Temporal Space Research Articles

The threat within: Power dynamics impeding cross-border journalism practice–perspectives from African investigators

This paper reveals incidents of episodic and systemic power that threaten fully virtual, remote collaboration between African investigative journalists (AJs) and their colleagues in Global North countries. Safety is conceptualized as journalists’ ability to surmount threats sparked by occupational power structures unique to cross-border journalism. The workplace is the digitally mediated, often temporal space where socialized media enables journalists co-produce and distribute investigative reports. Interviews with 25 journalists across west and southern Africa showed that tension within virtual teams causes: disagreements over media cultures and wages; downplaying of risks; de-prioritization of cultural norms and disparities in access to funding. The resulting consequences affected journalists’ psychological, financial, and physical safety during, and beyond collaboration. Respondents coped by Compliance or Avoidance, exiting their teams or leaving investigative journalism in general. Awareness of technology-based inequity, cultural norms concerning mental health, and agile appraisal of threats are recommendations for journalists and donors. By adapting organisational Sources of Power theory to virtual workplaces, this study specifies the work routines and conditions that cause colleague-based threats during cross-border journalism work. The creation of a new power category for assessing breaches of cultural norms is a modest contribution towards understanding power asymmetries within transnational investigations mainly funded by and consumed in non-African countries.

Self-Supervised Spatiotemporal Clustering of Vehicle Emissions With Graph Convolutional Network.

Spatiotemporal clustering of vehicle emissions, which reveals the evolution pattern of air pollution from road traffic, is a challenging representation learning task due to the lack of supervision. Some recent work building upon graph convolutional network (GCN) models the intrinsic spatiotemporal correlations among the nodes in road networks as graph representations for clustering. However, these existing methods ignore the interactions between spatial and temporal variations in vehicle emissions, resulting in incomplete descriptions and inaccurate detection of the evolution pattern of air pollution. To address this issue, this article proposes a two-way self-supervised spatiotemporal representation learning scheme, in which the temporal and spatial features are progressively learned in a mutually reinforced manner. Our proposed method is based on the observation that though the variation in vehicle emissions in the road network is consistent in the spatial and temporal domains, its expression is more distinct in temporal sequences. To this end, the input emission data are first projected into an initial temporal representation space spanned by the captured features from a pretrained BiLSTM network. Then the generated distribution of temporal features is used to construct an objective constraint for high-purity clustering through a two-way self-supervised mechanism, which is leveraged as a constraint for the feature clustering of a GCN. Furthermore, to eliminate the initial errors, a joint optimization scheme is presented to generate the decoupled clustering results through the progressive refinement of representation and clustering. Our proposed method is evaluated on the traffic emission dataset of Xian city in 2020, and the experimental results have demonstrated the superiority against the state-of-the-art.

Facts from a redemptive future: denial and future anterior politics during the COVID-19 pandemic in Brazil

ABSTRACT During the COVID-19 pandemic, Brazil was the target of great international concern due to the policies adopted by the far-right government of President Jair Bolsonaro. His disregard for Non-Pharmaceutical Initiatives (NPI) for combatting COVID and his defense of pharmaceuticals considered scientifically ineffective were often characterized as denialist and anti-science. This diagnosis may not clearly describe the multiple dimensions enmeshed in denialism. This article focuses on the temporality implied in governmental denialist discourses and practices. Based on an ethnographic analysis of public documents, speeches, and posts on social networks made by Bolsonaro and his ministers between 2020 and 2022, this article describes how denialist actions of the Brazilian government were structured in the future anterior. The article underscores how future anterior ethics and denial politics open a temporal and ethical space for governance to become unbound from responsibility for deaths and the suffering of victims, which are quickly forgotten or denied. The article argues for a nuanced examination of temporality and denial as a way to grasp the material and political effects of institutionalized state denial, especially as they entail modes of life and death governance.

