Taking a look at small-scale pedestrians and occluded pedestrians

Small-scale pedestrian detection and occluded pedestrian detection are two challenging tasks. However, most state-of-the-art methods merely handle one single task each time, thus giving rise to relatively poor performance when the two tasks, in practice, are required simultaneously. In this paper, it is found that small-scale pedestrian detection and occluded pedestrian detection actually have a common problem, i.e., an inaccurate location problem. Therefore, solving this problem enables to improve the performance of both tasks. To this end, we pay more attention to the predicted bounding box with worse location precision and extract more contextual information around objects, where two modules (i.e., location bootstrap and semantic transition) are proposed. The location bootstrap is used to reweight regression loss, where the loss of the predicted bounding box far from the corresponding ground-truth is upweighted and the loss of the predicted bounding box near the corresponding ground-truth is downweighted. Additionally, the semantic transition adds more contextual information and relieves semantic inconsistency of the skip-layer fusion. Since the location bootstrap is not used at the test stage and the semantic transition is lightweight, the proposed method does not add many extra computational costs during inference. Experiments on the challenging CityPersons and Caltech datasets show that the proposed method outperforms the state-of-the-art methods on the small-scale pedestrians and occluded pedestrians (e.g., 5.20% and 4.73% improvements on the Caltech)

Photo-excited hot carrier dynamics in hydrogenated amorphous silicon imaged by 4D electron microscopy

Charge carrier dynamics in amorphous semiconductors has been a topic of intense research that has been propelled by modern applications in thin-film solar cells, transistors and optical sensors. Charge transport in these materials differs fundamentally from that in crystalline semiconductors owing to the lack of long-range order and high defect density. Despite the existence of well-established experimental techniques such as photoconductivity time-of-flight and ultrafast optical measurements, many aspects of the dynamics of photo-excited charge carriers in amorphous semiconductors remain poorly understood. Here, we demonstrate direct imaging of carrier dynamics in space and time after photo-excitation in hydrogenated amorphous silicon (a-Si:H) by scanning ultrafast electron microscopy (SUEM). We observe an unexpected regime of fast diffusion immediately after photoexcitation, together with spontaneous electron–hole separation and charge trapping induced by the atomic disorder. Our findings demonstrate the rich dynamics of hot carrier transport in amorphous semiconductors that can be revealed by direct imaging based on SUEM

MUSTANG 3.3 Millimeter Continuum Observations of Class 0 Protostars

We present observations of six Class 0 protostars at 3.3 mm (90 GHz) using the 64-pixel MUSTANG bolometer camera on the 100-m Green Bank Telescope. The 3.3 mm photometry is analyzed along with shorter wavelength observations to derive spectral indices (S_nu ~ nu^alpha) of the measured emission. We utilize previously published dust continuum radiative transfer models to estimate the characteristic dust temperature within the central beam of our observations. We present constraints on the millimeter dust opacity index, beta, between 0.862 mm, 1.25 mm, and 3.3 mm. Beta_mm typically ranges from 1.0 to 2.4 for Class 0 sources. The relative contributions from disk emission and envelope emission are estimated at 3.3 mm. L483 is found to have negligible disk emission at 3.3 mm while L1527 is dominated by disk emission within the central beam. The beta_mm^disk <= 0.8 - 1.4 for L1527 indicates that grain growth is likely occurring in the disk. The photometry presented in this paper may be combined with future interferometric observations of Class 0 envelopes and disks.Comment: 19 pages, 3 figures, AJ accepted, in pres

Analysis on the path of labor identity regression among higher vocational college students in the context of professional attainment education

In order to improve the practical needs of&nbsp;professional attainment education, it is of great significance&nbsp;for higher vocational colleges to train the qualified practical&nbsp;talents who respect labor, care for the grass-roots and have&nbsp;a sense of responsibility in the process of strengthening&nbsp;their labor identity education. Faced with the lack of student&nbsp;labor identity in higher vocational colleges, combined with&nbsp;the Marxist Theory of labor and human rights, this paper&nbsp;proposes the path to realize the regression of labor identity in&nbsp;higher vocational college students, so as to provide reference&nbsp;for further promoting and innovating professional attainment&nbsp;education in higher vocational colleges

