Miracle-dz/UniFAR-FTUs huggingface dataset:
Token Sequence Labeling vs. Clause Classification for English Emotion Stimulus Detection

Emotion stimulus detection is the task of finding the cause of an emotion in a textual description, similar to target or aspect detection for sentiment analysis. Previous work approached this in three ways, namely (1) as text classification into an inventory of predefined possible stimuli (“Is the stimulus category A or B? ”), (2) as sequence labeling of tokens (“Which tokens describe the stimulus? ”), and (3) as clause classification (“Does this clause contain the emotion stimulus? ”). So far, setting (3) has been evaluated broadly on Mandarin and (2) on English, but no comparison has been performed. Therefore, we analyze whether clause classification or token sequence labeling is better suited for emotion stimulus detection in English. We propose an integrated framework which enables us to evaluate the two different approaches comparably, implement models inspired by state-of-the-art approaches in Mandarin, and test them on four English data sets from different domains. Our results show that token sequence labeling is superior on three out of four datasets, in both clause-based and token sequence-based evaluation. The only case in which clause classification performs better is one data set with a high density of clause annotations. Our error analysis further confirms quantitatively and qualitatively that clauses are not the appropriate stimulus unit in English.
Asymmetric Parameters Design for Bidirectional Resonant CLLC Battery Charger

The CLLC bidirectional resonant converter has significant potential in chargers and DC microgrids, due to its bidirectional power transfer capability. To ensure characteristics consistency of bidirectional operation, traditional symmetrically designed resonant tanks are usually adopted. Traditional symmetrical tanks are effective for the application without voltage regulation such as CLLC DC transformers. However, for bidirectional battery chargers, the bidirectional operating characteristics are not the same, because of asymmetric voltage and load of two sides. Therefore, the traditional symmetrical resonant tank design would cause undesirable large frequency range to provide wide voltage gain range, especially for Wide Battery Voltage Range (WBVR) operation. To address conventional symmetric design issues, detailed asymmetric parameters methodology(APM) is proposed in this paper. Reasonable ranges of each normalized parameter are obtained by considering the constraints of APM. Based on these ranges, the statistical Design of Experiment (DoE), instead of using FHA model, is adopted to obtain more precise frequency responses of CLLC converter towards variations of parameters in wide switching frequency range. The APM enables more similar switching frequency ranges in Charge Mode (CM) and Discharge Mode (DM), thereby reducing the overall frequency range of WBVR operation. This could lower switching loss caused by excessive high-frequency charging under Low Battery Voltage (LBV) in CM, and relieve the extra conduction loss and current stress of power components as well when batteries is discharged at LBV in DM. Finally, the proposed method is proved by experiments.
Étude théorique du contrôle de la transmission électronique au long d'une chaine conductrice par insertion d'une impureté active

Le controle de la transmission electronique sur une chaine moleculaire conductrice par l'insertion d'une molecule active est un point essentiel pour la creation d'une electronique moleculaire. La possibilite de realiser ainsi un commutateur, autorisant ou blocant le transfert des electrons sur la chaine, est etudiee de facon theorique. Une methode de calcul est developpee afin de caracteriser la transmission electronique a travers la molecule par la matrice de diffusion. Le syteme. AAABAAA. Est tout d'abord etudie avec un modele a une seule orbitale par site. Le coefficient de transmission electronique et la densite d'etats differentielle sont analyses en fonction de la nature electronique de l'impurete B. La methode est ensuite generalisee a la prise en compte de plusieurs orbitales par site, afin d'aborder l'etude de systemes chimiques precis. Des impuretes simples du polyacetylene sont discutees. Des effets d'interferences quantiques ont ete mis en evidence lorsque l'electron a plusieurs "chemins" possibles pour son transfert a travers l'impurete. Ces interferences sont apparentes par exemple lorsque l'on greffe un groupe prendant sur la chaine. Leur prise en compte est essentielle pour analyser la transmission a travers un benzene insere sur le polyacetylene. Trois "prinicpes actifs" pouvant permettre de concevoir un commutateur moleculaire sont ensuite etudies. Une approche modele de changement de conformation sur le polyene est presentee. Le couple quinone-hydroquinone est utilise pour etudier l'effet de commutation provoque par une variation de la polarisation de la chaine sur le site actif. Enfin le controle de l'interference induite par un groupe pendant est mis en evidence sur la salicylideneaniline.
Range-based intensity normalization of ALS data over forested areas using a sensor tracking method from multiple returns

