Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment of antidepressant-like effects of dextromethorphan on differential reinforcement of low-rate 72-s performance in rats

The effectiveness of ketamine for treatment-resistant depression along with several other clinical advantages, such as rapid onset and reduced adverse effects associated with serotonin transporter inhibition, has garnered interest in other similar acting psychedelics as novel antidepressant drugs. The antitussive dextromethorphan exhibits glutamate N-methyl-d-aspartate receptor antagonism, sigma-1 receptor agonism, and serotonin reuptake inhibition, which has exhibited antidepressant effects in limited human studies and animal models. The present study sought to further examine dextromethorphan using a differential reinforcement of low-rate 72-s schedule, which can be used to screen antidepressant drugs, in male and female rats. The tricyclic antidepressant drug imipramine and the psychostimulant d-amphetamine also were examined. Sex differences were not shown for baseline performance or for the drugs tested. Further, performance did not differ between the estrus and diestrus stages. Dextromethorphan alone and with quinidine produced an antidepressant-like effect by reducing the number of responses emitted, increasing the number of reinforcers earned, and shifting inter-response times to the right, although significant response suppression occurred at these doses. An antidepressant-like effect was shown with imipramine, but d-amphetamine increased the number of responses emitted and did not affect the number of reinforcers earned. The present findings provide additional support for antidepressant effects produced by dextromethorphan.
Craig’s Anti–Platonism, Lowe’s Universals, and Christ’s Penal Substitutionary Atonement

William Lane Craig has defended nominalism as a kind of “anti– Platonism. ” To him, Platonism is inimical to God’s aseity. More recently, he also has defended the penal substitution of Christ. However, he has not brought the two subjects into dialogue with each other. In this essay, I will attempt to do that by exploring the implications of two major types of nominalism, austere nominalism and trope theory, for the penal substitution. I will argue that nominalism will undermine the penal substitution of Christ. Instead, to try to preserve both his anti–Platonism and the penal substitution, a better alternative for Craig is to embrace E. J. Lowe’s immanent universals.
Two Stream Pose Guided Network for Vehicle Re-identification

Vehicle Re-Identification is the task of finding images of the same vehicle with different views across a surveillance camera network, which is a very beneficial yet challenging task. Huge intra-class differences and small inter-class difference makes this task hard to tackle. Appearance-based information is utilized in this paper to cope with vehicle re-identification problem; we have proposed a deep learning technique by incorporating poses of vehicles generated by pose estimation network and visual information. When query image is given, the two-stream network generates a feature embedding by concatenating pose feature from pose network. Extensive experiments are done on two of the benchmark datasets of vehicle re-identification VeRi-776 and VehicleID. Experimental results are supporting the competitiveness of the proposed method with recent state-of-the-art methods.
Clothing Image Retrieval Based on Parts Detection and Segmentation

With the rapid development of E-commerce, more and more users are buying clothes through the Internet, and "image search" for clothing images has become a popular research direction. The current "image search" technology mainly relies on the results of feature extraction of the whole image, but cannot focus on the parts of the clothing, and the background of the clothing image is generally complex, resulting in low accuracy of clothing image retrieval, so we propose a retrieval method based on clothing image detection and segmentation. Firstly, Mask R-CNN is used to detect and segment the image to get the information of garment body, collar parts, sleeve category and pocket positions, then VGG16 is used to extract 512-dimensional features from the garment body and collar parts, based on this information, the similarity between the garment to be retrieved and the garment in the database is calculated one by one. We calculate the similarity by weighting the cosine similarity of 512-dimensional features of the garment body and collar, as well as the similarity of the sleeves and pockets. The search results are presented to the user according to the descending order of similarity. The experimental results show that the method can focus on the whole garment as well as their parts, thus enabling retrieval based on garment style. It also allows users to adjust the weights of each part and can return the search results that best meet their individual needs
Integration of Machine Learning with MEC for Intelligent Applications