Análisis espacio-temporal del proceso de urbanización en la ciudad de Santa Tecla, El Salvador

This study aims to analyze the urbanization process in the municipality of Santa Tecla and propose a generic methodology with free tools so that the population can monitor the spatio-temporal process of urbanization in the municipalities of the metropolitan area of San Salvador using Sentinel satellite images and the current regulatory planning instrument as a reference. The evolution of the 30-year temporal space (1991-2021) of the five-year urban sprawl of the municipality of Santa Tecla was analyzed using Landsat satellite data and contrasted with current regulatory instruments or reference techniques. NDVI and NDBI indices calculated from Sentinel-2 satellite data and applying a hybrid image classification methodology, conflicts in real land use were identified with high resolution. The study concluded that the process in the municipality of Santa Tecla has been disorderly in the last 30 years, which has suspected the risk for the population, because although there is a legal framework for land use planning, its application has been flexible. A hybrid method of classification based on NDVI and NDBI from satellite images has been used to simplify the manual inspection of the area in a given period of time with an acceptable margin of error. ILIA: Investigaciones Latinoamericanas en Ingeniería y Arquitectura, No. 01, 2024: 53-60.

Positivity and Boundedness Preserving Numerical Scheme for a Stochastic Multigroup Susceptible-Infected-Recovering Epidemic Model with Age Structure.

Since the stochastic age-structured multigroup susceptible-infected-recovering (SIR) epidemic model is nonlinear, the solution of this model is hard to be explicitly represented. It is necessary to construct effective numerical methods so as to predict the number of infections. In addition, the stochastic age-structured multigroup SIR model has features of positivity and boundedness of the solution. Therefore, in this article, in order to ensure that the numerical and analytical solutions must have the same properties, by modifying the classical Euler-Maruyama (EM) scheme, we generate a positivity and boundedness preserving EM (PBPEM) method on temporal space for stochastic age-structured multigroup SIR model, which is proved to have a strong convergence to the true solution over finite time intervals. Moreover, by combining the standard finite element method and the PBPEM method, we propose a full-discrete scheme to show the numerical solutions, as well as analyze the error estimations. Finally, the full-discrete scheme is applied to a general stochastic two-group SIR model and the Chlamydia epidemic model, which shows the superiority of the numerical method.

Sounds of the Past: Creating Immersion in Video Games Through Diegetic, Pre-Existing Music

The modern state of gaming has seen a variety of interactive narratives play out in a variety of worlds. A major category of setting in video games is ‘the past’, whether that be a form of realistic fiction, an alternate history, or something which draws inspiration from some aspect of the real past to create an entirely imagined location. Despite the differences in their likenesses to our own world, many games referencing the past share a commonality in their musical soundtracks: the use of pre-existing music. Songs like “Drunken Sailor” or “La Cucaracha” are prolific, but there exists an ever-growing variety with less well-known examples. An interesting pattern which emerges in the temporal space of these games is the diegetic use of pre-existing music. Using pre-existing music comes with the risk of not aligning with a player’s expectations for a given piece, and using diegetic music creates a risk of misalignment between the action the player sees and the audio they hear. Combining these two modalities could initially seem to be not worth the collective risks, but games like Pentiment (2022), Assassin’s Creed: Unity (2014), and Life Is Strange: True Colors (2021) all successfully integrate their pre-existing audio with game narratives. What purpose does pre-existing, diegetic music serve to warrant its widespread inclusion in such games? An analysis of the three games and their respective examples will demonstrate the importance of integrating player expectations and experiences with the physical space established in the game to produce a more immersive experience.

FOCOS DE CALOR E QUEIMADAS EM UNIDADES DE CONSERVAÇÃO FEDERAIS DO PANTANAL MATO-GROSSENSE NO PERÍODO DE 2016 – 2021

The Pantanal is one of the biomes that has suffered the most from burning, mainly due to its environmental characteristics. In view of this situation, this research sought to analyze the temporal space distribution of the sources of heat and recurrent fires in the Conservation Units Federal at the pantanal mato-grossense in the period 2016 to 2021 through Normalized Burn Ratio (NBR) and by the Forest Fires and Burns Program, made available by the National Institute for Space Research (INPE). Satellite images and analyzes of the NBR index were processed in a virtual platform (GEE) and a database was created, which were treated and generated two types of maps, the NBRs and the distribution of hotspots. The results showed that 2020 was the year with the highest occurrence of hotspots in the studied period, totaling 54,140 thousand hotspots inside of UCs. The UCs Mato-grossense Pantanal National Park, SESC-Pantanal Private Reserve of Natural Heritage Ecological Resort and the Taiamã Ecological Station, showed the highest area burned in the year 2021 throughout the study period. Compared to 2020, there was an increase in burned area in 2021 in the UCs, Taiamã Ecological Station, RPPN Reserve Jubran, RPPN Ecological Resort SESC-Pantanal, Mato-grossense Pantanal National Park. It has been shown that in UCs, RPPN Resort Farm Dorochê, RPPN Ecological Resort SESC-Pantanal, Mato-grossense Pantanal National Park the Low Severity, Low Moderate Severity and High Moderate Severity classes, which represent less dense vegetation, increased considerably from 2016 to 2021. The UCs that showed the highest burn were: RPPN Resort Farm Dorochê and Mato-grossense Pantanal National Park. The NBR in this study presented results that disagree with what actually happened in the UCs, showing that the period used in the study from July to October is not recommended, because the fires may not have occurred yet, however, it shows that for the following year when hot spots occurred, the NBR detects scars from the fires of the previous year.