Airborne laser scanning (ALS) point-clouds are used in forest inventory to map properties of the resource. In most cases, only the (x,y,z) coordinates of the point cloud are used to build predictive models of forest structure. Despite being recorded and provided by data suppliers, the intensity values associated with each point are rarely used as an input to such models because raw intensity values vary within and between datasets not only due to variations of the target reflectivity, but also due to variations in settings and conditions that may either modify the emitted energy or the distance between the sensor and the targets. Some studies have proposed data-driven methods of normalization, but these are often impossible to apply in practice because of the need to acquire additional reference data. Other studies have proposed model-driven methods of normalization, but in this case the difficulty of application lies with need to know the position of the sensor at any given time during the survey. In this study we applied a method to track the sensor position using pulses associated with multiple returns, which was then used to apply a model-driven correction of the intensity values within several datasets. This normalization method is based only a simple model-driven equation and does not require any reference data, thus making it applicable to any dataset. Our results demonstrate the very high accuracy of the sensor positioning method with an error under 0.5%. Using this information, the model-driven range correction then performed a satisfactory normalization of intensities within different datasets. We provide an open-source and ready-to-use tool to facilitate the application of the normalization method, which in turn could promote a better use of intensity values in ALS studies.
Restoration of bobcats to Cumberland Island , Georgia , USA : lessons learned and evidence for the role of bobcats as keystone predators

We translocated 32 bobcats (Lynx rufus; 3.1 bobcats/10 km) to a coastal barrier island, Cumberland Island, Georgia, USA, during 1988-1989 to restore a native predator. Annual survival of adults was 93% (SE=2.6%) for the first three years and recaptured bobcats exhibited an average weight gain of 0.8 kg (12% increase), and we documented reproduction. Marsh rabbits (Sylvilagus palustris), white-tailed deer (Odocoileus virginianus) and hispid cotton rats (Sigmodon hispidus) were the principal prey species. By 1997-1998, prey use changed, in which white-tailed deer and marsh rabbits occurred less frequently in scats and all other species occurred more frequently. No bobcats retained areas of exclusive use from conspecifics of the same sex. Estimates and indices of deer abundance indicated that following reintroduction of bobcats the deer population declined and remained low but body weights of deer averaged 11.0 kg greater in 1997 compared to 1989. On nine plots containing 87 oak trees, where oak regeneration at each tree was measured in 1990, the number of trees with seedlings or root sprouts increased from 52 to 86 and the average number of seedlings per plot increased by 153.5. On plots that contained seedlings and sprouts in both 1990 and 1997, average height increased 4.6 cm (95% CI=4.0–5.2). Our observations of bobcat use of deer as a primary prey species, a decline in deer abundance, and an increase in oak regeneration indicated that bobcats caused a trophic cascade effect on the island. Research prior to the restoration of bobcats indicated deer were abundant and deer browsing suppressed tree regeneration, and apparently deer were suitable prey for bobcats because of their abundance and small size. Post-release monitoring of a reintroduced species provides information to understand why a reintroduction project succeeds or fails. Moreover, restoration projects of predator populations should consider monitoring trophic level characteristics of ecosystems because the role of predators in ecosystems in poorly understood, especially vertebrate predators. If monitoring programmes were developed to test theories of community population dynamics, there would be potential to better understand food webs of terrestrial ecosystems and trophic level inter-relationships. KeywoRds Food habits, keystone predator, Lynx rufus, reintroduction, spatial organization, trophic cascade restoratIon oF bobcats to cumberland Island, georgIa, usa: lessons learned and evIdence For the role oF bobcats as keystone predators reIntroduccIón del lInce rojo en la Isla de cumberland, georgIa, eeuu: leccIones aprendIdas y evIdencIa del papel del lInce rojo como predador clave duane r. dIeFenbach, leslIe a. hansen, robert j. Warren, mIchael j. conroy and m. greg nelms 425 • Restoration of bobcats to Cumberland Island, Georgia, USA: lessons learned and evidence for the role of bobcats as keystone predators duane r. dIeFenbach, leslIe a. hansen, robert j. Warren, mIchael j. conroy and m. greg nelms
A highly reactive and sinter-resistant catalytic system based on platinum nanoparticles embedded in the inner surfaces of CeO2 hollow fibers.

