Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Spoiler-Sensitive Video Preview Generation with Reinforcement Learning

: With the plethora of video content uploaded to the internet each day, the amount of watchable content far exceeds the amount of time available for us to consume it. That being said, the aim of this research is not to create something that does the watching for us and merely generates a short summary; instead, the goal is to help us select videos that we would enjoy watching the most while preserving the viewing-integrity of the original video. In other words, we wish to be able to generate preview video summaries that do not reveal any essential plot items. To accomplish this task, we developed an algorithm that is able to select a fraction of a video’s total frames to create a video trailer/summary while excluding important frames that would detract from the main viewing experience (i.e. it does not include any main plot points, spoilers, plot twists, etc.). This output video would allow us to decide better whether we are interested in a particular video, and at the same time will still allows us to fully enjoy the video if we do choose to watch it since we will have avoided any spoilers.
Induction in first order logic from noisy training examples and fixed example set sizes

This dissertation investigates the field of inductive logic programming (ILP) and in so doing an ILP system, LIME, is designed and developed. LIME addresses the problem of noisy training examples; learning from only positive, only negative, or both positive and negative examples; efficiently biasing and searching the hypothesis space; and handling recursion efficiently and effectively. The Q-heuristic is introduced to address the problem of learning with both noisy training examples and fixed numbers of positive and negative training examples. This heuristics is based on Bayes rule. Both a justification of its derivation and a description of the context in which it is appropriately applied are Oven. Because of the general nature of this heuristic its application is not restricted to ILP. Instead of employing a greedy covering approach to constructing clauses, LIME employs the Q-heuristic to evaluate entire logic program as hypotheses. To tame the inevitable explosion in the search space, the notion of a simple clause is introduced. These sets of literals may be viewed as subparts of clauses that are effectively independent in terms of variables used. Instead of growing a clause one literal at a time, LIME efficiently combines simple clauses to construct a set of gainful candidate clauses. Subsets of these candidate clauses are evaluated using the Q-heuristic to find the final hypothesis. Details of the algorithms and data structures of LIME are discussed. LIME's handling of recursive logic programs is also described. Experimental results are provided to illustrate how LIME achieves its design goals of better noise handling, learning from a fixed set of examples (eg., from only positive data), and of learning recursive logic programs. These results compare the performance of LIME with other leading ILP systems like FOIL and PROGOL in a variety of domains. Empirical results with a boosted version of LIME are also reported. The dissertation also makes an attempt to characterise the learning ability of the Q-heuristic. To this end, some preliminary results are presented to show that the Q-heuristic stochastically learns in the limit some restricted classes of concepts. This includes cases where the training set may contain: noise, only positive examples, or only negative examples.
MODELING THE PERSISTENCE OF CONDITIONAL VARIANCES: A COMMENT

ISSN: 0747-4938 (Print) 1532-4168 (Online) Journal homepage: https://www.tandfonline. com/loi/lecr20 Modeling The persistence Of Conditional Variances: A Comment Francis X. Dieobold To cite this article: Francis X. Dieobold (1986) Modeling The persistence Of Conditional Variances: A Comment, Econometric Reviews, 5:1, 51-56, DOI: 10.1080/07474938608800096 To link to this article: https://doi.org/10.1080/07474938608800096
Super resolution reconstruction based on L1-norm and orthogonal gradient operator

For the ill-posed problem of super resolution reconstruction,a new adaptive algorithm for image sequence was proposed.The new algorithm was based on the framework of L1-norm.In the new algorithm,the pyramidal algorithm coupled with Lucas-Kanade algorithm was used for images registration to obtain the sub-pixel motion estimation.Displacement operator was introduced to achieve the regular term based on the orthogonal gradient operator and the regularization parameter was determined adaptively.Finally,the steepest descent method was used to solve the minimum of the constraint equation.The simulation experiments and the true sequence experiments show that the method proposed has advantages over spline interpolation,Tikhonov reconstruction and bilateral total variation reconstruction.On the one hand it can provide better reconstructing results,on the other hand the reconstruction time is reduced at the same time since the regularization item is simple.
A new method for the design of interpolation filter using polynomial fit

