Miracle-dz/UniFAR-FTUs huggingface dataset:
Innovations and Economic Output Scale with Social Interactions in the Workforce

The COVID-19 pandemic of 2020 fundamentally changed the way we interact with and engage in commerce. Social distancing and stay-at-home orders leave businesses and cities wondering how future economic activity moves forward. The reduction in face-to-face interactions creates an impetus to understand how social interactivity influences economic efficiency and rates of innovation. Here, we create a measure of the degree to which a workforce engages in social interactions, analyzing its relationships to economic innovation and efficiency. We do this by decomposing U.S. occupations into individual work activities, determining which of those activities are associated with face-to-face interactions. We then re-aggregate the labor forces of U.S. metropolitan statistical areas (MSA) into a metric of urban social interactiveness. Using a novel measure of urbanized area, we then calculate each MSA’s density of social work activities. We find that our metric of urban socialness is positively correlated with a city’s per worker patent production. Furthermore, we use our set of social work activities to reaggregate the workforces of U.S. industries into a metric of industry social interactivness, finding that this measure scales superlinearly with an industry’s per worker GDP. Together, the results suggest that social interaction among workers is an important driver of both a city’s rate of invention and an industry’s economic efficiency. Finally, we briefly highlight analogies between cities and stars and discuss their potential to guide further research, vis-à-vis the density of social interactions “igniting” a city or industry.
Autonomous Navigation with Simultaneous Localization and Mapping in/outdoor

The necessity and use of wheelchairs are increasing with the constant rise of the world population and human needs. Admittedly, the advancements in technology over the years have helped to push forward from manual to electrical powered wheelchairs. However, these are still inaccessible to many individuals with specific disabilities. With the technological improvements over the years, electrical powered wheelchairs have helped filled the restrictions that come with manual wheelchairs, but only to an extent. Furthermore, the impact of intelligent wheelchairs can be recognized by allowing access to them to a more significant number of users, as they integrate smart and autonomous systems capable of behaviors such as obstacle detection and avoidance, apprehension of objects and autonomous navigation. Besides, given the complexity intelligent wheelchairs can obtain, it reaches a point where it is not feasible to properly test the system completely without any risks involved. One of the most studied areas in the field of mobile robotics is the SLAM problem, where the robot achieves a certain level of perception of its surroundings with the use of external sensors like cameras and lasers. Therefore, this dissertation explores a platform for a wheelchair capable of simultaneous localization and mapping (SLAM) with autonomous navigation, applied on a simulation environment. The simulation allowed to test the wheelchair in different situations with a distinct set of sensors, as to conclude what achieves better results for indoor and outdoor environments. With the tests done it was possible to conclude that only sensors like the ZED 2 or the RPLidar A3 are capable of obtaining reliable results in all use case scenarios studied. Additionally, the system is part of the IntellWheels 2.0 project, an intelligent wheelchair platform that strives to be capable of being easily adapted to any commercial electric wheelchair, aiding any person with any disabilities with different methods of inputs and a robust multimodal interface.
Refining Indirect Call Targets at the Binary Level

—Enforcing ﬁne-grained Control-Flow Integrity (CFI) is critical for increasing software security. However, for commercial off-the-shelf (COTS) binaries, constructing high-precision Control-Flow Graphs (CFGs) is challenging, because there is no source-level information, such as symbols and types, to assist in indirect-branch target inference. The lack of source-level information brings extra challenges to inferring targets for indirect calls compared to other kinds of indirect branches. Points-to analysis could be a promising solution for this problem, but there is no practical points-to analysis framework for inferring indirect call targets at the binary level. Value set analysis (VSA) is the state-of-the-art binary-level points-to analysis but does not scale to large programs. It is also highly conservative by design and thus leads to low-precision CFG construction. In this paper, we present a binary-level points-to analysis framework called BPA to construct sound and high-precision CFGs. It is a new way of performing points-to analysis at the binary level with the focus on resolving indirect call targets. BPA employs several major techniques, including assuming a block memory model and a memory access analysis for partitioning memory into blocks, to achieve a better balance between scalability and precision. In evaluation, we demonstrate that BPA achieves a 34.5% precision improvement rate over the current state-of-the-art technique without introducing false negatives.
High Expression of CDCA7 Predicts Poor Prognosis in Clear Cell Renal Cell Carcinoma and Its Associations With Immunity