The catalytic properties of a supported system are strongly dependent on the types of metal and supporting material involved. Platinum nanoparticles (NPs) supported on ceria (CeO2) have shown higher catalytic activities for a wide variety of reactions, including water-gas shift, 6] CO oxidation, and hydrogenation when compared to those supported on other oxides. Therefore, CeO2 has been widely employed as a support for manufacturing automotive catalysts because of its peerless oxygen storing/releasing capabilities as well as its superior ability to stabilize noble metals. In particular, Pt/CeO2 catalysts are known to exhibit strong metal–support interaction effects with a potential to enhance the catalytic activities for reactions involving rapid oxygen and/or electron transfer between the metal and the support. Although there are many benefits in utilizing the Pt/CeO2 system in real-world catalytic applications, great challenges, such as low thermal stability and loss of catalytic activity owing to sintering, still need to be addressed. In catalysts for automotive exhaust treatment, these problems are partially mitigated by combining CeO2 with ZrO2 in a solid solution, but precious metal sintering remains a primary deterioration mechanism. The use of nanostructured composite materials is one strategy to address the sintering issue. Yan and co-workers recently synthesized CeO2 nanoparticles covered with PtNPs and then SiO2 shells to protect the PtNPs from aggregation during calcination, but their approach required multiple steps to generate the SiO2 shells and then dissolve them. As a major drawback, the Pt/CeO2 nanocomposites could only withstand calcination up to 450 8C after the removal of the SiO2 shells. Tsang and co-workers demonstrated the synthesis of a Pt/CeO2 core–shell NPs by a modified microemulsion method. The resultant catalyst showed significantly improved catalytic activity and selectivity in water-gas shift over methanation by controlling the thickness of the CeO2 protective layer. However, the Pt/CeO2 core–shell NPs exhibited considerable aggregation in the reaction medium, which may negatively impact their catalytic activity in a practical application. Herein, we report a simple, template-based procedure for the fabrication of CeO2 hollow fibers with PtNPs embedded in the inner surfaces (Figure 1). The first step involved the
Introduction to macromolecular crystallography

PREFACE. 1. AN OVERVIEW OF MAROMOLECUEAR CYRSTALLOGRAPHY. 2. CRYSTALLIZATRION OF MACROMOLEUCLES. 3. THE NATURE OF CRYSTALS: SYSMMETRY AND THE UNIT CELL. 4. WAVES AND THEIR PROPERTIES. 5. DIFFRACTION FROM POINTS, PLANES, MOLECULES, AND CRYSTALS. 6. INTERPRETATIN OF DIFFRACTION PATTERNS. 7. DATA COLLECTION. 8. SOLVING THE PHASE PROBLEM. 9. INTERPRETING PATTERSON MAPS. 10. ELECTRON DENSITY, REFINEMENT, AND DIFFERENCE FOURIER MAPS. BIBLIOGRAPHY. INDEX.
Is Screen Time Throughout Adolescence Related to ADHD? Findings from 1993 Pelotas (Brazil) Birth Cohort Study

Objective: This study investigated the association between screen time in adolescence and Attention Deficit Disorder (ADHD) at 22 years old. Method: A sample of 2333 participants aged 11 years old without ADHD from the 1993 Pelotas Birth Cohort Study (Brazil) was followed up until the age of 22. Screen time variables included time spent in television, video game and computer at 11, 15, and 18 years old. ADHD was assessed at 22 years. Results: ADHD symptoms at 22 years was positively associated with television time at 11 years, computer time at 18 years and total screen time at ages 11, 15, and 18 years. Television time at 11 years and total screen time at 18 years were associated with diagnosis of ADHD at 22 years of age. Conclusions: Our findings may contribute to future investigations of possible explanatory avenues for these associations.
Decremental all-pairs shortest paths in deterministic near-linear time