This paper proposes a new method for the design of high-performance interpolation filter using polynomial fit.Different from the traditional method, it designs interpolation filter in temporal response instead of spectral response. It overcomes the disadvantage of exaustive search which requirs too much time to fred the optimal coefficients. In view of many coefficients or high precision, this method is advantageous especially.
ELLIPTIC CURVES OVER FUNCTION FIELDS AND THE PICARD NUMBER

Introduction. The purpose of this paper is to study the rank of the group of rational points on an elliptic curve defined over a function field and simultaneously the Picard number of the corresponding elliptic surface. The results utilize the general theory of elliptic surfaces due to Kodaira [5] and [6], and properties of the Gauss-Manin connexion associated with an elliptic surface (Stiller [15]). Let E be an elliptic surface having a global section over its base curve X. We assume throughout that the functional invariant J is non-constant and that E has no exceptional curves of the first kind in the fibres. We denote by NS(E) the Neron-Severi group of the surface E and by p the rank of NS(E) which is called the Picard number of E. Additionally, letting K(X) be the function field of X, we denote by Egen (K(X)) the group of K(X)-rational points on the generic fibre of E over X, whose rank we denote by r. We refer the reader to Shioda [14] for a description of NS(E) and its relation to the group Egen (K(X)). In particular recall
MODELING OF CONTROLLED DC MOTOR WITH A CHOPPER CIRCUIT

To design a control system for any system, it is required to model the system's dynamic behaviour. The modeling and analysis of these systems can be either numerical or analytical. This paper studies the analytical methods of modeling DC motors mainly continuous-time averaging technique and discrete-time approach. It shows that the discrete-time approach provides clear expressions representing the operation of DC motors and thus can help designers gain a better understanding of the performance of the motor. The results are shown using numerical simulations.
Evaluating Common Measures 2.0 in 4-H: Intra- and Interpersonal Skills Predict Engaged Citizenship

Research indicates that positive youth development (PYD) programming leads to improved developmental outcomes. Yet there continues to be a lack of clarity about how PYD programs are defined and measured. Therefore, Common Measures 2.0 was created for assessing 4-H youth development programs. We examined the universal skills (intrapersonal and interpersonal) and engaged citizenship scales of Common Measures 2.0 through a survey of 175 participants at a 4-H youth leadership conference. Intrapersonal and interpersonal skills were significantly positively associated with engaged citizenship. The universal skills and engaged citizenship scales showed acceptable factor loadings and Cronbach's alpha values. Limitations and future directions are discussed.
THERMAL BEHAVIOR OF ELECTROSPUN GELATIN AND CHITOSAN COMPLEX

This paper studied the thermal behavior of gelatin, chitosan, and their complex. Thermal stability was analyzed by thermogravimetric analysis, and the results showed that gelatin and chitosan raw materials exhibited two decomposition stages while as-electrospun nanofibrous mats had three decomposition stages. Moreover, chitosan raw material had better thermal stability than gelatin. However, its electrospun partner revealed its opposite trend. X-ray diffraction was used to analyze the crystalline property and the result showed that the crystallinity decreased due to the interaction with the solvents, and thus the thermal stability sharply decreased. In addition, incorporation of gelatin could improve the thermal stability of chitosan.
Transcriptomic analyses revealed detoxification genes responsive to solanine treatment in Phthorimaea operculella

