinical trials. Earlier research conducted by the Center for Information and Study on Clinical Research Participation (CISCRP) examined whether pharmacists might be a viable channel to educate and engage the public about clinical research, and the results suggested that pharmacy-directed outreach and education are feasible. This study measured the impact of in-pharmacy education on patient comprehension and willingness to participate in clinical research. In collaboration with McKesson and its network of independent community pharmacies, CISCRP trained 32 pharmacists and provided them with educational materials to display and/or distribute at their pharmacies for a period of 2 to 3 months. Presurveys and postsurveys among 487 patients were conducted to gather baseline measures and to assess the impact of educational materials and in-pharmacy discussions. A postsurvey was also conducted among pharmacists. The results of the study show that patient discussions with their pharmacists and patient review of educational materials distributed through pharmacies positively impacted patient awareness, comprehension, and willingness to participate in clinical trials. Indeed, during the study period, 4% of patients who reviewed the materials chose to volunteer for clinical trials. Nearly all baseline measures of awareness and comprehension increased by 10 to 20 percentage points. Respondents were more interested in learning about clinical research after speaking with their pharmacists and reviewing educational materials, and 40% were more likely to recommend participation to a friend or family member.
Removing the Feature Correlation Effect of Multiplicative Noise

Multiplicative noise, including dropout, is widely used to regularize deep neural networks (DNNs), and is shown to be effective in a wide range of architectures and tasks. From an information perspective, we consider injecting multiplicative noise into a DNN as training the network to solve the task with noisy information pathways, which leads to the observation that multiplicative noise tends to increase the correlation between features, so as to increase the signal-to-noise ratio of information pathways. However, high feature correlation is undesirable, as it increases redundancy in representations. In this work, we propose non-correlating multiplicative noise (NCMN), which exploits batch normalization to remove the correlation effect in a simple yet effective way. We show that NCMN significantly improves the performance of standard multiplicative noise on image classification tasks, providing a better alternative to dropout for batch-normalized networks. Additionally, we present a unified view of NCMN and shake-shake regularization, which explains the performance gain of the latter.
A Review of Integrated Motor Drive and Wide-Bandgap Power Electronics for High-Performance Electro-Hydrostatic Actuators

An integration of an electric motor and a drive with wide-bandgap (WBG) devices possesses numerous attractive features for electrified and decentralized actuation systems. The WBG devices can operate at a high-junction temperature (>170 °C) with improved efficiency due to fast switching speed and low on-state resistance. It also leads to better performance and higher power density electro-hydrostatic actuators (EHAs) than the traditional solutions, which are being widely adopted in industrial applications such as aerospace, robotics, automobiles, manufacturing, wind turbine, and off-road vehicles. This paper introduces and investigates the benefits of the integrated motor drive with the WBG-based power electronics for the EHA systems.
Customer Behavior Analysis Towards Online Shopping using Data Mining

Study of customer behavior in online shopping usually deals with identification of customers and their buying behavior patterns. The aim of such studies is to make certain who buys where, what, when and how. The results of these studies are useful in the solution of marketing problems. Various studies on customer purchasing behaviors have been presented and used in real problems. For analysis of customer behaviors data mining techniques are consider more effective. The target of this paper is to analyze behavior of such people who are visiting the online shopping sites and spending their time there, surfing for different stuff. It would also be taken into account that how many people are there and how many of them are actually shopping. In this paper, different queries are applied to mine the database of a specified site which results in analysis of customer behavior towards online shopping.
Unbalanced Three-Phase Distribution Grid Topology Estimation and Bus Phase Identification

There is an increasing need for monitoring and controlling uncertainties brought by distributed energy resources in distribution grids. For such goal, accurate three-phase topology is the basis for correlating and exterminating measurements in unbalanced distribution networks. Unfortunately, such topology knowledge is often unavailable due to limited investment, especially for secondary distribution grids. Also, the bus phase connectivity information is inaccurate due to human errors or outdated records. For this challenge, we utilize smart meter data at different phases for an information-theoretic approach to learn the structures. Specifically, we convert the system of three unbalanced phasors into symmetrical components, namely the positive, negative, and zero sequences. Then, we prove that Chow-Liu algorithm can find the optimal topology by utilizing power flow equation and the conditional independence relationships implied by the radial three-phase structure of distribution grids with the presence of incorrect bus phase labels. At last, by utilizing Carson's equation, we prove that the bus phase connection can be correctly identified using voltage measurements. For validation, we extensively simulate on IEEE $37$- and $123$-bus systems using real data from PG\&E, ADRES Project, and Pecan Street. We observe that the algorithm is highly accurate for finding three-phase topology in distribution grids even with strong load unbalancing condition and DERs. This ensures close monitoring and controlling DERs in distribution grids.
Diagnostic value of KL-6 in idiopathic interstitial pneumonia.