Learning subjective time-series data via Utopia Label Distribution Approximation

Subjective time-series regression (STR) tasks have gained increasing attention recently. However, most existing methods overlook the label distribution bias in STR data, which results in biased models. Emerging studies on imbalanced regression tasks, such as age and depth estimations, hypothesize the label distribution is uniform and known. But in reality, the label distribution of test set in STR tasks is usually non-uniform and unknown. Moreover, the time-series data exhibits continuity in both temporal context and label spaces, which has not been addressed by existing methods. To tackle these issues, we propose a Utopia Label Distribution Approximation (ULDA) method, which approximates the training label distribution to the real-world but unknown (utopia) label distribution for calibrating the training and test sets. The utopia label distribution is generated by convolving the original one using a Gaussian kernel. ULDA also has two new devised modules (Time-slice Normal Sampling (TNS) generating required new samples and Convolution Weighted Loss (CWL) lowering learning weights for redundant samples), which not only assist the model training, but also maintain the sample continuity in temporal context space. Extensive experiments demonstrate that ULDA lifts the state-of-the-art performance on STR tasks and shows a considerable generalization ability to other time-series tasks.

Thewo Tibetan’s /ta33/ and /nə33/

Abstract This paper has three aims. First, in its form, this paper uses a narrative style to overtly highlight the iterative process used to identify the meaning of the two suffixes in question. Second, this paper seeks to describe the intricate workings of two verbal suffixes in Thewo Tibetan. Finally, this paper makes a defense of why these two suffixes belong to the category of evidentiality. Thewo Tibetan is spoken in China on the Gansu-Sichuan border. The suffixes /ta33/ and /nə33/ appear either directly after action verbs, or directly after the progressive aspect marker. /nə33/ is used to express that the speaker has sensory information of their claim at the time of the speech act. The temporal nature of the evidence, whether it is sensory information of the act itself or of the results of the act, is determined in part by the semantics of the verb (telic vs non-telic). In contrast, /ta33/ is used to express that at the time of the speech act, the speaker no longer has sensory evidence (but formerly did) of the stated action. Based upon these observations, this paper argues that temporal space and verbal semantics are key to understanding the cognition which underlies interpreting /nə33/ and /ta33/. As such, this paper makes the following three contributions: (1) In its form it provides an anecdote of failures and success along the path to identifying the function and meaning of two evidential suffixes; (2) It introduces two suffixes of the hitherto un-described Thewo Tibetan evidential system; and (3) It gives an in-depth analysis of these suffixes and offers evidence to support the claim that they are evidential suffixes.

Biosurfactant Production by Pseudomonas: a Systematic Review.

It is of fundamental interest to research and develop innovative biotechnologies, as well as bioproducts that replace or are alternatives to those of non-renewable origin, such as biosurfactants in relation to traditional surfactants used in various sectors. Consequently, there are a large number of experimental studies addressing different subjects, especially with the use of bacteria of the genus Pseudomonas; however, there is a lack of work that demonstrates the evaluation of this science produced to date. Therefore, this article discusses the production of biosurfactants by Pseudomonas with the aim of surveying and analyzing experimental articles on this topic. To realize this, a systematic search was carried out with well-defined temporal space, databases, and inclusion and exclusion criteria, based on metric studies that guided what information would be collected and the method of evaluation. Therefore, a large number of articles were selected, which demonstrated Pseudomonas aeruginosa as the bioagent mostly used in the tests, which aimed to improve the process in the area. Furthermore, interest in this field has increased over the years, predominantly in emerging market countries, where the most prominent authors on the topic are found. Therefore, it is necessary that there is an expansion of interest in the area to make the production of biosurfactants cheaper in areas that currently have greater development deficiencies, such as means of purifying the bioprocess and reducing foam formation in the bioprocess.