∗ In recent years, telecom operators and large companies are eager to obtain value from the edge of the network, and the priority of cloud computing has been transferred from the center to the edge. In addition, with the comprehensive deployment of 5G base station (BS), the number of 5G users has been largely increased. For 5G users, they expect to have a better experience of high bandwidth and low latency. Thus, the Mobile Edge Computing (MEC) came into being. MEC brings the capability from the center to the edge of the mobile network. Requests and data of User equipment (UE) has been underlined in MEC. These requests and data will be analyzed and disposed at the edge without being uploaded to the cloud center, which diminishes the latency efficiently. Besides, with the help of machine learning, MEC can show a better performance. This paper is aimed at studying superiorities of MEC itself and integration of machine learning with MEC, and intelligent applications they will bring. This paper first discusses the concept and architecture of MEC, then the advantages of MEC are listed. Next, the improvements of integration of machine learning with MEC and the intelligent applications which employ these technologies will be introduced. Finally, the deficiencies and future research trend of MEC will be discussed. After that, conclusion can be drought that MEC augment the performance of speed, security and privacy, energy saving and reliability. Furthermore, integration of machine learning with MEC can provide better resource management and offloading decision.
In Situ Synthesis of Nitrogen- and Sulfur-Enriched Hierarchical Porous Carbon for High-Performance Supercapacitor

In this work, we present a simple and facile method for the nitrogen (N)- and sulfur (S)- doped porous three-dimensional (3D) spongelike carbon materials via direct pyrolysis of N and S containing polymer N,N′-methylene-bis-acrylamide cross-linked poly(acrylamide-co-2-acrylamido-2-methyl-1-propanesulfonic acid) at varying temperatures under inert atmosphere. The obtained nitrogen- and sulfur-doped porous carbons (NSPCs) possess 3D hierarchical porous structure and contain a significantly high amount of N and S species. The concurrent incorporation of N and S successfully modified the surface properties of carbon materials and lead to enhanced capacitive performance. The presented NSPC exhibits specific capacitance of 230 F g–1 at a current density of 1 A g–1 and showed excellent cycling stability, depicting a promising material for energy storage devices.
EFFECT OF SERIAL CAFFEINE MOUTH RINSE ON WINGATE ANAEROBIC PERFORMANCE

Caffeine has been shown to increase aerobic and anaerobic performance especially in elite level athletes. Recently, caffeine mouth rinsing without ingestion has had same ergogenic potential with ingestion in some performance parameters. The purpose of this study was to investigate serial rinsing of caffeine during the warm-up period before wingate anaerobic test (Want) performance. Ten physically active male completed the following 10-s mouth rinsing protocols; a-) %2 w/v caffeine solution (Caf) b-) water (Pla) c-) no rinse as a control (Con) with a randomised, double-blind, crossover and counterbalanced research design. Serial mouth rinse protocol was implemented (8x25 ml solutions at 30-s intervals during 5 minutes warm up). Following to last rinse, participants were asked to cycle 30s maximal sprint on a cycle ergometer. Peak and Mean power output were calculated via cycle ergometer software, also at rest and immediately after Want, heart rate (HR) and rating of perceived exertion (Rpe) were assessed. In comparison with Pla and Con, Caf trial had no significant effect on peak (p>0.05) and mean (p>0.05) power output, there were also no significant effects on heart rate and ratings of perceived exertion (p>0.05)
Evaluation of vermicompost fertilizer application on growth, nutrient uptake and photosynthetic pigments of lentil (Lens culinaris Medik.) under moisture deficiency conditions

ABSTRACT Vermicompost can play an effective role in plant growth and also in reducing harmful effects of various environmental stresses on plants due to its porous structure, high-water storage, having hormone-like substances and also high levels of macro and micro nutrients. Application of vermicompost in soil, especially at the levels of 15 and 25 Wt% significantly increased all studied traits under non-stress conditions. Under temperate and severe stress conditions, vermicompost at 25 Wt% treatment resulted in a significant increase in plant height (+10% +21%), number of pod (+44% +65%), root dry weight (+63% +66%), shoot dry weight (+50% +89%), leaf area (+6% +7%), root area (+65% +35%). The leaf calcium (Ca) (+62% +67%), leaf potassium (K) (+39% +45%) and root Ca (+33% +34%) under temperate and severe stress, increased using of 25% vermicompost. In conclusion, we suggested that application of vermicompost had a positive effect on lentil under moisture deficit stress.
Effective Axiomatizations of Simple Ordered Structures