BackgroundCell division cycle-associated 7 (CDCA7), as a member of the cell division cycle associated family, was reported to be aberrantly expressed in both solid tumors and hematological tumors, suggesting its essential role in promoting tumorigenesis. Hence, we aimed to explore its comprehensive role of overall survival (OS) in clear cell renal cell carcinoma (ccRCC) and emphasis on immunity.MethodsThe RNA sequencing data and corresponding clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. Gene set enrichment analysis (GSEA) was adopted to explore CDCA7 associated signaling pathways. Univariate and multivariate Cox regression analyses were carried out to assess independent prognostic factors. Furthermore, roles of CDCA7 in human immunity were also investigated.ResultsOur results suggested that CDCA7 was overexpressed in ccRCC and its elevated expression was related to shorter OS (P<0.01). Univariate and multivariate Cox regression analyses identified CDCA7 as an independent prognostic factor (both P<0.05). The prognostic nomogram integrating CDCA7 expression level and clinicopathologic variables was constructed to predict 1-, 3- and 5-year OS. GSEA indicated that high CDCA7 expression was related to the apoptosis pathway, cell cycle pathway, JAK-STAT pathway, NOD like receptor pathway, P53 pathway, T cell receptor pathway and toll like receptor pathway, etc. As for immunity, CDCA7 was significantly associated with tumor mutational burden (TMB), immune checkpoint molecules, tumor microenvironment and immune infiltration.ConclusionsCDCA7 could serve as an independent prognostic factor for ccRCC and it was closely related to immunity
A Uniﬁed Surrogate Loss Framework by Re-expression and Interpolation

Training Details The baseline loss is a 2-class cross-entropy loss. Our loss is formulated as described in Sec. 4.2. The h function is the sigmoid relaxation of the binary variables. The g function is the interpolation over anchors of the binary variables. For example, the anchor that gives the best performance is that all positive examples have higher scores than negative examples—all bi,j = 1. Following Sec. 3.4, three types of anchors are generated randomly. The good anchors and nearby anchors are obtained by flipping one binary bit from the best anchor and the anchor at the current training step respectively.We sample 16 anchors for each type. We train models with the baseline loss and UniLoss with SGD using the same training schedule: with a fixed learning rate of 0.01 for 30 epochs.
Quantum Clustering Analysis: Minima of the Potential Energy Function

Quantum clustering (QC), is a data clustering algorithm based on quantum mechanics which is accomplished by substituting each point in a given dataset with a Gaussian. The width of the Gaussian is a 𝜎 value, a hyper-parameter which can be manually defined and manipulated to suit the application. Numerical methods are used to find all the minima of the quantum potential as they correspond to cluster centers. Herein, we investigate the mathematical task of expressing and finding all the roots of the exponential polynomial corresponding to the minima of a two-dimensional quantum potential. This is an outstanding task because normally such expressions are impossible to solve analytically. However, we prove that if the points are all included in a square region of size 𝜎, there is only one minimum. This bound is not only useful in the number of solutions to look for, by numerical means, it allows to to propose a new numerical approach “per block”. This technique decreases the number of particles (or samples) by approximating some groups of particles to weighted particles. These findings are not only useful to the quantum clustering problem but also for the exponential polynomials encountered in quantum chemistry, Solid-state Physics and other applications.
RELIABILITY CRITERIA FOR RE-ENGINEERING OF LARGE-SCALE PRESSURIZED IRRIGATION SYSTEMS

A study was conducted in a pressurized irrigation district in southern Italy to analyze current delivery performance and determine improvements needed to meet current and future delivery needs. Such an analysis is required due to changes that have occurred, since the system was first put into service, in cropping patterns, farming practices and irrigation techniques. The Combined Optimization and Performance Analysis Model (COPAM) was used to evaluate the irrigation system present performance under different operating conditions, to identify the areas within the irrigation district where rehabilitation and modernization are more urgently needed, and to suggest the most effective engineering and operational improvements. Post-intervention operating scenarios were simulated and analyzed to refine and validate the re-engineering process. Results show the usefulness of simulation models when analyzing modernization alternatives for irrigation schemes.
Enhanced Quadratic V/f-Based Induction Motor Control of Solar Water Pumping System

