Miracle-dz/UniFAR-FTUs huggingface dataset:
Peer Rejection and Internalizing Behavior: The Mediating Role of Peer Victimization in Preschool

ABSTRACT The author examined the relationship among peer rejection, peer victimization, and internalizing behaviors. The author hypothesized that physical and relational victimization would have a different indirect effect on the relationship between peer rejection and internalizing behaviors. Participants were 94 preschool children (37 girls; average age 49.97 months) from two university preschools located in the northern part of the United States. The results indicated that internalizing behaviors predicted the mediating variables only regarding relational victimization. Relational victimization indirectly affected the association between peer rejection and internalizing behaviors. The study provides evidence of the mediating effect of victimization behaviors on the relationship among peer rejection, victimization, and internalizing behaviors.
Hierarchical Task Generalization with Neural Programs

In this work, we propose a novel modeling framework for hierarchical tasks called Neural Task Programming (NTP). We bridge the idea of meta-learning and neural programming. NTP takes as input a task specification and recursively decomposes it into finer subtask specifications. These specifications are fed to a hierarchical neural program, where bottomlevel programs are callable subroutines to interact with the environment. We validate our method in two robot manipulation tasks. NTP achieves strong generalization results across sequential tasks that exhibit a hierarchal and compositional structure. The experimental results show that NTP learns to generalize well towards unseen tasks with increasing lengths, variable topologies, and changing objectives.
The protective effects of naringin against 5-fluorouracil-induced hepatotoxicity and nephrotoxicity in rats

Objective(s): 5-fluorouracil-induced (5-FU), an anticarcinogenic agent, is reported to have side-effects that include hepatotoxicity and nephrotoxicity. The study objective was to investigate the protective effects of naringin on 5-FU-induced hepatotoxicity and nephrotoxicity. Materials and Methods: Thirty rodents were assigned to three groups. The control group received 1 ml of intragastric distilled water for 14 days. The 5-FU group received 1 ml of distilled water for 14 days as a placebo. On day 9, this same group received a 20 mg/kg dose of 5-FU administered intraperitoneally(IP) for a further five days. The naringin+5-FU group received a 100 mg/kg dose of naringin (IP) for 14 days. On day 9, 20 mg/kg of 5-FU was administered (IP) to this group for a further five days. On day 15, the rats were decapitated, and blood and renal and hepatic tissues were taken. Results: It was determined that serum creatinine, BUN, AST, ALT, ALP, and LDH levels, as well as cytokine levels in the liver and kidney tissues were significantly elevated in the 5-FU group, compared to the control group. The comparative values were similar in the control and naringin+5-FU groups. When the liver tissue was examined histopathologically, in the control group it was found to be normal in structure. However, necrosis was observed in the hepatocytes of the pericentric region in the 5-FU group. 8-OHdG cell density was significantly elevated in the 5-FU group, compared to the control and naringin+5-FU groups. Conclusion: Naringin was observed to have a protective effect on 5-FU-induced liver and kidney damage.
Emission Spectroscopy and Ab Initio Calculations for TaN

The emission spectra of TaN have been investigated in the region 3000–35 000 cm−1 using a Fourier transform spectrometer. The spectra were observed in a tantalum hollow-cathode lamp by discharging a mixture of 1.5 Torr of Ne and about 6 mTorr of N2. In addition to previously known bands, numerous additional bands were observed and assigned to a number of new transitions. The spectroscopic properties of the low-lying electronic states of TaN were also predicted by ab initio calculations. A 1 + state, with equilibrium constants of Be = 0.457 852 1(48) cm−1, αe = 0.002 235 9(67) cm−1, and Re = 1.683 099 9(88) Å, has been identified as the ground state of TaN based on our experimental observations supported by the ab initio results. The first excited state has been identified as the a3 1 spin component at 2827 cm−1 above the ground state. To higher energies, the states become difficult to assign because of their Hund’s case (c) behavior and extensive interactions between the spin components of the electronic terms. C © 2002 Elsevier Science (USA)
Regret Bounds for Robust Adaptive Control of the Linear Quadratic Regulator

We consider adaptive control of the Linear Quadratic Regulator (LQR), where an unknown linear system is controlled subject to quadratic costs. Leveraging recent developments in the estimation of linear systems and in robust controller synthesis, we present the first provably polynomial time algorithm that provides high probability guarantees of sub-linear regret on this problem. We further study the interplay between regret minimization and parameter estimation by proving a lower bound on the expected regret in terms of the exploration schedule used by any algorithm. Finally, we conduct a numerical study comparing our robust adaptive algorithm to other methods from the adaptive LQR literature, and demonstrate the flexibility of our proposed method by extending it to a demand forecasting problem subject to state constraints.
Predictive Local Smoothness for Stochastic Gradient Methods