Axiomatization in ﬁnite model theory is the process of ﬁnding a set of axiom formulas from which all other formulas in a theory can be derived. Applied to the theory of a structure like “binary strings” or “natural number arithmetic,” this allows us to ﬁnd the precise minimal mathematical deﬁnition of that structure. This has consequences on the computability of a structure; if a theory has a recursively enumerable axiomatization, then it is itself recursively enumerable. For example, the theory of binary strings can be axiomatized using just the axioms of a linear order, deﬁnable induction, and a maximal element. To do this, a technique called an “Ehrenfeucht-Fraisse game" is required, which shows that different models are equivalent for all formulas with at most n unique variables. However, the pigeonhole principle is logically equivalent to deﬁnable induction, so anything which can be derived from deﬁnable induction can be derived from the pigeonhole principle. Thus, it should be possible to axiomatize the theory of binary strings with the pigeonhole principle instead of deﬁnable induction, with interesting consequences and extensions as the pigeonhole principle (unlike induction) applies to sets without a linear order. For example, the same axiomatization technique can be applied to other ordered structures, like trees, which have a very different form of order.
Case Report: A giant fecaloma revealed by severe aspiration pneumonia and urinary retention [version 1; peer review: awaiting peer review]

Fecaloma is an accumulation of hardened impacted stool typically occurring in the sigmoid colon and rectum. It mainly affects elderly and bedridden patients suffering from chronic constipation and can be revealed by different signs. We report a case of 74-year-old female, with anorexia, Alzheimer’s disease, and chronic constipation, who was admitted to the emergency department with complaints of dyspnea and anuria. Clinical examination showed fever, Glasgow Coma Scale score of 13/15, tachycardia with a blood pressure of 100/50 mmHg, polypnea with hypoxia, foci of crepitant rales in pulmonary auscultation and a tender hypogastric mass with mild diffuse abdominal tenderness. Digital rectal examination revealed hard fecal material. Computed tomography (CT) images demonstrated bilateral pulmonary parenchymal condensation and a huge heterogeneous fecaloma in the sigmoid colon and rectum compressing the bladder. Based on these findings, the diagnosis of giant fecaloma causing aspiration pneumonia and urinary retention was retained. Manual disimpaction and bowel enemas were done but they were unsuccessful and surgical treatment was refused. Ultimately the patient died due to septic shock. Early diagnosis should be made to relieve symptoms and prevent complications.
Automatically Locating ARM Instructions Deviation between Real Devices and CPU Emulators

Emulator is widely used to build dynamic analysis frameworks due to its fine-grained tracing capability, full system monitoring functionality, and scalability of running on different operating systemsand architectures. However, whether the emulator is consistent with real devices is unknown. To understand this problem, we aim to automatically locate inconsistent instructions, which behave differently between emulators and real devices. We target ARM architecture, which provides machine readable specification. Based on the specification, we propose a test case generator by designing and implementing the first symbolic execution engine for ARM architecture specification language (ASL). We generate 2,774,649 representative instruction streams and conduct differential testing with these instruction streams between four ARM real devices in different architecture versions (i.e., ARMv5, ARMv6, ARMv7-a, and ARMv8-a) and the state-of-the-art emulators (i.e., QEMU). We locate 155,642 inconsistent instruction streams, which cover 30% of all instruction encodings and 47.8% of the instructions. We find undefined implementation in ARM manual and implementation bugs of QEMU are the major causes of inconsistencies. Furthermore, we discover four QEMU bugs, which are confirmed and patched by thedevelopers, covering 13 instruction encodings including the most commonly used ones (e.g.,STR,BLX). With the inconsistent instructions, we build three security applications and demonstrate thecapability of these instructions on detecting emulators, anti-emulation, and anti-fuzzing.
The Learning Loop for General Purpose AI