In rural and remote areas, solar photovoltaic energy (PV) water pumping systems (SPWPSs) are being favored over diesel-powered water pumping due to environmental and economic considerations. PV is a clean source of electric energy offering low operational and maintenance cost. However, the direct-coupled SPWPS requires inventive solutions to improve the system’s efficiency under solar power variations while producing the required amount of pumped water concurrently. This paper introduces a new quadratic V/f (Q V/f) control method to drive an induction motor powered directly from a solar PV source using a two-stage power converter without storage batteries. Conventional controllers usually employ linear V/f control, where the reference motor speed is derived from the PV input power and the dc-link voltage error using a simple proportional–integral (PI) controller. The proposed Q V/f-based system is compared with the conventional linear V/f control using a simulation case study under different operating conditions. The proposed controller expectedly enhances the system output power and efficiency, particularly under low levels of solar irradiance. Some alternative controllers rather than the simple PI controller are also investigated in an attempt to improve the system dynamics as well as the water flow output. An experimental prototype system is used to validate the proposed Q V/f under diverse operating conditions.
Cell Segmentation with the U-Net Convolutional Network

In biomedical research, electron microscopy images are required to investigate the detailed structure of tissues, cells, organelles and macromolecular complexes. Modern electron microscopy is used to obtain high resolution images of biological and non-biological specimens. Key regions need to be identiﬁed, to support clinical analysis and medical interventions. This process is sophisticated and tedious for human experts. To reduce the required amount of human labor by semiauto-mated systems, state-of-the-art methods use supervised learning with deep neural networks. In this work, the convolutional neural network u-net is reimplemented in PyTorch. Human expert labeling, for cell segmentation, is time consuming and demanding. To overcome the assumption of extensive manual labeling for training, we propose a new data augmentation method. The ISBI 2012 challenge is used to evaluate and compare our novel approach with two di ↵ erent elastic transformation methods. Furthermore, the use of di ↵ erent loss functions for the proposed network is outlined. Experiments show the beneﬁt of the used data augmentations. Our proposed network achieves rank 24 of 143 participants, while the original u-net ranks at place 44. The di ↵ erence at the foreground-restricted rand score value, between our proposed network and the ﬁrst place at the ISBI 2012 challenge, is less than a value of 0.011.
INFLUENCE OF BACTERIA ON THE MYCELIAL GROWTH AND BIOLOGICAL EFFICIENCY OF OYSTER MUSHROOM PLEUROTUS FLORIDA PF05

Oyster mushroom Pleurotus florida was cultivated in poly bags employing wheat straw as substrate. The effect of bacterial culture (BC) and culture filterate (CF) of a bacterial isolate (Glutamicibacter arilaitensis MRC119) was investigated on the growth rate, ACC deaminase, fruitbodies and the biological efficiency of oyster mushroom. The growth rate of mycelia was faster when treated with culture filtrate and bacterial culture in comparision to untreated control. The fastest radial and lineal growth rate achieved were as 8.97 mm/d, 8.11 mm/d respectively with the th treatments of CF and BC on 20 day, while control showed 4.98 mm/d. The highest ACC-deaminase (687.76 IU/g) production with CF while BC showed as 513.67 IU/g. However, the highest number of fruiting body were recorded as 41.32 and 32.23 with CF and BC treatments, which were higher than control (17.32) sets. The treatment with CF resulted highest yield of biological efficiency (256.45%) followed by 204.65% with BC and 154.87% of control. Therefore, the use of mushroom growth promoting bacterial culture and its culture is recommended for better biological efficiencies.
Genomic Profiling of Small Superficial Non-Ampullary Duodenal Epithelial Tumors

