Miracle-dz/UniFAR-FTUs huggingface dataset:
Beyond Global Average Pooling: Alternative Feature Aggregations for Weakly Supervised Localization

: Weakly supervised object localization (WSOL) enables the detection and segmentation of objects in applications where localization annotations are hard or too expensive to obtain. Nowadays, most relevant WSOL approaches are based on class activation mapping (CAM), where a classiﬁcation network utilizing global average pooling is trained for object classiﬁcation. The classiﬁcation layer that follows the pooling layer is then repurposed to generate segmentations using the unpooled features. The resulting localizations are usually im-precise and primarily focused around the most discriminative areas of the object, making a correct indication of the object location difﬁcult. We argue that this problem is inherent in training with global average pooling due to its averaging operation. Therefore, we investigate two alternative pooling strategies: global max pooling and global log-sum-exp pooling. Furthermore, to increase the crispness and resolution of localization maps, we also investigate the application of Feature Pyramid Networks, which are commonplace in object detection. We conﬁrm the usefulness of both alternative pooling methods as well as the Feature Pyramid Network on the CUB-200-2011 and OpenImages datasets.
High-Occupancy Toll Lane Decision Making: Income Effects on I-85 Express Lanes, Atlanta, Georgia

The I-85 express lanes were the first value pricing facility in the Atlanta, Georgia, metropolitan region. This study investigated the behavior and decision making of users and nonusers of those high-occupancy toll (HOT) lanes. The study used a unique combination of express lane data and household demographic data, obtained from a private marketing firm, to examine decision making at the trip level for users from different income segments and demographic clusters. Trip characteristics (e.g., toll amount and trip length, operating conditions on HOT and general purpose lanes) and socioeconomic characteristics (e.g., household income and size) were incorporated into the choice models. This study also provided a summary of express lane use characteristics by income segments and investigated trip speeds by lane types and income categories. Lane choice decisions were modeled with binary logit methods to examine potential determinants of HOT lane use. Households were segmented into lower-, medium-, and higher-income groups to illustrate the differences in responses across these categories. In addition, households were clustered by their demographic characteristics to compare lane choice determinants across similar households. Elasticities for the various factors were calculated. The results showed slightly higher rates of HOT use among the higher-income segment, inelastic demand sensitivities for most factors, and few differences between lane choice determinants across income groups. With goodness-of-fit values that ranged from . 18 to . 21, these disaggregate models left most lane choice variation unexplained.
Carbon footprint of cows’ milk: a case study of peri-urban and urban dairy farms within Mekelle milk-shed, Ethiopia

Abstract In Ethiopia, there is an urge to enhance milk production where urban and peri-urban farms play important role. Therefore, the role of such production system in Greenhouse gas (GHG) emissions should be known. This study was conducted with the objective of estimating the carbon footprint (CF) of milk within Mekelle milkshed, Ethiopia. 50 urban and 42 peri-urban dairy farms were selected randomly and both primary and secondary data was collected. Lifecycle Assessment (LCA) approach was employed to quantify GHG emissions using cradle to- farm gate approach. The mean GHG emissions per cattle unit (000 kg CO2-e y-1) were 2.84 ± 1.23 and 3.19 ± 1.99 for peri-urban and urban farms, respectively. The share of enteric fermentation was 75.5% and 73.6% for peri-urban and urban farms, respectively. Milk contributed for 88.5% and 90.8% to the economic value of peri-urban and urban farms, respectively. Overall, the CF of milk production in urban and peri-urban farms was 2.2 kg CO2-e/kg and 3.2 kg CO2-e/kg without economic allocation, and 2.0 kg CO2-e/kg and 2.8 kg CO2-e/kg with economic allocation. In order to reduce GHG emission intensity from dairy farms it is important to adopt climate smart dairy practices.
Three-Dimensional Interconnected Porous Partially Unzipped MWCNT/Graphene Composite Aerogels as Electrodes for High-Performance Supercapacitors