Background Idiopathic interstitial pneumonia (IIP) can induce type II alveolar epithelial cell proliferation and pulmonary basement membrane damage and subsequent release of Krebs von den Lungen-6 antigen (KL-6) to the bloodstream. This study investigated the diagnostic and prognostic value of serum KL-6 levels for IIP. Methods One hundred five patients with lung disease were divided into IIP (n=75) and non-IIP groups (n=30) according to pathological and computed tomography findings. Serum KL-6 levels were evaluated in blood samples from all subjects. Nineteen IIP group patients were also subjected to a longitudinal study of disease progression and serum KL-6 levels over time. Results Serum KL-6 levels were significantly higher in the IIP group vs. the non-IIP group [1,096.0 (565.0-1,544.0) vs. 226.0 (173.5-346.5) U/mL; P<0.01]. Within the IIP group, serum KL-6 levels differed significantly between patients with and without concomitant disease or pulmonary infection (Z=-2.475, P=0.013). In a receiver operating characteristic (ROC) curve analysis, the area below the curve for serum KL-6 was 0.911 [95% confidence interval (CI): 0.847-0.975, P<0.001], indicating a good diagnostic performance for IIP, with a cut-off level of 485 U/mL, sensitivity of 85.33%, specificity of 90.00%, positive predictive value (PPV) of 95.52%, negative predictive value (NPV) of 71.05%, and Kappa value of 0.70. Accordingly, the serum KL-6 and clinical diagnostic results were consistent. Moreover, in the longitudinal study, the serum KL-6 levels differed significantly from before to after treatment in patients with exacerbated or improved disease (P=0.004 and P=0.043, respectively), whereas no obvious changes were observed in patients with stable disease (P=0.692). Conclusions The serum KL-6 level is a valuable and significant diagnostic marker of IIP and a useful predictor of clinical prognosis.
Protein palmitoylation and cancer

Protein S‐palmitoylation is a reversible post‐translational modification that alters the localization, stability, and function of hundreds of proteins in the cell. S‐palmitoylation is essential for the function of both oncogenes (e.g., NRAS and EGFR) and tumor suppressors (e.g., SCRIB, melanocortin 1 receptor). In mammalian cells, the thioesterification of palmitate to internal cysteine residues is catalyzed by 23 Asp‐His‐His‐Cys (DHHC)‐family palmitoyl S‐acyltransferases while the removal of palmitate is catalyzed by serine hydrolases, including acyl‐protein thioesterases (APTs). These enzymes modulate the function of important oncogenes and tumor suppressors and often display altered expression patterns in cancer. Targeting S‐palmitoylation or the enzymes responsible for palmitoylation dynamics may therefore represent a candidate therapeutic strategy for certain cancers.
Generating 3D Adversarial Point Clouds

Deep neural networks are known to be vulnerable to adversarial examples which are carefully crafted instances to cause the models to make wrong predictions. While adversarial examples for 2D images and CNNs have been extensively studied, less attention has been paid to 3D data such as point clouds. Given many safety-critical 3D applications such as autonomous driving, it is important to study how adversarial point clouds could affect current deep 3D models. In this work, we propose several novel algorithms to craft adversarial point clouds against PointNet, a widely used deep neural network for point cloud processing. Our algorithms work in two ways: adversarial point perturbation and adversarial point generation. For point perturbation, we shift existing points negligibly. For point generation, we generate either a set of independent and scattered points or a small number (1-3) of point clusters with meaningful shapes such as balls and airplanes which could be hidden in the human psyche. In addition, we formulate six perturbation measurement metrics tailored to the attacks in point clouds and conduct extensive experiments to evaluate the proposed algorithms on the ModelNet40 3D shape classification dataset. Overall, our attack algorithms achieve a success rate higher than 99% for all targeted attacks.
Distributed error confinement