Performing seemingly mundane and straightforward household tasks is a 1 daunting challenge for today’s robotic systems. Successful execution of these tasks 2 requires commonsense knowledge. Instead of end-to-end training on a pre-defined 3 set of tasks we propose a framework for to create a never-ending learning loop 4 where the agent self-learns. Our proposal is based on the interplay of intrinsically 5 motivated exploration, model-building and social learning. The learning loop will 6 continuously accumulate knowledge in a manner that is amenable to long-term 7 planning and achieving new goals. 8
CMAR_A_300574 3181..3192

1Medical College of Soochow University, Suzhou, Jiangsu Province, 215123, People’s Republic of China; 2Department of Urology, The Second Affiliated Hospital of Nantong University, Nantong, Jiangsu Province, 226001, People’s Republic of China; 3Department of Urology, Xuzhou Central Hospital, Xuzhou Clinical College Affiliated to Xuzhou Medical University, Xuzhou, Jiangsu Province, 221009, People’s Republic of China Background: We conducted a two-center study to investigate the prognostic value of preoperative fibrinogen–albumin ratio (FAR) in patients undergoing radical cystectomy (RC). Methods: The clinical and survival data of 267 patients with bladder cancer (BCa) treated with RC were collected, of which 140 patients from Xuzhou Central Hospital were divided into training set and 127 patients from The Second Affiliated Hospital of Nantong University were divided into validation set. X-tile software was used to obtain the optimal cut-off values for preoperative platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR) and FAR. Receiver operating characteristic (ROC) curves were used to compare the predictive ability of PLR, NLR, FAR and modified Glasgow prognostic score (mGPS). Kaplan–Meier curves were used to assess overall survival (OS) and progression-free survival (PFS) of patients in different FAR groups. Univariate and multivariate Cox regression were used to assess patients’ independent risk factors, and R software was used to construct prognostic nomograms. Results: In the training set, the optimal cut-off values for PLR, NLR and FAR were 76.76, 3.97 and 0.08, respectively. Both in the training and validation sets, FAR had better ability to predict OS and PFS than PLR and NLR, and patients in the higher FAR group had worse OS and PFS. In the multivariate Cox regression analysis, FAR was an independent risk factor for OS [hazard ratio (HR) 3.569, 95% confidence interval (CI): 1.015–12.546, P=0.047] and PFS [HR 5.071, 95% CI: 1.394–18.451, P=0.014]. In addition, FAR-based prognostic nomograms had high predictive ability than TNM staging. Conclusion: Preoperative FAR is an independent prognostic factor for OS and PFS in BCa patients treated with RC, and a high FAR predicted a poor prognosis. In addition, a prognostic nomogram based on FAR can better predict individual survival.
The Knowledge and Educational Needs of Nurses Regarding Pain Management of Patients on Maintenance Hemodialysis: A Qualitative Study

Results: The five themes and fifteen sub-categories were extracted from the nurses' experiences regarding nurses’ knowledge of pain management. The five categories that emerged from the analysis were (1) nurses’ knowledge of pain management, (2) nurses’ self-learning pain management strategy, (3) nurses’ approach to pain management, (4) patients continue to suffer unrelieved pain, (5) nurses’ educational needs.
Randomness Bounds for Private Simultaneous Messages and Conditional Disclosure of Secrets

In cryptography, the private simultaneous messages (PSM) and conditional disclosure of secrets (CDS) are closely related fundamental primitives. We consider k -party PSM and CDS protocols for a function f with a common random string, where each party P i generates a message and sends it to a referee P 0 . We consider bounds for the optimal length ρ of the common random string among k parties (or, randomness complexity ) in PSM and CDS protocols with perfect and statistical privacy through combinatorial and entropic arguments. ( i ) We provide general connections from the optimal total length λ of the messages (or, communication complexity ) to the randomness complexity ρ . ( ii ) We also prove randomness lower bounds in PSM and CDS protocols for general functions. ( iii ) We further prove randomness lower bounds for several important explicit functions. They contain the following results: For PSM protocols with perfect privacy, we prove λ − 1 ≤ ρ ≤ λ as the general connection. To prove the upper bound, we provide a new technique of randomness sparsiﬁcation for perfect privacy, which would be of independent interest. From the general connection, we prove ρ ≥ 2 ( k − 1) n − 1 for a general function f : ( { 0 , 1 } n ) k → { 0 , 1 } under universal reconstruction, in which P 0 is independent of f . This implies that the Feige-Killian-Naor PSM protocol for a general function [Proc. STOC ’94, pp.554–563] is optimal with respect to randomness complexity. We also provide a randomness lower bound ρ > kn − 2 for a generalized inner product function. This implies the optimality of the 2-party PSM protocol for the inner-product function of Liu, Vaikuntanathan, and Wee [Proc. CRYPTO 2017, pp.758–790]. For CDS protocols with perfect privacy, we show ρ
Observation and modeling of the historic “Godzilla” African dust intrusion into the Caribbean Basin and the southern US in June 2020