The self-restacking of graphene nanosheets inevitably compromises the electrochemical performance of conventional graphene-based materials. Herein, to solve this problem, we prepared a new type of three-dimensional porous aerogel with partially unzipped multiwalled carbon nanotubes inserted into graphene nanosheets via a reduction-reaction-induced self-assembly process. In the resulting aerogels, the inner carbon nanotubes (CNTs) tightly attach to the unzipped outer graphene nanoribbons (GNRs), which bridge with the graphene nanosheets. These interconnections bring them excellent electrical contact; the CNTs act as spacers to prevent the restacking of adjacent graphene nanosheets, and the abundant interconnected pores in the aerogels provide large channels for charge transfer. Accordingly, the aerogels exhibit a specific capacitance of 348.4 Fg−1 at a scan rate of 5 mVs−1, with capacitance retention remaining at 89.7% at a current density of 2 Ag−1 after 5000 cycles. The results show that the aerogels are promising electrode materials for supercapacitor applications.
Synergistic Adverse Effects of Azithromycin and Hydroxychloroquine on Human Cardiomyocytes at a Clinically Relevant Treatment Duration

Adverse effects of drug combinations and their underlying mechanisms are highly relevant for safety evaluation, but often not fully studied. Hydroxychloroquine (HCQ) and azithromycin (AZM) were used as a combination therapy in the treatment of COVID-19 patients at the beginning of the pandemic, leading to higher complication rates in comparison to respective monotherapies. Here, we used human-induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) to systematically investigate the effects of HCQ, AZM, and their combination on the structure and functionality of cardiomyocytes, and to better understand the underlying mechanisms. Our results demonstrate synergistic adverse effects of AZM and HCQ on electrophysiological and contractile function of iPSC-CMs. HCQ-induced prolongation of field potential duration (FPDc) was gradually increased during 7-day treatment period and was strongly enhanced by combination with AZM, although AZM alone slightly shortened FPDc in iPSC-CMs. Combined treatment with AZM and HCQ leads to higher cardiotoxicity, more severe structural disarrangement, more pronounced contractile dysfunctions, and more elevated conduction velocity, compared to respective monotreatments. Mechanistic insights underlying the synergistic effects of AZM and HCQ on iPSC-CM functionality are provided based on increased cellular accumulation of HCQ and AZM as well as increased Cx43- and Nav1.5-protein levels.
Experimental Investigation of a Flexible Wing with a Variable Camber Continuous Trailing Edge Flap Design

Nhan Nguyen ⇤ NASA Ames Research Center, Moffett Field, CA 94035 Nathan Precup† University of Washington, Seattle, WA 98195 James Urnes, Sr. ‡ Boeing Research & Technology, St. Louis, MO 63134 Chester Nelson § Boeing Commercial Airplanes, WA Sonia Lebofsky ¶ Stinger Ghaffarian Technologies Inc., Moffett Field, CA 94035 Eric Ting k Stinger Ghaffarian Technologies Inc., Moffett Field, CA 94035 Eli Livne ⇤⇤ University of Washington, Seattle, WA 98195
Hybrid FPGA–CPU-Based Architecture for Object Recognition in Visual Servoing of Arm Prosthesis

The present paper proposes an implementation of a hybrid hardware–software system for the visual servoing of prosthetic arms. We focus on the most critical vision analysis part of the system. The prosthetic system comprises a glass-worn eye tracker and a video camera, and the task is to recognize the object to grasp. The lightweight architecture for gaze-driven object recognition has to be implemented as a wearable device with low power consumption (less than 5.6 W). The algorithmic chain comprises gaze fixations estimation and filtering, generation of candidates, and recognition, with two backbone convolutional neural networks (CNN). The time-consuming parts of the system, such as SIFT (Scale Invariant Feature Transform) detector and the backbone CNN feature extractor, are implemented in FPGA, and a new reduction layer is introduced in the object-recognition CNN to reduce the computational burden. The proposed implementation is compatible with the real-time control of the prosthetic arm.
Qualitative and Quantitative Comparison of Hippocampal Volumetric Software Applications: Do All Roads Lead to Rome?