Stochastic gradient methods are dominant in nonconvex optimization especially for deep models but have low asymptotical convergence due to the fixed smoothness. To address this problem, we propose a simple yet effective method for improving stochastic gradient methods named predictive local smoothness (PLS). First, we create a convergence condition to build a learning rate which varies adaptively with local smoothness. Second, the local smoothness can be predicted by the latest gradients. Third, we use the adaptive learning rate to update the stochastic gradients for exploring linear convergence rates. By applying the PLS method, we implement new variants of three popular algorithms: PLS-stochastic gradient descent (PLS-SGD), PLS-accelerated SGD (PLS-AccSGD), and PLS-AMSGrad. Moreover, we provide much simpler proofs to ensure their linear convergence. Empirical results show that the variants have better performance gains than the popular algorithms, such as, faster convergence and alleviating explosion and vanish of gradients.
R-PackDroid: Practical On-Device Detection of Android Ransomware

Ransomware constitutes a major threat for the Android operating system. It can either lock or encrypt the target devices, and victims may be forced to pay ransoms to restore their data. Despite previous works on malware detection, little has been done to specifically identify Android malware as ransomware. This is crucial, as ransomware requires immediate countermeasures to avoid data being entirely compromised. In this paper, we propose R-PackDroid, a machine learning-based application (which directly runs on Android phones) for the detection of Android ransomware. R-PackDroid is a lightweight approach that leverages a methodology based on extracting information from system API packages. We demonstrate its effectiveness by testing it on a wide number of legitimate, malicious and ransomware-based applications. Our analyses pointed out three major results: first, R-PackDroid can distinguish ransomware from malware and legitimate applications with very high accuracy; second, R-PackDroid guarantees resilience against heavy obfuscation attempts, such as class encryption; third, R-PackDroid can be used to effectively predict and detect novel ransomware samples that are released after the ones used to train the system. R-Packdroid is available on the Google Play Store, and it is the first, academic ransomware-oriented detector available for Android.
The Limitations of Video-Based Telemedicine Systems

We conducted an interview and observational study of current video-based telemedicine health consultations to understand how they are used by patients and health providers and what challenges, if any, exist. Our results reveal challenges relating to four main themes: privacy and awareness, body language, emotional expression, and playfulness and humor. Patients and providers experienced issues in each of these areas where pertinent content was not visible over the video link or patients did not know who all could see it. Overall, these findings illustrate the need to consider design suggestions from across the literature on workplace media spaces and domestic video usage, while also considering the unique context of health consultations.
Words in context: a reference perspective on the lexicon

In this paper, we present a rich contextual perspective on the lexicon and background knowledge for the purpose of deep semantic parsing. In the project Understanding Language By machine1, we address various aspects of semantics in relation to i. ) reference to entities and event instances, ii. ) modeling of author and reader perspectives. Lexical resources and even resources with world-knowledge such as Wikipedia do not provide the episodic knowledge that is needed to determine reference and eventually meaning. Most resources and also the Natural Language Processing that uses these resources focus too much on semantic knowledge and local context. We argue that we need richer and more complex context models that integrate episodic knowledge, discourse structure and reader/writer perspectives to be able to correctly process text. We outline the directions of research that our project follows and the different aspects that we will study.
Comparative Nutritive Values of Palm Saps Before and after Their Partial Fermentation and Effective Use of Wild Date (Phoenix sylvestris Roxb.) Sap in Treatment of Anemia

Palmyra (Borassus f labellifer L.), Wild dat e (Phoenix sy lvestris Roxb.) and Coconut (Cocos nucifera L.) sap were analyzed before and after 24 hrs partial natural fermentation. All these three species of p alm saps sho wed high nutritive values. Total suga rs, Ca F e, Z n, C u, P , niacin w ere highest in fre sh Date sap followed by m ale Palmyra which was richest in vitamin-A and Coconut wa s ric hest in Na and K content. Thiamin, riboflavin a nd niacin showed dramatic increase in fermented Date and male Palmyra s ap wi th n o s ignificant change i n mi cronutrients. Here we first time dem onstrated t hat balanced administration of f resh and fermented wild date sap significantly improve condition of he moglobin deficient male anemic p atients.
A Gaussian Kernel Approach for Location Recommendations