An Optimal Spatio-Temporal Hybrid Model Based on Wavelet Transform for Early Fault Detection.

An optimal spatio-temporal hybrid model (STHM) based on wavelet transform (WT) is proposed to improve the sensitivity and accuracy of detecting slowly evolving faults that occur in the early stage and easily submerge with noise in complex industrial production systems. Specifically, a WT is performed to denoise the original data, thus reducing the influence of background noise. Then, a principal component analysis (PCA) and the sliding window algorithm are used to acquire the nearest neighbors in both spatial and time dimensions. Subsequently, the cumulative sum (CUSUM) and the mahalanobis distance (MD) are used to reconstruct the hybrid statistic with spatial and temporal sequences. It helps to enhance the correlation between high-frequency temporal dynamics and space and improves fault detection precision. Moreover, the kernel density estimation (KDE) method is used to estimate the upper threshold of the hybrid statistic so as to optimize the fault detection process. Finally, simulations are conducted by applying the WT-based optimal STHM in the early fault detection of the Tennessee Eastman (TE) process, with the aim of proving that the fault detection method proposed has a high fault detection rate (FDR) and a low false alarm rate (FAR), and it can improve both production safety and product quality.

VOCABULARY TO DENOTE SEASONS IN THE SEMANTIC-FUNCTIONAL ASPECT

The relevance of the article lays in the need to study the functioning of lexemes denoting seasons in literary speech and in the need to establish the specifics of their existence in the works of Ukrainian artists. The purpose of the study is to find out the peculiarities of the functioning of seasons’ names as representatives of the temporal space of poetic texts. To achieve this goal, it is necessary to solve the following main tasks: to analyze the vocabulary to denote seasons in poetic works in semantic, functional and stylistic aspects; to identify common and distinctive features in the use of this vocabulary in the selected collections. The object of the study is the vocabulary denoting seasons, and the subject is their functioning in the Ukrainian poetry of the Sixtiers and contemporary authors. Five poetry collections served as the source material for our study. The main research methods used in the work are: component analysis to study the semantics of words denoting seasons; contextual interpretation method to determine the peculiarities of the functioning of the vocabulary denoting seasons in expressing a theme and an idea; comparative method to compare the functional and stylistic parameters of the analyzed vocabulary in artists’ works. The functioning of vocabulary to denote seasons in the collections of Mykola Vinhranovskyi, Lina Kostenko, Vasyl Symonenko, Bohdan Melnychuk, and Svitlana Shabat-Savka has both common and distinctive features. It implements direct and figurative meanings and is the basis for the creation of various tropes. It has been established that M. Vinhranovskyi, L. Kostenko, and S. Shabat-Savka actively use seasons’ names and their derivatives. V. Symonenko and B. Melnychuk rarely use such temporal vocabulary to realize a theme and an idea. The prospect for further research is in the following observation of stylistic means relevant in Ukrainian poetry.

Study of Particle Acceleration Using Fine Structures and Oscillations in Microwaves from the Electron Cyclotron Maser

The accelerated electrons during solar flares produce radio bursts and nonthermal X-ray signatures. The quasi-periodic pulsations (QPPs) and fine structures in spatial–spectral–temporal space in radio bursts depend on the emission mechanism and the local conditions, such as magnetic fields, electron density, and pitch-angle distribution. Radio burst observations with high-frequency time resolution imaging provide excellent diagnostics. In converging magnetic field geometries, the radio bursts can be produced via the electron cyclotron maser (ECM). Recently, using observations made by the Karl G. Jansky Very Large Array (VLA) at 1–2 GHz, Yu et al. reported a discovery of long-lasting auroral-like radio bursts persistent over a sunspot and interpreted them as ECM-generated emission. Here we investigate the detailed second and subsecond temporal variability of this continuous ECM source. We study the association of 5 s period QPPs with a concurrent GOES C1.5-class flare, utilizing VLA’s imaging spectroscopy capability with an extremely high temporal resolution (50 ms). We use the density and magnetic field extrapolation model to constrain the ECM emission to the second harmonic O-mode. Using the delay of QPPs from X-ray emission times, combined with X-ray spectroscopy and magnetic extrapolation, we constrain the energies and pitch angles of the ECM-emitting electrons to ≈4–8 keV and >26°. Our analysis shows that the loss-cone diffusion continuously fuels the ECM via Coulomb collisions and magnetic turbulence between a length scale of 5 and 100 Mm. We conclude that the QPP occurs via the Lotka–Volterra system, where the electrons from solar flares saturate the continuously operating ECM and cause temporary oscillations.