Brain volumetric software is increasingly suggested for clinical routine. The present study quantifies the agreement across different software applications. Ten cases with and ten gender- and age-adjusted healthy controls without hippocampal atrophy (median age: 70; 25–75% range: 64–77 years and 74; 66–78 years) were retrospectively selected from a previously published cohort of Alzheimer’s dementia patients and normal ageing controls. Hippocampal volumes were computed based on 3 Tesla T1-MPRAGE-sequences with FreeSurfer (FS), Statistical-Parametric-Mapping (SPM; Neuromorphometrics and Hammers atlases), Geodesic-Information-Flows (GIF), Similarity-and-Truth-Estimation-for-Propagated-Segmentations (STEPS), and Quantib™. MTA (medial temporal lobe atrophy) scores were manually rated. Volumetric measures of each individual were compared against the mean of all applications with intraclass correlation coefficients (ICC) and Bland–Altman plots. Comparing against the mean of all methods, moderate to low agreement was present considering categorization of hippocampal volumes into quartiles. ICCs ranged noticeably between applications (left hippocampus (LH): from 0.42 (STEPS) to 0.88 (FS); right hippocampus (RH): from 0.36 (Quantib™) to 0.86 (FS). Mean differences between individual methods and the mean of all methods [mm3] were considerable (LH: FS −209, SPM-Neuromorphometrics −820; SPM-Hammers −1474; Quantib™ −680; GIF 891; STEPS 2218; RH: FS −232, SPM-Neuromorphometrics −745; SPM-Hammers −1547; Quantib™ −723; GIF 982; STEPS 2188). In this clinically relevant sample size with large spread in data ranging from normal aging to severe atrophy, hippocampal volumes derived by well-accepted applications were quantitatively different. Thus, interchangeable use is not recommended.
FNUG: Imperfect mazes traversal based on detecting and following the nearest-to-final-goal and unvisited gaps

Mazes traversal techniques are broadly classified into model-based and sensor information-based. In model-based techniques the configuration space is explicitly modeled in the form of a grid, potential field or a connectivity graph, while in sensor information-based all control decisions are based on processing the current sensor's data. A maze with no pre-model available is called unknown maze, while an imperfect maze is the one which has loops or closed circuits. Most of the traditional information-based unknown-maze traversal techniques are based on trial and error, which are time-consuming and may trap the robot in infinite loops or dead ends. To address the above issues, this paper proposed a gap-based approach for imperfect unknown mazes traversal named Follow the Nearest-to-final goal and Unvisited Gap (FNUG) for the first time. The algorithm is computationally efficient, and has the ability to deal with loops and dead ends through the revisit check. The main part of this approach is finding out gaps around the robot by analyzing the depth scans, building a coordinate system based topological map in the form of a tree. Then a heuristic selection of robot's unvisited-neighbor gap for navigation based on its Euclidean distance with the final target location. Compared to existing sensor information-based methods, our proposed algorithm not only takes into account the current sensor data, the shape and size of the robot, but also all the sub goals visited so far. This is the key factor of our algorithm that prevents the robot from falling into a dead end or trapping in a loop. To prove the effectiveness of this algorithm, simulations and physical experiments were performed using using a 2-wheeled differential mobile robot.
Thermoluminescence Dating in Architectural History: Venetian Villas

Thermoluminescence (TL) is the name given to an effect observed when certain minerals give off light created by natural radiation. Some of these minerals are contained in clay, and the effect occurs upon firing of the clay. The time elapsed between such firings can therefore be measured and serves as a reliable dating method. It is well established in the fields of archaeology (where it is used extensively for authenticity testing) and geology. Until recently this method has not been used in the discipline of architectural history. Here we propose specific adaptations of this method to this discipline and demonstrate the inherent limits in its accuracy. We examined a number of Venetian villas dating from the 15th to the 17th centuries, with three goals in mind: 1. To establish this method in architectural history by cross-checking it against well dated buildings. 2. To try and date buildings of unknown age. 3. To try and distinguish individual building phases of one and the same building. The results obtained confirmed all hopes placed in the method. In this paper we discuss in detail, some of these results exemplifying all three goals cited above; and we give a brief explanation of the scientific background and the sampling procedure.
A Novel Computer-Aided Diagnostic System for Early Detection of Diabetic Retinopathy Using 3D-OCT Higher-Order Spatial Appearance Model