The Location Based Social Networks (LBSN) are systems where the user can share the locations he/she has visited with his/her friends. These systems have become very popular because of the increasing number of users with smartphones equipped with GPS, which allow them to be connected to the internet wherever they are and geotag their current locations. In this context, the task to recommend new places for the user to visit is very important in order to improve the user experience while using the system. Several recommendation models have been proposed to solve this problem. In all these works, the nal model is a combination of several specialized recommender algorithms, being the most recurrent ones based on social networks, collaborative ltering, and geographic information. Although geographic information is a key dimension in LBSNs, there is no work so far providing an empirical comparison between the existing recommendation algorithms solely based on geographic information. In this paper, besides providing this comparison, we propose a new geographic-aware recommendation algorithm based on a gaussian kernel to infer the regions more likely to be visited by the user. We conduct experiments on real world datasets of two popular LBSNs: Gowalla and Foursquare. Our experiments show that our approach outperforms the state-of-the-art geographic-aware recommenders proposed by the literature.
Reinforcement learning applied to RTS games

Reinforcement learning algorithms are often used to compute agents capable of acting in environments without any prior knowledge. However, these algorithms struggle to converge in environments with large branching factors and their large resulting state-spaces. In this paper, we develop an approach to compress the number of entries in a Q-value table using a deep auto-encoder. We develop a set of techniques to mitigate the large branching factor problem. We apply such techniques in the scenario of a Real-Time Strategy (RTS) game, where both state space and branching factor are a problem. We empirically evaluate an implementation of the technique to control agents in an RTS game scenario where classical reinforcement learning fails and point towards future work.
Swift Development of State-of-the-Art Taggers for Portuguese

The application of general-purpose machine learning techniques to natural language part-of-speech tagging has matured to a point where it is now quite rapid to develop new taggers. In the present paper, we report on solutions we adopted for the specific issues that arise when developing a new automatic tagger for Portuguese and are generic enough to be further reused to develop other new taggers for this language, possibly by using other training data
Relative and Combined Prognostic Importance of On-Treatment Mean and Visit-to-Visit Blood Pressure Variability in ONTARGET and TRANSCEND Patients

In 28 790 patients recruited for the ONTARGET (Ongoing Treatment Alone and in Combination With Ramipril Global End Point Trials) and TRANSCEND (Telmisartan Randomized Assessment Study in ACE Intolerant Subjects With Cardiovascular Disease) trials, we investigated the prognostic value for cardiovascular events (primary outcome) of (1)on-treatment visit-to-visit systolic blood pressure (SBP) variability versus mean SBP and (2) the 2 measures together. SBP variability was measured by the coefficient of variation (CV) of mean SBP to which it was unrelated. Confounders such as variable time and number of visits from which to calculate SBP-CV were avoided by using the same number of visits at identical times in all patients. The covariate-adjusted risk of the primary outcome (Cox models) increased as SBP-CV or mean on-treatment quintile SBP increased, but only for mean on-treatment SBP, the relationship achieved statistical significance: global test for trend, P=0.12 versus P<0.0001. SBP-CV showed a relationship with fatal events, but it was unrelated to the risk of myocardial infarction and stroke, which were predicted by on-treatment mean SBP. Prediction of the primary outcome improved by the combined use of both measures: global test for trend, P<0.0001; hazard ratio for combined fifth versus first quintile, 1.42 (1.20–1.68) compared with 1.13 (1.01–1.27) for SBP-CV and 1.24 (1.11–1.40) for mean SBP. Thus, in the present study, on-treatment mean SBP provided an overall better prediction of cardiovascular risk than visit-to-visit SBP-CV. Prediction improved by their combined use, which may thus offer a more precise estimate of the protective effect of treatment. Clinical Trial Registration— URL: http//www.clinicaltrial. gov. Unique identifier: NCT153.101.
Pinch-Off Effect in P-Type Double Gate and Single Gate Junctionless Silicon Nanowire Transistor Fabricated by Atomic Force Microscopy Nanolithography

The spark of aggressive scaling of transistors was started after the Moors law on prediction of device dimensions. Recently, among the several types of transistors, junctionless transistors were considered as one of the promising alternative for new generation of nanotransistors. In this work, we investigate the pinch-off effect in double gate and single gate junctionless lateral gate transistors. The transistors are fabricated on lightly doped (1015) p-type Silicon-on-insulator wafer by using an atomic force microscopy nanolithography technique. The transistors are normally on state devices and working in depletion mode. The behavior of the devices confirms the normal behavior of the junctionless transistors. The pinch-off effect appears at VG +2.0 V and VG +2.5 V for fabricated double gate and single structure, respectively. On state current is in the order of 10-9 (A) for both structures due to low doping concentration. The single gate and double gate devices exhibit an Ion/Ioff of approximately 105 and 106, respectively.
Phytochemical Evaluation, Antioxidant Activity and Toxicity of Paeonia daurica ssp. macrophylla Root