Introduction The mechanism underlying carcinogenesis and the genomic features of superficial non-ampullary duodenal epithelial tumors (SNADETs) have not been elucidated in detail. In this study, we examined the genomic features of incipient SNADETs, such as small lesions resected via endoscopic treatment, using next-generation sequencing (NGS). Methods Twenty consecutive patients who underwent endoscopic treatment for SNADETs of less than 20 mm between January 2017 and December 2017 were enrolled. Targeted genomic sequencing was performed through NGS using a 160 cancer-related gene panel. We examined the alteration/mutation frequencies in SNADETs. Results The maximum size of the SNADETs examined in this study was 12 mm in diameter. Five SNADETs were classified as low grade dysplasia (LGD) tumors, while 14 SNADETs were classified as high grade dysplasia tumors. Only one carcinoma-in-situ tumor was detected. We obtained NGS data for 16 samples. APC alterations were detected in 81% of samples (13/16). KRAS, BRAF, and TP53 alterations were detected in 25% (4/16), 18.8% (3/16), and 6.3% (1/16) of cases, respectively. Conclusions We detected APC alterations in most small SNADETs resected via endoscopic treatment, from LGD to carcinoma samples. Even in SNADETs classified as small LGD, KRAS and BRAF alterations were present in a few samples.
Ideology in Vicarious Learning–Related Communication

Organizations often learn vicariously by observing what other organizations do. Our study examines vicarious learning–related communication through which individuals share their observations with other organizational members. Most students and members of present-day organizations would expect that this communication is driven by a prodevelopment logic—that communication serves the purpose of organizational improvement and competitiveness. Our unique historical evidence on learning-related communication over multiple decades shows that the subjective and collective attitude toward prodevelopment communication may be ideologically conditioned. Prodevelopment communication is the norm in capitalist organizations, but competing ideologies may emphasize other goals higher than organizational development. Consequently, increasing challenges to capitalism as the ideological basis of economic organization can have deep impacts on how organizations learn and produce innovations in the future.
Hydrothermal Synthesis of Perovskite Bismuth Ferrite Micro/Nano Powders

Bismuth ferrite powders with different morphologies have been successfully synthesized via a facile hydrothermal route based on the reaction between BiCl3, FeCl3, and KOH. The products were characterized by X-ray diffraction, Scanning electron microscopy, Fourier transform infrared spectrometry and thermogravimetry and differential scanning calorimetry. The results show that the BiFeO3 powders are well-crystallized with rhombohedral phase. The morphologies of the final products are obviously influenced by KOH amount and surfactant. FT-IR spectra results show characteristic vibrations of the obtained BiFeO3 samples. TG-DSC results indicate the Curie temperature of the obtained BiFeO3 is 830°C.
Running head: MT FOR STROKE REHABILITATION NORTHERN ILLINOIS UNIVERSITY Using Mirror Therapy for Stroke Rehabilitation A Capstone Submitted to the University Honors Program In Partial Fulfillment of the Requirements of the Baccalaureate Degree

In the United States, strokes are known to kill over 140,000 people every year and effect mobility in more than 50 percent of stroke survivors (Centers for Disease Control and Prevention, 2020). It is a debilitating disease that befalls when blood vessels transporting nutrients to the brain are disrupted by a blood-clot or rupture of a vessel. While many rehabilitative therapies are available to those who have suffered from a stroke, there is one therapy that may benefit stroke survivors more than just conventional therapies. Mirror therapy (MT), a therapy that utilizes a mirror box to create an optical illusion, may be one way to help survivors to recover their lost motor functions. MT can aid rehabilitation by using a mirror to produce a reflection of a person’s unaffected limb in place of the affected limb when performing tasks. When the unaffected extremity is shifted, the mirror image allows the brain to believe that the affected limb is moving. These optical illusions allow the patients to feel as if their two extremities are moving symmetrically. This literature review focuses on how mirror therapy is utilized in practice today and if it should be incorporated as a conventional therapy for stroke rehabilitation. Peer reviewed journal articles have been dissected from the databases CINAHL Complete and MEDLINE. Through the review process, it was revealed that while mirror therapy on its own did not show any significant changes in overall motor functionality, mirror therapy in combination with conventional therapy has the potential to have statistically significant results.
PREDICTING TRUST IN A SOCIAL NETWORK BASED ON STRUCTURAL SIMILARITIES USING A MULTI-LAYERED PERCEPTRON NEURAL NETWORK