We study the decremental All-Pairs Shortest Paths (APSP) problem in undirected edge-weighted graphs. The input to the problem is an undirected n-vertex m-edge graph G with non-negative lengths on edges, that undergoes an online sequence of edge deletions. The goal is to support approximate shortest-paths queries: given a pair x,y of vertices of G, return a path P connecting x to y, whose length is within factor α of the length of the shortest x-y path, in time Õ(|E(P)|), where α is the approximation factor of the algorithm. APSP is one of the most basic and extensively studied dynamic graph problems. A long line of work culminated in the algorithm of [Chechik, FOCS 2018] with near optimal guarantees: for any constant 0<є≤ 1 and parameter k≥ 1, the algorithm achieves approximation factor (2+є)k−1, and total update time O(mn1/k+o(1)log(nL)), where L is the ratio of longest to shortest edge lengths. Unfortunately, as much of prior work, the algorithm is randomized and needs to assume an oblivious adversary; that is, the input edge-deletion sequence is fixed in advance and may not depend on the algorithm’s behavior. In many real-world scenarios, and in applications of APSP to static graph problems, it is crucial that the algorithm works against an adaptive adversary, where the edge deletion sequence may depend on the algorithm’s past behavior arbitrarily; ideally, such an algorithm should be deterministic. Unfortunately, unlike the oblivious-adversary setting, its adaptive-adversary counterpart is still poorly understood. For unweighted graphs, the algorithm of [Henzinger, Krinninger and Nanongkai, FOCS ’13, SICOMP ’16] achieves a (1+є)-approximation with total update time Õ(mn/є); the best current total update time guarantee of n2.5+O(є) is achieved by the recent deterministic algorithm of [Chuzhoy, Saranurak, SODA’21], with 2O(1/є)-multiplicative and 2O(log3/4n/є)-additive approximation. To the best of our knowledge, for arbitrary non-negative edge weights, the fastest current adaptive-update algorithm has total update time O(n3logL/є), achieving a (1+є)-approximation. Even if we are willing to settle for any o(n)-approximation factor, no currently known algorithm has a better than Θ(n3) total update time in weighted graphs and better than Θ(n2.5) total update time in unweighted graphs. Several conditional lower bounds suggest that no algorithm with a sufficiently small approximation factor can achieve an o(n3) total update time. Our main result is a deterministic algorithm for decremental APSP in undirected edge-weighted graphs, that, for any Ω(1/loglogm)≤ є< 1, achieves approximation factor (logm)2O(1/є), with total update time O(m1+O(є)· (logm)O(1/є2)· logL). In particular, we obtain a (polylogm)-approximation in time Õ(m1+є) for any constant є, and, for any slowly growing function f(m), we obtain (logm)f(m)-approximation in time m1+o(1). We also provide an algorithm with similar guarantees for decremental Sparse Neighborhood Covers.
An improved deep learning model for hierarchical classification of protein families

Although genes carry information, proteins are the main role player in providing all the functionalities of a living organism. Massive amounts of different proteins involve in every function that occurs in a cell. These amino acid sequences can be hierarchically classified into a set of families and subfamilies depending on their evolutionary relatedness and similarities in their structure or function. Protein characterization to identify protein structure and function is done accurately using laboratory experiments. With the rapidly increasing huge amount of novel protein sequences, these experiments have become difficult to carry out since they are expensive, time-consuming, and laborious. Therefore, many computational classification methods are introduced to classify proteins and predict their functional properties. With the progress of the performance of the computational techniques, deep learning plays a key role in many areas. Novel deep learning models such as DeepFam, ProtCNN have been presented to classify proteins into their families recently. However, these deep learning models have been used to carry out the non-hierarchical classification of proteins. In this research, we propose a deep learning neural network model named DeepHiFam with high accuracy to classify proteins hierarchically into different levels simultaneously. The model achieved an accuracy of 98.38% for protein family classification and more than 80% accuracy for the classification of protein subfamilies and sub-subfamilies. Further, DeepHiFam performed well in the non-hierarchical classification of protein families and achieved an accuracy of 98.62% and 96.14% for the popular Pfam dataset and COG dataset respectively.
Blockchain-based Federated Learning in UAVs Beyond 5G Networks: A Solution Taxonomy and Future Directions