Background and objectives: Paeonia daurica ssp. macrophylla, is an herbaceous and perennial plant which belongs to Paeoniaceae family. Two species of this plant grow in northern parts of Iran. The roots in powder form have been used in Persian traditional medicine for treatment of epilepsy, nightmares and gynecological diseases. Several biological activities such as antioxidant and anti-tumor effects of Paeonia species have been reported. Methods: methanol-water (80-20) extract (total extract) was fractionated using n-hexane, chloroform and ethyl acetate. Antioxidant activity of the total extract and fractions were evaluated using DPPH and FRAP assays. Total phenolics content of the extracts was determined by Folin-Ciocalteu method. In addition, cytotoxic activity of the fractions was determined against brine shrimp larvae. Column chromatography with normal phase silica gel and preparative TLC were also used for the isolation and purification of compounds. Results: Evaluation of the results indicated that the ethyl acetate and chloroform fractions with IC50 values of 16.55, 23.9 µg/mL, respectively showed potent radical scavenging activity. As well, the ethyl acetate and chloroform fractions indicated the highest antioxidant power by FRAP assay. Due to the potent antioxidant activity, the chloroform fraction was chosen for further investigations. Three compounds were identified as benzoic acid, veratric acid and oleanolic acid by different spectroscopic methods. Conclusion: According to our findings in this study, the root of Paeonia daurica ssp. macrophyla has beneficial antioxidant activity without toxicity and the therapeutic use of this plant in traditional medicine can be somewhat justifiable.
Personality characteristics below facets: A replication and meta-analysis of cross-rater agreement, rank-order stability, heritability, and utility of personality nuances.

Mõttus and colleagues (2017) reported evidence that the unique variance in specific personality characteristics captured by single descriptive items often displayed trait-like properties of cross-rater agreement, rank-order stability, and heritability. They suggested that the personality hierarchy should be extended below facets to incorporate these specific characteristics, called personality nuances. The present study attempted to replicate these findings, employing data from 6,287 individuals from 6 countries (Australia, Canada, Czech Republic, Denmark, Japan, and United States). The same personality measure-240-item Revised NEO Personality Inventory-and statistical procedures were used. The present findings closely replicated the original results. When the original and current results were meta-analyzed, the unique variance of nearly all items (i.e., items' scores residualized for all broader personality traits) showed statistically significant cross-rater agreement (median = .12) and rank-order stability over an average of 12 years (median = .24), and the unique variance of the majority of items had a significant heritable component (median = .14). These 3 item properties were intercorrelated, suggesting that items systematically differed in the degree of reflecting valid unique variance. Also, associations of items' unique variance with age, gender, and body mass index (BMI) replicated across samples and tracked with the original findings. Moreover, associations between item residuals and BMI obtained from one group of people allowed for a significant incremental prediction of BMI in an independent sample. Overall, these findings reinforce the hypotheses that nuances constitute the building blocks of the personality trait hierarchy, their properties are robust and they can be useful. (PsycINFO Database Record
An Assessment Of Universal Dependency Annotation Guidelines For Turkic Languages

Annotated corpora of three Turkic languages — Turkish, Kazakh, and Uyghur — were released as part of version 2 of the Free/Open-Source Universal Dependencies (UD) syntactic and morphological annotation guidelines. The objective of these guidelines is to provide consistent dependency annotation to facilitate cross-linguistic comparison. This paper presents the current state of each of the three UD-annotated Turkic corpora, along with an evaluation of the performance of parsers trained on these corpora. Overall, the UD annotation guidelines for Turkish, Kazakh, and Uyghur are fairly compatible — a testament to the careful design of the guidelines. However, the specific annotation guidelines for each of these languages were developed mostly independently; because of this, differences between the three standards exist. Moving forward with Turkic annotation standards in UD, attempts will be made to reconcile the differences. These differences are overviewed in this paper. Furthermore, a number of issues in annotation have arisen and have yet to be resolved. Some of these issues require further investigation of the phenomena, and some require consultation within the UD community to determine whether solutions may be determined based on similar phenomena in other languages. A number of these open issues are discussed, including tokenisation (how to deal with words that include an orthographic space, or multiple words that do not include an orthographic space), the difference between core and oblique arguments of verbs, complex predicates (including structures where there is a combination of a non-finite form which governs argument structure and contributes to TAM and a finite-form which contributes to TAM and takes person agreement), multiple derivation (multiple causative or causative–passive combinations), and use of copulas instead of auxiliaries in what appear to be auxiliary constructions.
CS231A - Group Activity Recognition

Group activity dynamics can be inferred from individual activity of each person in the scene. We build a deep learning based hierarchical model that learns to capture these dynamics using LSTM model. We train an LSTM model to first predict individual activity from CNN features and then pool over all individuals in a frame to train another LSTM to predict the group activity label. We evaluate this model on Collective Activity Dataset.