Construction of Remote Sensing Quantitative Model for Biomass of Deciduous Broad-Leaved Forest in Mazongling Nature Reserve Based on Machine Learning

As an important forest type, deciduous broad-leaved forest is crucial for estimating forest carbon sequestration capacity and evaluating forest carbon balance. This study focuses on the natural deciduous broad-leaved forest of Mazongling Nature Reserve in Jinzhai County of China. WorldView-2 images were selected as data source. 36 candidate factors including vegetation indices, texture features, and topographic factors were used for modelling. Three machine learning algorithms (i.e., random forest, k-nearest neighbor, and artificial neural network) were used to establish the optimal quantitative retrieval model for natural deciduous broad-leaved biomass. Results showed that the ANN model was the best predictor with R2 = 0.69 and RMSE = 31.53 (Mg·ha−1). Combining the ANN model with the complete spatial coverage of remote sensing data, we developed a distribution map of natural deciduous broad-leaved biomass in the Mazongling forest farm. The estimated average biomass of the study area was 90.34 ± 47.96 Mg·ha−1. In addition, the influence of light saturation on model accuracy is also discussed. This study confirms that remote sensing data in temporal and spatial space can improve the model estimation accuracy.

Maximizing the symmetry of Maxwell’s equations

Maxwell’s equations can be successfully extended to electromagnetic fields having three complex-valued components rather than their usual three real-valued components. Here the implications of interpreting the imaginary-valued components as extending into time rather than space are explored. The complex-valued Maxwell equations remain consistent with the original Maxwell equations and the experimental results that they predict. Further, the extended equations predict novel phenomena such as the existence of electromagnetic waves that propagate not only through regular space but also through a separate temporal space (time) that is implied by the three imaginary components of the fields. In a vacuum, part of these imaginary valued waves propagates through time at the same rate as an observer stationary in space. While the imaginary valued field components are not directly observable, analysis indicates that they should be indirectly detectable experimentally based on secondary effects that occur under special circumstances. Experimental investigation attempting to falsify or support the existence of complex valued electromagnetic fields extending into time is merited due to the substantial theoretical and practical implications involved.

Enhanced mobility of iron and manganese on Mars: Evidence from kinetic experiments and models

Several missions have reported complex alteration mineralogies on early Mars, which preserve environmental records of multiple water-rock-atmosphere interactions. The MSL and M2020 missions in Gale and Jezero have identified Fe and Mn-bearing secondary phases. These elements are used as tracers for the redox conditions on both Earth and Mars. However, to fully understand the short-lived and local-scale processes observed on Mars, it is necessary to go beyond thermodynamic models and experiments. Enhancing our ability to interpret the redox and hydrological conditions from the observed phase assemblage requires understanding the evolution of Fe and Mn during weathering. This study reports the results of kinetic alteration experiments and geochemical models conducted under Mars-like conditions. We tested variable pO2, pCO2, temperatures, and starting solutions. The results suggest that Fe is more mobile on Mars than on Earth, with a pseudo-equilibrium concentration that is kinetically controlled by dissolution and oxidation rates. Despite some initially modeled siderite precipitation, no siderite precipitation was observed in the altered powder. Solutions with higher acid concentrations were primarily controlled by dissolution kinetics, with both Fe and Mn being mobile, even when a minor amount of P, Fe, and S bearing secondary phases are formed. Based on our experimental results, we updated the model and conducted two large-scale sensitivity tests on our kinetic simulation. We confirmed that our experiments were too high in pO2 for siderite to form; however, we found that over a range of clearly oxidizing conditions from an equilibrium standpoint, Fe and Mn are mostly mobile, and siderite precipitation can occur. We were able to determine the pO2, pCO2 and the temporal space where Fe-oxide or siderite predominate or coexist, constraining the meaning of reducing or oxidizing conditions. Moreover, we also observed that siderite formation would require a much longer water residence time than Fe-oxide to precipitate, interpreted as higher weathering rates, or later evaporation required to effectively precipitate under any conditions. On ancient Mars, both Fe and Mn would be relatively mobile and prone to be leached from their host rock. Observing siderite or oxide would not primarily be a redox marker but would be a clue to a different hydrological regime. At the planetary scale, it would be challenging to form authigenic siderite during alteration. Although siderite would not indicate the presence of a particularly reducing atmosphere and that Mn-oxides are mainly pH controlled and do not require terrestrial-level amounts of oxygen, a collocated precipitation of siderite and Mn-oxides could also provide valuable information to constrain the redox environment of the ancient Mars.