Perturbations in the carbon budget of the tropics

The carbon budget of the tropics has been perturbed as a result of human influences. Here, we attempt to construct a ‘bottom-up’ analysis of the biological components of the budget as they are affected by human activities. There are major uncertainties in the extent and carbon content of different vegetation types, the rates of land-use change and forest degradation, but recent developments in satellite remote sensing have gone far towards reducing these uncertainties. Stocks of carbon as biomass in tropical forests and woodlands add up to 271 ± 16 Pg with an even greater quantity of carbon as soil organic matter. Carbon loss from deforestation, degradation, harvesting and peat fires is estimated as 2.01 ± 1.1 Pg annum(−1); while carbon gain from forest and woodland growth is 1.85 ± 0.09 Pg annum(−1). We conclude that tropical lands are on average a small carbon source to the atmosphere, a result that is consistent with the ‘top-down’ result from measurements in the atmosphere. If they were to be conserved, they would be a substantial carbon sink. Release of carbon as carbon dioxide from fossil fuel burning in the tropics is 0.74 Pg annum(−1) or 0.57 MgC person(−1) annum(−1), much lower than the corresponding figures from developed regions of the world

Forecasting the Impact of Product-Harm Events on Firm Value by Leveraging Negative Word of Mouth

Product-harm events are always a nightmare for all stakeholders. Analysts believe that defective items may not only provide risks to the general population, but can likewise cause critical monetary and reputational harm to the firms. Since ignoring a problem does not lead to having it go away, more research is needed to shed new light on the way crisis and risk communication should take place once necessary. Prior study has suggested the complexities of consumer word of mouth effects and how to accurately forecast the impacts of product-harm events on firm value as important subjects. This study extracts the sentiments of consumer complaints in the context of product defects and examines if including consumer sentiment in time series models can improve forecasting performance. Authors make an empirical comparison between two multivariate time series forecasting methods: VAR (vector autoregressive model), and deep learning LSTM (long short-term memory model). Unique datasets, containing five-year data of all automobile nameplates for three major manufacturers in the U.S. are analyzed. The one-step rolling forecast approach is applied to validate time series forecasting values. The results of mean RMSE suggest that LSTM outperforms VAR predictive ability of firm value, and on average obtains 59.02% reduction in error rates when compared with error rates of VAR. It is also noticed that adding consumer sentiment in modeling can improve the predictive performance of both LSTM and VAR models; however, VAR-based models make greater progress in predictive error reduction with consumer sentiment. Implications for marketing research and managerial contributions are discussed

FedBiOT: LLM Local Fine-tuning in Federated Learning without Full Model

Large language models (LLMs) show amazing performance on many domain-specific tasks after fine-tuning with some appropriate data. However, many domain-specific data are privately distributed across multiple owners. Thus, this dilemma raises the interest in how to perform LLM fine-tuning in federated learning (FL). However, confronted with limited computation and communication capacities, FL clients struggle to fine-tune an LLM effectively. To this end, we introduce FedBiOT, a resource-efficient LLM fine-tuning approach to FL. Specifically, our method involves the server generating a compressed LLM and aligning its performance with the full model. Subsequently, the clients fine-tune a lightweight yet important part of the compressed model, referred to as an adapter. Notice that as the server has no access to the private data owned by the clients, the data used for alignment by the server has a different distribution from the one used for fine-tuning by clients. We formulate the problem into a bi-level optimization problem to minimize the negative effect of data discrepancy and derive the updating rules for the server and clients. We conduct extensive experiments on LLaMA-2, empirically showing that the adapter has exceptional performance when reintegrated into the global LLM. The results also indicate that the proposed FedBiOT significantly reduces resource consumption compared to existing benchmarks, all while achieving comparable performance levels.Comment: KDD 202