Although research on social networks is progressing rapidly, the positive and negative effects of this area should be evaluated. One of the problems is that social networks are very broad and anyone can have influence on them. This matter can cause the issue of people with different beliefs. Therefore, determining the amount of trust to various resources on social networks, and especially resources for which there is no previous history on the web, is one of the main challenges in this field. In this paper, we present a method for predicting trust in a social network by structural similarities through the neural network. In this method, the web of trust data set is converted to a structural similarity data set based on the similarity of the trustors and trustees first. Then, on the created data set, a part of the data set is considered as the training data and it is trained based on the multilayer perceptron neural network and then the trained neural network is tested based on the test data. In the proposed method, the MSE value is less than 0.01, which has improved more than 0.02 compared to previous methods. Based on the obtained results, the proposed method has provided acceptable accuracy. ABSTRAK: Walaupun kajian tentang rangkaian sosial adalah sangat pesat, kesan positif dan negatif dalam ruang lingkup ini perlu dinilai. Masalah rangkaian sosial adalah sangat luas dan sesiapa sahaja boleh terpengaruh. Perkara ini akan menyebabkan manusia dengan pelbagai isu kepercayaan. Oleh itu, menentukan nilai kepercayaan melalui pelbagai sumber dalam rangkaian sosial, terutama sumber-sumber yang tidak mempunyai sejarah lepas dalam web, adalah salah satu cabaran dalam bidang ini. Kajian ini membentangkan jangkaan kepercayaan dalam rangkaian sosial melalui persamaan struktur dengan menggunakan rangkaian neural. Kaedah ini ditentukan dengan menukar set data web kepercayaan kepada struktur set data hampir sama berdasarkan kesamaan pemegang dan pemberi amanah. Kemudian, sebilangan set data yang telah dibina ini dipertimbangkan sebagai data latihan dan ia dilatih berdasarkan rangkaian neural perseptron berbagai lapisan dan kemudian rangkaian neural yang terlatih ini diuji berdasarkan data ujian. Dalam kaedah yang dicadangkan ini, nilai MSE adalah kurang daripada 0.01, di mana telah diperbaiki kepada 0.02 lebih daripada kaedah-kaedah sebelum ini. Berdasarkan dapatan kajian, didapati kaedah yang dicadangkan ini menunjukkan ketepatan yang boleh diterima.
Avian thermoregulation in the heat: evaporative cooling capacity in an archetypal desert specialist, Burchell's sandgrouse (<italic>Pterocles burchelli</italic>)

Sandgrouse (Pterocliformes) are quintessential examples of avian adaptation to desert environments, but relatively little is known about the limits to their heat tolerance and evaporative cooling capacity. We predicted that evaporative cooling in Burchell’s sandgrouse (Pterocles burchelli) is highly efficient and provides the basis for tolerance of very high air temperature (Ta). We measured body temperature (Tb), resting metabolic rate (RMR) and evaporative water loss (EWL) at Ta between 25°C and ∼58°C in birds exposed to successive increments in Ta. Normothermic Tb averaged 39.0°C, lower than typical avian values. At Ta>34.5°C, Tb increased linearly to a maximum of 43.6°C at Ta=56°C. The upper critical limit of thermoneutrality (Tuc) was Ta=43.8°C, closely coinciding with the onset of panting and gular flutter. Above the Tuc, RMR increased 2.5fold to 2.89 W at Ta=56°C, a fractional increase far exceeding that of many other species under comparable conditions. Rates of EWL increased rapidly at Ta>42.9°C to 7.84±0.90 g h −1 at Ta=56°C, an 11fold increase above minimal levels. Maximum evaporative cooling efficiency (ratio of evaporative heat loss to metabolic heat production) was 2.03, but could be as high as 2.70 if our assumption that the birds weremetabolising lipids is incorrect. Thermoregulation at very high Ta inP. burchelliwas characterised by large increases in RMR and EWL, and is much less efficient than in taxa such as columbids and