Early diagnosis of diabetic retinopathy (DR) is of critical importance to suppress severe damage to the retina and/or vision loss. In this study, an optical coherence tomography (OCT)-based computer-aided diagnosis (CAD) method is proposed to detect DR early using structural 3D retinal scans. This system uses prior shape knowledge to automatically segment all retinal layers of the 3D-OCT scans using an adaptive, appearance-based method. After the segmentation step, novel texture features are extracted from the segmented layers of the OCT B-scans volume for DR diagnosis. For every layer, Markov–Gibbs random field (MGRF) model is used to extract the 2nd-order reflectivity. In order to represent the extracted image-derived features, we employ cumulative distribution function (CDF) descriptors. For layer-wise classification in 3D volume, using the extracted Gibbs energy feature, an artificial neural network (ANN) is fed the extracted feature for every layer. Finally, the classification outputs for all twelve layers are fused using a majority voting schema for global subject diagnosis. A cohort of 188 3D-OCT subjects are used for system evaluation using different k-fold validation techniques and different validation metrics. Accuracy of 90.56%, 93.11%, and 96.88% are achieved using 4-, 5-, and 10-fold cross-validation, respectively. Additional comparison with deep learning networks, which represent the state-of-the-art, documented the promise of our system’s ability to diagnose the DR early.
Neuromarketing - An Opportunity or a Threat? Neuromarketing - An Opportunity or a Threat?

consumer behaves consciously only in 5 % of cases. Thanks to this new approach, which links knowledge of neuropsychology, cognitive psychology and neuroscience to marketing decision - making environment, scientists can determine why consumers make certain decisions and which part of the brain “tells” them to do so, respectively what motivators are activated. Follows from the above, in most cases a consumer remembers only what fits into his mental patterns and he selects especially those data that support his views. Psychological findings say that consumer, in general, attaches the greatest importance to information relating to his own person. The aim of marketers is therefore to place the advertising message into consumer’s The reason of neuromarketing use lies in an attempt to understand consumer behaviour and motivation to make a purchase of certain product. This method is based on the assumption that the purchase decisions is largely influenced by emotions. The paper explains the essence of neuromarketing and selected neuromarketing technologies, such as Functional magnetic resonance imaging (FMRI), Electroencephalography (EEG) and Eye tracking. In this way, as a literature review, it provides a comparison of the views of several experts on the issue. An important part of the paper is to analyse the perception of neuromarketing by the professional and general public, which points to the advantages and disadvantages of its application. Accordingly, it is possible to summarize the benefits of using the results of neuromarketing research for companies that includes better understanding of customers, their loyalty, in order to strengthen their image and brand value.
Evaluation of Zinc, Copper, and Lead Levels in The Blood of Breast Cancer Women in Baghdad City

The current study was conducted on 100 females who were divided into two main groups; 60 with breast cancer and 40 healthy controls. Blood samples were collected from both premenopausal and postmenopausal breast cancer and healthy women. The samples were appropriately processed for the analysis of trace elements (zinc, copper, and lead) by using flame atomic absorption spectrophotometry (FAAS). The results showed a highly significant decrease (p< 0.01) in the mean serum level of zinc of in both pre- and postmenopausal breast cancer women (71.7 + 5.1 and 70.4 + 5.4 µg/dL, respectively) compared with healthy controls (89.7 + 10.2 and 97.5 + 13.2 µg/dL, respectively) . Also, a highly significant elevation (p< 0.01) in the mean serum level of copper (157.2 + 13.9 µg/dL and 157.4 +11.9 µg/dL, respectively) was found in pre- and postmenopausal breast cancer women as compared to healthy controls (122.2 + 15.5 µg/dL and 112.2 + 15.8 µg/dL, respectively). Furthermore, a highly significant elevation (p< 0.01) in the mean blood level of lead (20.7 + 2.5 µg/dL and 19.9 + 1.7 µg/dL, respectively) was found in pre- and postmenopausal breast cancer women compared to healthy controls (15.1 + 2.0 µg/dL and 14.6 + 2.3 µg/dL, respectively). It is concluded that the disturbance in the homeostasis of some trace elements (zinc, copper, and lead) may lead to the development and even progression of breast cancer in both pre- and postmenopausal women.
Comparing dual access approaches for exact solution of location problems

4 K O M U N I K Á C I E / C O M M U N I C A T I O N S 2 / 9 9 Článok sa zaoberá kapacitne neobmedzenými umiestňovacími úlohami. Umiestňovacia úloha pozostáva z umiestňovania nejakých zariadení s neobmedzenou kapacitou vo vytváranej sieti. Zariadenie môže byť najrôznejšieho druhu. Môže to byť servisné stredisko, ktoré môže byť umiestnené v uzle dopravnej siete. Ďalší druh zariadenia môže byť napríklad priamy vlak umiestňovaný do dopravného plánu. V porovnaní s predchádzajúcim prípadom má toto zariadenie charakter hrany v grafe. Základom výpočtovej úspešnosti často používanej metódy vetiev a hraníc je tesnosť dolnej hranice. V článku sú publikované výsledky numerických experimentov s metódou duálneho vzostupu pre riešenie uzlovej lokačnej úlohy na rozsiahlej sieti a sú tu porovnané viaceré možné prístupy pre výpočet dolnej hranice hranovej lokačnej úlohy.
Transcriptomic Analysis Suggests Auxin Regulation in Dorsal-Ventral Petal Asymmetry of Wild Progenitor Sinningia speciosa

