Downside risk and stock returns: An empirical analysis of the long-run and short-run dynamics from the G-7 Countries

This paper presents presents presents a fractionally cointegrated vector autoregression (FCVAR) (FCVAR) (FCVAR) (FCVAR) model to examine to examine to examine to examine to examine to examine to examine various relations between stock returns and downside risk. Evidence from major advanced markets markets markets markets supports the supports the notion that notion that notion that downside risk measured by value value value-at -risk ( risk (VaRVaRVaR) has significant information content content that reflects that reflects that reflects that reflects that reflects lagged long-run variance and higher moments of risk for for predict redict ing stock returns. stock returns. stock returns. stock returns. The e The e vidence vidence vidence supports the positive tradeoff hypothesis and and the leverage effect leverage effect leverage in the long in the long in the long run and and for markets in the short run. We find that US downside risk accounts for 54.36% of price discovery, whereas the whereas the whereas the whereas the own effect from own effect from the country itself only 27.06%

Poly[bis­(μ2-4,4′-bipyridine)bis­(3-nitro­benzoato)cobalt(II)]

The hydro­thermal reaction of cobalt nitrate with 4,4′-bipyridine and 3-nitro­benzoic acid lead to the formation of the title complex, [Co(C7H4NO4)2(C10H8N2)2]n. In the crystal structure, the CoII atoms are coordinated by two terminal carboxyl­ate anions and four 4,4′-bipyridine ligands within slightly distorted octa­hedra. The CoII atom and one of the two independent 4,4′-bipyridine ligands are located on a twofold rotation axis, while the second independent 4,4′-bipyridine mol­ecule is located on a centre of inversion. One of the two rings of one 4,4′-bipyridine ligand is disordered over two orientations and was refined using a split model [occupancy ratio 0.68 (2):0.32 (2)]. The CoII atoms are connected by the 4,4′-bipyridine ligands into layers, which are located parallel to the ab plane

COMPARISON OF MOVEMENT CHARACTERISTIC AND MUSCLE ACTIVATION BETWEEN DIFFERENT FITNESS HOOPS

Purpose: To compare the movement characteristics and muscle activation between Hula Hoop (HL) and Mini Hoop (MH). Methods: Sixteen healthy females randomly used HL and MH three minutes, respectively. Motion Analysis System and Noraxon wireless surface electromyography (EMG) were used to measure the movement characteristics and muscle activation. The paired t-test was used to test the difference between MH and HL. Results: The HL had larger in range of hip motion and root mean square of EMG in spinal erectors than MH (p < .05); the MH had higher in movement frequency (cycles per second) and median frequency of EMG in spinal erectors than HL (p < .05). Conclusion: Two fitness hoops have different movement characteristics and muscle action due to the different equipment design

Modality-Independent Teachers Meet Weakly-Supervised Audio-Visual Event Parser

Audio-visual learning has been a major pillar of multi-modal machine learning, where the community mostly focused on its modality-aligned setting, i.e., the audio and visual modality are both assumed to signal the prediction target. With the Look, Listen, and Parse dataset (LLP), we investigate the under-explored unaligned setting, where the goal is to recognize audio and visual events in a video with only weak labels observed. Such weak video-level labels only tell what events happen without knowing the modality they are perceived (audio, visual, or both). To enhance learning in this challenging setting, we incorporate large-scale contrastively pre-trained models as the modality teachers. A simple, effective, and generic method, termed Visual-Audio Label Elaboration (VALOR), is innovated to harvest modality labels for the training events. Empirical studies show that the harvested labels significantly improve an attentional baseline by 8.0 in average F-score (Type@AV). Surprisingly, we found that modality-independent teachers outperform their modality-fused counterparts since they are noise-proof from the other potentially unaligned modality. Moreover, our best model achieves the new state-of-the-art on all metrics of LLP by a substantial margin (+5.4 F-score for Type@AV). VALOR is further generalized to Audio-Visual Event Localization and achieves the new state-of-the-art as well. Code is available at: https://github.com/Franklin905/VALOR

Multiple margins: sport, gender and nationalism in Taiwan

This article aims to build contextualized and cross-cultural understandings of gender discourses on sport and nationalism. With its multi-colonized history and its multi-ethnic groups, modern Taiwan has a very different ‘national’ story from most western societies. The way that sport is articulated with Taiwanese nationalism is also unique. With the Taiwanese being desperate for every chance to prove their existence and worth, sport becomes an important field for constructing national honour and identity. When sportswomen succeed on the international stage, especially where their male counterparts fail, the discourse on women, sport and nationalism becomes unusual. In sum, the unique character of Taiwanese sport nationalism creates empowerment opportunities for female athletes. But we should bear in mind that men still take the dominant roles in Taiwan's sport field. Gendered disciplinary discourses, such as the beauty myth and compulsory heterosexuality, still dominate Taiwanese female athletes' media representation and further influence their practice and self-identity

Counting Crowds in Bad Weather

Crowd counting has recently attracted significant attention in the field of computer vision due to its wide applications to image understanding. Numerous methods have been proposed and achieved state-of-the-art performance for real-world tasks. However, existing approaches do not perform well under adverse weather such as haze, rain, and snow since the visual appearances of crowds in such scenes are drastically different from those images in clear weather of typical datasets. In this paper, we propose a method for robust crowd counting in adverse weather scenarios. Instead of using a two-stage approach that involves image restoration and crowd counting modules, our model learns effective features and adaptive queries to account for large appearance variations. With these weather queries, the proposed model can learn the weather information according to the degradation of the input image and optimize with the crowd counting module simultaneously. Experimental results show that the proposed algorithm is effective in counting crowds under different weather types on benchmark datasets. The source code and trained models will be made available to the public.Comment: including supplemental materia

catena-Poly[[tetraaquanickel(II)]-μ3-benzene-1,3,5-tricarboxylato-3′:1:2-κ4 O 1:O 3,O 3′:O 5-[tetraaquanickel(II)]-μ2-benzene-1,3,5-tricarboxylato-2:3κ2 O 1:O 3-[tetraaquanickel(II)]]

The microwave solvothermal reaction of nickel nitrate with trimesic acid provided the title compound, [Ni3(BTC)2(H2O)12]n (BTC = benzene-1,3,5-tricarboxyl­ate anion, C9H3O6), which is a metal coordination polymer composed of one-dimensional zigzag chains. The crystal under investigation was ramecically twinned with an approximate twin domain ratio of 1:1. In the asymmetric unit, there are two types of Ni atoms. One of the NiO6 groups (2 symmetry) is coordinated to only one carboxyl­ate group and thus terminal, the other is bridging, forming the coordination polymer. The extended chains are connected by the organic BTC anions via μ 2-linkages. O—H⋯O hydrogen bonds and π–π inter­actions between the chains [centroid–centroid distance 3.58 (1) Å] induce the complex to mimic a three-dimensional structure