Phthorimaea operculella attacks potatoes worldwide, causing great losses in potatoes' quantity and quality. P. operculella feed on potatoes with a relatively high concentration of steroidal glycoalkaloids called solanine, which potatoes produce as secondary metabolites. The potential genes involved in solanine‐induced response in P. operculella were identified by performing next‐generation sequencing and Illumina RNA‐seq analysis on solanine‐treated P. operculella to explore differentially expressed genes. Four major families of detoxification enzymes were identified, including 71 cytochrome P450 monooxygenases (CYPs), 22 glutathione S‐transferases (GSTs), 58 esterases (ESTs) and 16 UDP‐glucuronosyltransferases (UGTs). The Illumina RNA‐Seq data showed 2875 and 1625 differentially expressed genes (DEGs) from the P. operculella larvae after exposure to the LC25 and LC75 of solanine compared with the untreated controls. Furthermore, 21 upregulated DEGs, including 13 CYPs, 3 GSTs, 4 ESTs and 1 UGT, and 20 downregulated DEGs, including 5 CYPs, 3 GSTs, 9 ESTs and 3 UGTs, were detoxification genes that may be related to solanine‐induced response and needed to be further verified. The RT‐qPCR results among the six upregulated P450 unigenes after solanine treatment were consistent with RNA‐Seq data, confirming the accuracy and reproducibility of the gene expression profiles generated from RNA‐seq. This study provides new information about the detoxification genes involved in solanine‐induced response in P. operculella.
The concept map as a substitute for lectures: Effects on student performance and mental health

Abstract In comparison with traditional lectures, active learning methods have the potential to foster learner agency and consequently raise academic performance. However, these methods are often applied with little knowledge about their effectiveness. In this study, we introduced two concept maps in an undergraduate cell biology course, to investigate their effect on student performance, but also mental health, in a quasi-experimental setup. By implementing group tasks into the work, we aimed to reduce feelings of loneliness among the participants, a common phenomenon among students in the study country. We evaluated the effects of the concept maps via exam grades and in an anonymous student survey. Here, we found no increase in student performance after introduction of the active learning task. Students also did not report improvements in their feelings of loneliness. We conclude that this active learning method did not detectably improve student performance or mental health, a result which does not conform to the popular perception of positive effects from active learning. The survey revealed that students do not have a preference for either active learning or traditional lectures, however, the students experienced that the concept map helped them to describe, explain and hypothesize about the topics. Overall, this shows that the concept map method is a valid alternative to traditional lectures, since students perform equally between both learning approaches.
Assessment of Potent Phosphate-Solubilizing Bacteria Isolated from the Olive Tree Rhizosphere Grown on Phosphate Sludge and Their Effect on Common Bean Growth

Abstract One of the most restricting macronutrients for crop yield worldwide is phosphorus, which is often less than 1% of the total amount contained in soils. To promote its bioavailability, there are microorganisms considered vital that can solubilize and mineralize their pools in soils. In the current study, we assessed the potential of phosphate-solubilizing bacteria (PSB), isolated from the olive tree rhizosphere grown on phosphate sludge, to solubilize phosphate, promote plant growth, and tolerate abiotic stresses. Based on 16S rRNA gene sequencing, twenty-four PSB strains were identified and retained for in vitro analysis. According to the results, all the strains were able to solubilize the Moroccan rock phosphate, with soluble phosphate concentrations ranged from 5.34 µg/mL to 227 µg/mL. Furthermore, the majority of the strains are thermo-tolerant and halotolerant. Nonetheless, only five strains produced indole acetic acid. Regarding biocontrol potentialities, several PSB strains were characterized by producing hydrogen cyanide and hydrolytic enzymes (cellulase, and chitinase), of which three strains identified as Pseudomonas moraviensis, Bacillus cereus, and Bacillus aryabhattai, with a remarkable multi-trait combination were selected for antagonism and co-inoculation tests. The findings revealed that these PSB strains significantly inhibited Fusarium oxysporum (17.65%–62.35%) and Verticillium dahliae (52.35%–66.87%) and promoted common bean growth. The consortium of the three strains showed the best results by significantly increasing both plant height and tap root length and dry biomass compared to individual inoculation. The PSB selected from the olive tree rhizosphere growing on phosphate sludge have the potential to be useful as biofertilizer and biocontrol agents for attaining sustainable food crop production.
Transient force augmentation due to counter-rotating vortex ring pairs