Participation, assertion and aspiration-indigenous teacher agency in higher education

ABSTRACT Educational research often silences teachers’ voices, distorting perspectives. This article delves into the marginalized voices of educators and administrators, focusing on Indigenous agencies within higher education (HE) in the state of Jharkhand, India. Employing the narrative interview method of oral history, it elucidates life stories through lived experiences amidst conflicting educational paradigms. Drawing from the author’s doctoral research, the article provides a historical and socio-political context of colonial legacies and contemporary challenges faced by Indigenous communities in India self-identifying as ‘Adivasi.’ Synthesizing an analytical framework through a ground-up approach, agency theories uncover new themes within HE’s temporal, spatial, and personal-social space. The findings describe counter-strategies against systemic denial of Indigenous agency within HE, contributing to discussions on the requirement of context-specific epistemic frameworks for India’s Tribal communities to assert their voices and choices in HE.

A learnable continuous wavelet-based multi-branch attentive convolutional neural network for spatio–spectral–temporal EEG signal decoding

Deep learning methods, particularly convolutional neural networks (CNNs), have advanced the field of motor imagery (MI) by effectively extracting spatio-spectral–temporal (SST) features from electroencephalography (EEG) signals. Image-based CNNs using time–frequency analysis such as wavelet transform have outperformed signal-based CNNs in MI-EEG classification. However, the spatial characteristics of EEG signals can be neglected due to the high dimensionality of spectral–temporal feature space. Moreover, the performance hinges on whether the predefined transformation can capture the class-discriminative spectral–temporal representations of EEG signals well. Despite attempts to alleviate these challenges by selecting EEG electrodes or restricting spectral–temporal feature space, large inter-subject variabilities have impeded successful MI-EEG decoding. In this paper, we propose a learnable continuous wavelet-based multi-branch attentive CNN framework for decoding MI-EEG signals. Our method is designed to automatically generate class-discriminative SST representations of EEG signals within subject-specific sub-bands by utilizing learnable wavelet-based convolutions. Additionally, it employs multi-branch attentive CNNs for the effective and efficient extraction of local and global SST features. Our comprehensive evaluation on two public datasets demonstrates that the proposed method significantly outperformed state-of-the-art methods. We also conduct an ablation study under various configuration settings within the proposed framework to demonstrate the effectiveness of our method. The proposed method provides a powerful and innovative approach to MI-EEG signal decoding, with implications for personalized EEG decoding.

A Rare Case of Superficial Temporal Space Abscess Arising from Lower Wisdom Tooth

A Rare Case of Superficial Temporal Space Abscess Arising from Lower Wisdom Tooth

Exploiting Auxiliary Caption for Video Grounding

Video grounding aims to locate a moment of interest matching the given query sentence from an untrimmed video. Previous works ignore the sparsity dilemma in video annotations, which fails to provide the context information between potential events and query sentences in the dataset. In this paper, we contend that exploiting easily available captions which describe general actions, i.e., auxiliary captions defined in our paper, will significantly boost the performance. To this end, we propose an Auxiliary Caption Network (ACNet) for video grounding. Specifically, we first introduce dense video captioning to generate dense captions and then obtain auxiliary captions by Non-Auxiliary Caption Suppression (NACS). To capture the potential information in auxiliary captions, we propose Caption Guided Attention (CGA) project the semantic relations between auxiliary captions and query sentences into temporal space and fuse them into visual representations. Considering the gap between auxiliary captions and ground truth, we propose Asymmetric Cross-modal Contrastive Learning (ACCL) for constructing more negative pairs to maximize cross-modal mutual information. Extensive experiments on three public datasets (i.e., ActivityNet Captions, TACoS and ActivityNet-CG) demonstrate that our method significantly outperforms state-of-the-art methods.