The establishment of dorsal–ventral (DV) petal asymmetry is accompanied by differential growth of DV petal size, shape, and color differences, which enhance ornamental values. Genes involved in flower symmetry in Sinningia speciosa have been identified as CYCLOIDEA (SsCYC), but which gene regulatory network (GRN) is associated with SsCYC to establish DV petal asymmetry is still unknown. To uncover the GRN of DV petal asymmetry, we identified 630 DV differentially expressed genes (DV-DEGs) from the RNA-Seq of dorsal and ventral petals in the wild progenitor, S. speciosa ‘ES’. Validated by qRT-PCR, genes in the auxin signaling transduction pathway, SsCYC, and a major regulator of anthocyanin biosynthesis were upregulated in dorsal petals. These genes correlated with a higher endogenous auxin level in dorsal petals, with longer tube length growth through cell expansion and a purple dorsal color. Over-expression of SsCYC in Nicotiana reduced petal size by regulating cell growth, suggesting that SsCYC also controls cell expansion. This suggests that auxin and SsCYC both regulate DV petal asymmetry. Transiently over-expressed SsCYC, however, could not activate most major auxin signaling genes, suggesting that SsCYC may not trigger auxin regulation. Whether auxin can activate SsCYC or whether they act independently to regulate DV petal asymmetry remains to be explored in the future.
Global Distribution and Clinical Features of Pythiosis in Humans and Animals

Pythiosis is a difficult-to-treat infectious disease caused by Pythium insidiosum. The condition is unfamiliar among healthcare workers. Manifestation of pythiosis is similar to other fungal infections, leading to misdiagnosis and delayed treatment. The geographical extent of pythiosis at a global scale is unclear. This study aimed to analyze the clinical information recorded in the scientific literature to comprehensively project epidemiological characteristics, clinical features, and future trends of pythiosis. From 1980 to 2021, 4203 cases of pythiosis in humans (n = 771; 18.3%) and animals (primarily horse, dog, and cow; n = 3432; 81.7%), with an average of 103 cases/year, were recruited. Pythiosis case reports significantly increased in the last decade. Pythiosis spanned 23 tropical, subtropical, and temperate countries worldwide. Some patients acquired pythiosis from a trip to an endemic country. Strikingly, 94.3% of human cases were in India and Thailand, while 79.2% of affected animals were in the U.S.A. and Brazil. Clinical features of pythiosis varied. Vascular and ocular pythiosis were only observed in humans, whereas cutaneous/subcutaneous and gastrointestinal infections were predominant in animals. Mortality depended on host species and clinical forms: for example, none in patients with ocular pythiosis, 0.7% in cows with a cutaneous lesion, 26.8% in humans with vascular disease, 86.4% in dogs with gastrointestinal pathology, and 100% in several animals with disseminated infection. In summary, this study reports up-to-date epidemiological and clinical features of pythiosis in humans and animals. It increases awareness of this life-threatening disease, as the illness or outbreak can exist in any country, not limited to the endemic areas.
Comparative Assessment of Two Data Visualizations to Communicate Medical Test Results Online

As most countries in the world still struggle to contain the COVID-19 breakout, Data Visualization tools have become increasingly important to support decision-making under uncertain conditions. One of the challenges posed by the pandemic is the early diagnosis of COVID-19: To this end, machine learning models capable of detecting COVID-19 on the basis of hematological values have been developed and validated. This study aims to evaluate the potential of two Data Visualizations to effectively present the output of a COVID-19 diagnostic model to render it online. Specifically, we investigated whether any visualization is better than the other in communicating a COVID-19 test results in an effective and clear manner, both with respect to positivity and to the reliability of the test itself. The findings suggest that designing a visual tool for the general public in this application domain can be extremely challenging for the need to render a wide array of outcomes that can be affected by varying levels of uncertainty.