Of particular significance to biological locomotion is vortex ring interaction. In the wakes of animals, this unsteady process determines the changes in the impulse of counter-rotating vortex ring pairs (VRPs), which consist of two vortex rings with opposite sense of rotation. In this paper, these VRPs are proposed to be of particular importance to unsteady force generation. We carry out numerical computations, simulating the piston–cylinder apparatus, to study the transient changes in the impulse of counter-rotating VRPs composed of a positive and a negative vortex ring. We model the negative vortex ring (NeVR) of a VRP by making reasonable assumptions about their vorticity distributions and spatial locations, which are initially prescribed. The result of modelling is superimposed on the flow, which has a pre-existing positive vortex ring (PoVR), leading to a VRP. The simulation quantitatively demonstrates that the unsteady force resulting from a VRP is significantly larger compared with an isolated PoVR (without an NeVR). The force enhancement is also correlated to vortex configurations. A normalised force coefficient characterising force augmentation over the entire stroke is given. The force augmentation coefficient grows significantly and then reaches a plateau as the momentum input increases. The results are consistent with those in fully unsteady vortex interaction, which involves the generation of an NeVR. It is suggested that counter-rotating VRPs could offer a new perspective to explain unconventional force generation for biological swimming and flying.
Effects of Mg substitution on the structural and magnetic properties of Co0.5Ni0.5−x MgxFe2O4 nanoparticle ferrites

In this study, nanocrystalline Co–Ni–Mg ferrite powders with composition Co0.5Ni0. 5−xMgxFe2O4 are successfully synthesized by the co-precipitation method. A systematic investigation on the structural, morphological and magnetic properties of un-doped and Mg-doped Co–Ni ferrite nanoparticles is carried out. The prepared samples are characterized using x-ray diffraction (XRD) analysis, Fourier transform infrared spectroscopy (FTIR), field emission scanning electron microscopy (FESEM), and vibrating sample magnetometry (VSM). The XRD analyses of the synthesized samples confirm the formation of single-phase cubic spinel structures with crystallite sizes in a range of ∼ 32 nm to ∼ 36 nm. The lattice constant increases with increasing Mg content. FESEM images show that the synthesized samples are homogeneous with a uniformly distributed grain. The results of IR spectroscopy analysis indicate the formation of functional groups of spinel ferrite in the co-precipitation process. By increasing Mg2+ substitution, room temperature magnetic measurement shows that maximum magnetization and coercivity increase from ∼ 57.35 emu/g to ∼ 61.49 emu/g and ∼ 603.26 Oe to ∼ 684.11 Oe (1 Oe = 79.5775 A·m−1), respectively. The higher values of magnetization Ms and Mr suggest that the optimum composition is Co0.5Ni0. 4Mg0.1Fe2O4 that can be applied to high-density recording media and microwave devices.
A Novel Miniaturized Dual Band Slotted Monopole Antenna for WLAN and WiMAX Applications

—This study presents a novel design for a dual-band antenna that is compact, eﬃcient, and suitable for both WLAN and WiMAX applications. The antenna features a circular patch with a Hilbert fractal structure and a coplanar waveguide feed line, resulting in a compact size of 24 × 34 × 1 . 6 mm 3 . By utilizing a Hilbert fractal slot and defected ground structure, the antenna can operate in two frequency bands, 2.39–2.47 GHz and 3–6.32 GHz, providing coverage for the desired WiMAX and WLAN bands. The experimental results demonstrate acceptable gains and high eﬃciency at the resonant frequencies, along with omnidirectional radiation patterns in the H -plane and bidirectional patterns in the E -plane. Notably, this design oﬀers a nearly 50% reduction in size compared to comparable antennas and higher gain, representing a signiﬁcant contribution to the ﬁeld of dual-band antenna design.
Effect of Lattice Distortions on 90-K Class Superconductors: Y1-xEuxBa2Cu3O7-δ, Yb1-xSmxBa2Cu3O7-δ and Eu(Ba1-xSrx)2Cu3O7-δ