We study error confinement in distributed applications, which can be viewed as an extreme case of various fault locality notions studied in the past. Error confinement means that to the external observer, only nodes that were directly hit by a fault may deviate from their specified correct behavior, and only temporarily. The externally observable behavior of all other nodes must remain impeccable, even though their internal state may be affected. Error confinement is impossible if an adversary is allowed to inflict arbitrary transient faults on the system, since the faults might completely wipe out input values. We introduce a new fault-tolerance measure we call agility, which quantifies the fault tolerance of an algorithm that disseminates information against state corrupting faults. We then propose broadcast algorithms that guarantee error confinement with optimal agility to within a constant factor in synchronous networks. These algorithms can serve as building blocks in more general reactive systems. Previous results in exploring locality in reactive systems were not error confined, or allowed a wide range of behaviors to be considered correct. Our results also include a new technique that can be used to analyze the “cow path” problem.
Group membership: Who gets to decide?

Abstract In this commentary, I focus on several problems that the authors' understanding of group identity raises: the legality of avoiding background diversity, the problem of effectively unshareable knowledge, the practical quality of some outcomes arrived at by groups with homogeneous backgrounds, and moral issues about fairness. I note also that much recent research challenges the view that background diversity is more likely to be a detriment than a benefit.
An Orientation Factor for Object-Oriented SLAM

Current approaches to object-oriented SLAM lack the ability to incorporate prior knowledge of the scene geometry, such as the expected global orientation of objects. We overcome this limitation by proposing a geometric factor that constrains the global orientation of objects in the map, depending on the objects' semantics. This new geometric factor is a first example of how semantics can inform and improve geometry in object-oriented SLAM. We implement the geometric factor for the recently proposed QuadricSLAM that represents landmarks as dual quadrics. The factor probabilistically models the quadrics' major axes to be either perpendicular to or aligned with the direction of gravity, depending on their semantic class. Our experiments on simulated and real-world datasets show that using the proposed factors to incorporate prior knowledge improves both the trajectory and landmark quality.
Different Mechanisms of Two Subtypes of Perforating Artery Infarct in the Middle Cerebral Artery Territory: A High-Resolution Magnetic Resonance Imaging Study

Purpose: Perforating Artery Infarcts (PAIs) can be divided into two subtypes based on their etiologies: branch Atheromatous Disease (BAD) and Lacunar Infarct (LI). Recent studies have shown that while both subtypes can be caused by large artery lesions, the different mechanisms that underlie their development are not clear. This study was designed to use High-Resolution Magnetic Resonance Imaging (HRMRI) to explore the differences that contribute to the occurrence of these two subtypes in large artery lesions in the anterior circulation. Methods: Fifty patients with an acute PAI in the anterior circulation were enrolled (32 BAD and 18 LI patients). The ipsilateral middle cerebral artery (MCA) was scanned with HRMRI to analyze the atherosclerosis plaques. Artery remodeling and plaque characteristics of MCA lesions were compared between the two subtypes. Results: The rate of MCA lesions was significantly higher in BAD and substantially lower in LI (P = 0.033). LAs for the lumen areas in Bad, they were smaller than LI (P < 0.001), Additionally, the plaque area (P = 0.001) and plaque burden (P < 0.001) were superior in the BAD group. Most BAD patients displayed non-positive remodeling, while the great majority of LI patients showed positive remodeling (P < 0.001). Conclusion: In the anterior circulation, a considerable amount of BAD and LI share similarities with atherosclerotic plaques in large arteries. BAD patients mainly showed relatively large and stable atherosclerotic plaques in large arteries, while LI patients mainly exhibited relatively small and unstable atherosclerotic plaques. Clinical Trial Registration: This clinical trial is a retrospective study and therefore does not require registration.
Redundancy-Guaranteed and Receiving-Constrained Disaster Backup in Cloud Data Center Network

For disaster backup in cloud data center network, existing researches have not jointly considered sufficient data redundancy and limited receiving capacity, likely to result in underutilization of network transmission capability, unfair distribution of backup load, or even lacks of disaster resistance. In this paper, we propose a new strategy to realize bandwidth-efficient and load-fair disaster backup under redundancy and capacity constraints using customized bandwidth allocation and flexible flow scheduling in software-defined networking. Based on many-to-many relationship in disaster backup, we formulate a new redundancy-guaranteed and receiving-constrained capacitated multi-commodity flow problem. By constructing flow-ratio-constrained backup transmission model, we specify flow allocation ratio among backup data centers with limited receiving capacity. Then we present a basic ratio-aware ant colony optimization algorithm satisfying backup flow constraint and rate requirement constraint. Furthermore, to obtain higher performance in redundancy guarantee and enhance bandwidth allocation fairness among massive backup transfers, we propose a fair-rotating and ratio-aware ant colony optimization (FRRA-ACO) algorithm. Especially, we use rotary routing search for multiple concurrent flows based on backup requirement cloning to approximate the upper bound of bandwidth allocation, adjust ratio of bandwidth allocation for multiple backup transfers with different requirements, and further improve flow rate according to the maximum link utilization on links if possible. Experiments demonstrate that FRRA-ACO outperforms state-of-the-art algorithms with less backup completion time, fairer backup load distribution, and higher network utilization.
Efficient Light Field Images Compression Method Based on Depth Estimation and Optimization