Abstract. This study characterizes a massive African dust intrusion into the Caribbean Basin and southern US in June 2020, which is nicknamed the “Godzilla” dust plume, using a comprehensive set of satellite and ground-based observations (including MODIS, CALIOP, SEVIRI, AERONET, and EPA Air Quality network) and the NASA GEOS global aerosol transport model. The MODIS data record registered this massive dust intrusion event as the most intense episode over the past 2 decades. During this event, the aerosol optical depth (AOD) observed by AERONET and MODIS peaked at 3.5 off the coast of West Africa and 1.8 in the Caribbean Basin. CALIOP observations show that the top of the dust plume reached altitudes of 6–8 km in West Africa and descended to about 4 km altitude over the Caribbean Basin and 2 km over the US Gulf of Mexico coast. The dust intrusion event degraded the air quality in Puerto Rico to a hazardous level, with maximum daily PM10 concentration of 453 µg m−3 recorded on 23 June. The dust intrusion into the US raised the PM2.5 concentration on 27 June to a level exceeding the EPA air quality standard in about 40 % of the stations in the southern US. Satellite observations reveal that dust emissions from convection-generated haboobs and other sources in West Africa were large albeit not extreme on a daily basis. However, the anomalous strength and northern shift of the North Atlantic Subtropical High (NASH) together with the Azores low formed a closed circulation pattern that allowed for accumulation of the dust near the African coast for about 4 d. When the NASH was weakened and wandered back to the south, the dust outflow region was dominated by a strong African easterly jet that rapidly transported the accumulated dust from the coastal region toward the Caribbean Basin, resulting in the record-breaking African dust intrusion. In comparison to satellite observations, the GEOS model reproduced the MODIS observed tracks of the meandering dust plume well as it was carried by the wind systems. However, the model substantially underestimated dust emissions from haboobs and did not lift up enough dust to the middle troposphere for ensuing long-range transport. Consequently, the model largely missed the satellite-observed elevated dust plume along the cross-ocean track and underestimated the dust intrusion into the Caribbean Basin by a factor of more than 4. Modeling improvements need to focus on developing more realistic representations of moist convection, haboobs, and the vertical transport of dust.
Analysis of Distinct Feature Groups in the Credit Scoring Problem

Registration and financial data have been traditionally used for the credit scoring problem. However,slight improvements in the reliability of the scores positively impacts financial companies. Therefore, exploring newfeatures is a strategic task. This work analyzes the importance of new feature groups not commonly employed forthe credit scoring task and others already used. We categorized features from open credit scoring datasets, suchas German and Australian and compared their groups with the ones of a company dataset used in this work. Ourdataset contains unusual feature groups, such as historical, geolocation, web behavior, and demographic data. In ouranalyzes, we first conducted bivariate tests with each feature-pair to assess their individual importance. Secondly, weran XGBoost machine learning model with each feature group to evaluate each group importance. We also appliedfeature selection with binary Particle Swarm Optimization to assess the groups importance when combined. Next, weemployed correlation tests to find inner and inter-correlation among the features groups. Finally, we used the companydataset and employed AdaBoost, Multilayer Perceptron, and XGBoost algorithms to find the best model for the task.Some of our main findings were that the unusual features added a slight improvement to registration features. We alsodetected reasonable inner correlation among some feature groups and found that all groups were relevant for the taskwith the Historical Group as the most promising. Lastly, XGBoost obtained the best performance over AdaBoost andMultilayer-perceptron for the task.