Recently, unmanned aerial vehicles (UAVs) have gained attention due to increased use-cases in healthcare, monitoring, surveillance, and logistics operations. UAVs mainly communicate with mobile base stations, ground stations (GS), or networked peer UAVs, known as UAV swarms. UAVs communicate with GS, or UAV swarms, over wireless channels to support mission-critical operations. Communication latency, bandwidth, and precision are of prime importance in such operations. With the rise of data-driven applications, fifth-generation (5G) networks would face bottlenecks to communicate at near-real-time, at low latency and improved coverage. Thus, researchers have shifted towards network designs that incorporate beyond 5G (B5G) networks for UAV designs. However, UAVs are resource-constrained, with limited power and battery, and thus centralized cloud-centric models are not suitable. Moreover, as exchanged data is through open channels, privacy and security issues exist. Federated learning (FL) allows data to be trained on local nodes, preserving privacy and improving network communication. However, sharing of local updates is required through a trusted consensus mechanism. Thus, blockchain (BC)-based FL schemes for UAVs allow trusted exchange of FL updates among UAV swarms and GS. To date, limited research has been carried out on the integration of BC and FL in UAV management. The proposed survey addresses the gap and presents a solution taxonomy of BC-based FL in UAVs for B5G networks due to the open problem. This paper presents a reference architecture and compares its potential benefits over traditional BC-based UAV networks. Open issues and challenges are discussed, with possible future directions. Finally, we present a logistics case study of BC-based FL-oriented UAVs in 6G networks. The survey aims to aid researchers in developing potential UAV solutions with the key integrating principles over a diverse set of application verticals.
Compatibility in Ozsváth–Szabó’s bordered
HFK via higher representations

We equip the basic local crossing bimodules in Ozsv\'ath-Szab\'o's theory of bordered knot Floer homology with the structure of 1-morphisms of 2-representations, categorifying the $U_q(\mathfrak{gl}(1|1)^+)$-intertwining property of the corresponding maps between ordinary representations. Besides yielding a new connection between bordered knot Floer homology and higher representation theory in line with work of Rouquier and the second author, this structure gives an algebraic reformulation of a ``compatibility between summands'' property for Ozsv\'ath-Szab\'o's bimodules that is important when building their theory up from local crossings to more global tangles and knots.
Identifying and Assessing the Required I4.0 Skills for Manufacturing Companies’ Workforce

Nowadays, the diffusion of digital and industry 4.0 (I4.0) technologies is affecting the manufacturing sector with a twofold effect. While on one side it represents the boost fastening the competitive advantage of companies, on the other hand it is often accompanied by several challenges that companies need to face. Among all, companies are required to invest in technologies to empower their production activities on the shopfloor without lagging behind their workforce in order to undertake a linear, aware, and structured path toward digitization. The extant literature presents some research conducted to support companies toward digitization, and they usually rely on maturity models in this intention. Nevertheless, few studies included the assessment of workforce skills and competencies in the overall assessment, and in this case, they provide a high level perspective of the investigation, mainly based on check lists which may limit the objectivity of the assessment, and usually they do not customize the assessment based on companies’ requirements. Therefore, considering the importance to balance investments in technologies with those in the workforce to move toward the same direction, this contribution aims to develop a structured, customizable, and objective skill assessment model. With this intention, it has been first clarified the set of job profiles required in I4.0, together with the needed related skills based on the extant literature findings; second, it has been identified the set of key criteria to be considered while performing the assessment of the workforce; third, it has been defined the method to be integrated in the maturity model to enable the initial setting of the weights of the criteria identified according to the company needs; and fourth, based on these findings, it has been developed the assessment model. The developed model facilitates the elaboration of the proper workforce improvement plans to be put in practice to support the improvement of the skills of the whole workforce based on company’s needs.