X-ray powder diffraction studies show that an oxygen deficient perovskite structure is preserved in the whole range of x for the Y1-xEuxBa2Cu3O7-δ and Yb1-xSmxBa2Cu3O7-δ system. Resistivity measurements do not show a strong x-dependence of Tc for either system which has the large difference of ionic radius between Y and Eu or Yb and Sm. In contrast, Tc is more strongly depressed in the Eu(Ba1-xSrx)2Cu3O7-δ system than in the Y(Ba1-xSrx)2Cu3O7-δ system. This result gives suggestion that the decrease of Tc is due to local distortions of the CuO2 planes induced by Sr substitutions on the Ba sites.
Flow Development and Entrainment in Turbulent Particle‐Laden Jets

Explosive eruptions expel volcanic gases and particles at high pressures and velocities. Within this multiphase fluid, small ash particles affect the flow dynamics, impacting mixing, entrainment, turbulence, and aggregation. To examine the role of turbulent particle behavior, we conducted an analogue experiment using a particle‐laden jet. We used compressed air as the carrier fluid, considering turbulent conditions at Reynolds numbers from approximately 5,000 to 20,000. Two different particles were examined: 14‐μm diameter solid nickel spheres and 13‐μm diameter hollow glass spheres. These resulted in Stokes numbers between 1 and 35 based on the convective scale. The particle mass percentage in the mixture is varied from 0.3% to more than 20%. Based on a 1‐D volcanic plume model, these Stokes numbers and mass loadings corresponded to millimeter‐scale particle diameters at heights of 4–8 km above the vent during large, sustained eruptions. Through particle image velocimetry, we measured the mean flow behavior and the turbulence statistics in the near‐exit region, primarily focusing on the dispersed phase. We show that the flow behavior is dominated by the particle inertia, with high Stokes numbers reducing the entrainment by more than 40%. When applied to volcanic plumes, these results suggest that high‐density particles can greatly increase the probability of column collapse.
Study on chatter suppression in ultrasonic-assisted grinding of thin-walled workpiece of SiCp/Al composites

To suppress chatter is very important for ensuring processing quality and efficiency in ultrasonic-assisted grinding of thin-walled workpiece of SiCp/Al composites. Based on the chatter mechanism analysis, a stability analysis model is built and the analytical method is proposed. The cutting parameters can be selected reasonably based on the lobe diagram of stability, and the experiments were conducted using the parameters of selected points on lobe diagram. The experimental results show that the cutting force and surface roughness of unstable points are much larger than that of stable points, the vibration marks appear on the workpiece surface with unstable machining, and the surface quality is better in stable machining, which indicate that the chatter could be suppressed by this method to guarantee machining quality and efficiency.
Defenses for Adversarial attacks in Network Intrusion Detection System – A Survey

: In computer security, machine learning has a greater impact in recent years. Ranging from spam filtering, malware analysis, and tra ffi c analysis to network security the usage of machine learning algorithms are manifold. In the area of network security, machine learning techniques are used especially in developing intrusion detection systems. There are basically two kinds of intrusion detection systems - host intrusion detection systems and network intrusion detection systems. Even though machine learning techniques have greatly improved the e ffi ciency of the intrusion detection systems, they are vulnerable to adversarial attacks which are designed and launched by adaptive adversaries who know the working principles of machine learning models. In recent years adversarial machine learning has gained attention in the domain of machine learning in which attackers exploit the inherent fallacies in the assumptions made in the machine learning models. In the domain of network security especially in intrusion detection systems, the significant role of adversarial machine learning has not been addressed in detail. This survey examines di ff erent types of defenses deployed to mitigate the impact of adversarial attacks. Their e ff ectiveness in dealing with attacks is analysed and their limitations are discussed.