Massive recent investigations from both industry and academia have been poured into autostereoscopic display. One of the emerging techniques, light field image (LFI), can provide more immersive perception by increasing the number of views and the spatial resolution. However, these advantages restrict the storage and transmission due to such dense-view image simultaneously. To solve this problem, we propose to compress the LFI using multiview video plus depth (MVD) coding architecture. In this paper, we preliminarily estimate the depth based on the concept of epipolar plane image. To achieve a depth value, we design an optimal slope decision algorithm to determine the best slope with the minimal cost. While this rough estimation produces some error points within initial depth map, therefore, we present a depth optimization algorithm using the characteristic of the associated texture image. Ultimately, a small number of selected viewpoint images are encoded with their corresponding depth maps using the MVD framework, and then, the unselected viewpoint images are synthesized by depth image-based rendering technique. To verify the validity of the proposed LFI compression scheme, extensive experiments are conducted. The simulated results demonstrate that the proposed depth map estimation algorithm is superior to other state-of-the-art methods for the LFI. Meanwhile, our LFI compression method outperforms other LFI compression algorithms significantly.
Do positive illusions of control foster happiness?

Positive emotions have been shown to benefit from optimistic perceptions, even if these perceptions are illusory (Seligman & Csikszentmihalyi, 2000). The current research investigated the effects of increases and decreases in illusory control on positive and negative emotions. In two studies we experimentally induced changes in illusory perceptions of control (increase vs. decrease of illusory control) and assessed the extent to which these changes, in turn, influenced participants' emotions. Extending prior research, the results of both studies revealed that whereas illusions of personal control over environmental outcomes mitigated the experience of negative emotions, they did not foster positive emotions. Perceiving a loss of illusory control, however, significantly reduced the experience of positive emotions, but had no effect on negative emotions. Implications for emotion theory and intervention programs are discussed. (PsycINFO Database Record
Sexual selection on multiple female ornaments in dance flies

Sex-specific ornaments typically occur in males, but they can also develop in females. While there are several models concerning the evolution of male-specific ornaments, it is not clear how, or under what circumstances, those models apply to female-specific ornament evolution. Here, we present a manipulative field experiment that explores the theoretical ‘trait space’ of multiple female-specific ornaments to study how these unusual traits evolved. We measured the attractiveness of two female-specific ornaments (pinnate leg scales and inflatable abdominal sacs) in the dance fly Rhamphomyia longicauda in a wild mating swarm. We found significant directional preferences for larger ornaments of both types; however, variation in one of the ornaments (abdominal sacs) was almost three times more effective at improving attractiveness. The abdominal ornament was consistently effective in increasing attractiveness to males regardless of leg ornament expression, while leg ornament size was only effective if abdominal ornaments were very small. These results are consistent with predictions from a sexual conflict model of ornament expression in supporting the probable role of deception in the evolution of female-specific ornaments among dance flies. Sexual conflict can be an important force in generating elaborate sex-specific ornaments in females as well as males.
Better Conversations by Modeling, Filtering, and Optimizing for Coherence and Diversity

We present three enhancements to existing encoder-decoder models for open-domain conversational agents, aimed at effectively modeling coherence and promoting output diversity: (1) We introduce a measure of coherence as the GloVe embedding similarity between the dialogue context and the generated response, (2) we filter our training corpora based on the measure of coherence to obtain topically coherent and lexically diverse context-response pairs, (3) we then train a response generator using a conditional variational autoencoder model that incorporates the measure of coherence as a latent variable and uses a context gate to guarantee topical consistency with the context and promote lexical diversity. Experiments on the OpenSubtitles corpus show a substantial improvement over competitive neural models in terms of BLEU score as well as metrics of coherence and diversity.