esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of IMERG Precipitation Products in the Southeast Costal Urban Region of China
## 3 Methods
### Precipitation Intensity Classification Standard  
Evaluation and comparison of the three IMERG products was also performed under different precipitation intensities to obtain a more comprehensive understanding of their performance. To facilitate the metric calculation at different intensities, this work graded the precipitation intensity. On an hourly basis, this study classified precipitation into the six levels of <0.1 mm/h, 0.1-1 mm/h, 1-2.5 mm/h, 2.5-8 mm/h, 8-16 mm/h, and >16 mm/h, corresponding to no/trace rain, tiny rain, light rain, moderate rain, heavy rain, and torrential rain [37]. On a daily basis, precipitation was divided into the six grades of <0.1 mm/d, 0.1-1 mm/d, 1-2 mm/d, 2-10 mm/d, 10-50 mm/d, and >50 mm/d, corresponding to no/trace rain, tiny rain, light rain, moderate rain, heavy rain, and torrential rain [68].
# Evaluation of 5G Positioning Performance Based on UTDoA, AoA and Base-Station Selective Exclusion
## 3 Position Solution and Measurement Exclusion
### Location Solution Based on Time of Arrival
 \tag{21}\]  
is the vector of predicted pseudo-ranges corresponding to the real receiver position \(\mathbf{x}\), \(\mathbf{W}_{\text{RTT}}\) is the weighting matrix formed by the weighting coefficients of the RTT observables as:  
\[\mathbf{W}_{\text{RTT}}= \begin{bmatrix}\sigma_{1}^{2}&0&\cdots&0\\ 0&\sigma_{2}^{2}&\cdots&0\\ \vdots&\vdots&\ddots&\vdots\\ 0&0&\cdots&\sigma_{\text{M}_{\text{BS}}}^{2}\end{bmatrix}  \tag{22}\]  
with \(\sigma_{n}^{2}\) being the variance of the \(n\)-th ranging errors and \(\mathbf{G}_{\text{RTT}}\) is the geometry matrix defined as:  
\[\mathbf{G}_{\text{RTT},(n,1;3)}= \begin{bmatrix}\frac{\mathbf{x}_{\text{BS},(n)} -\hat{\mathbf{x}}}{\parallel\mathbf{x}_{\text{BS},n}-\hat{\mathbf{x}}\parallel }\end{bmatrix} ,for\ 1\leq n<M_{\text{BS}}. \tag{23}\]
# Extracellular Vesicles and Artificial Intelligence: Unique Weapons against Breast Cancer
## 2 EVs in Breast Cancer
### EVs' Involvement in BC Drug Resistance
In particular, the anticancer progression role of EVs was underlined in 2021 through the identification of miR-33-containing BC-derived EVs, which were able to induce M1 polarization in mouse macrophages [85], and the evidence that M1 macrophage-derived EVs cause BC sensitivity to chemotherapeuticutics [86]. Moreover, miRNA-205 is upregulated in tamoxifen-resistant MCF-7/TAMR-1 (M/T) cells and, also, in M/T cell-derived exosomes (M/T-Exo). Exosomal miRNA-205 could confer tamoxifen phenomena and tumorigenesis progress in recipient tumor cells by targeting E2F Transcription Factor 1 (E2F1) in human BC cells (BCCs) [87]. On the other hand, cancer stem cells (CSCs) are able to self-renew and differentiate into diverse cells, resulting in being resistant to chemotherapy; thus, they are a good therapeutic target. CD44+/CD24\(-\) surface markers were found to be minimum biomarkers for breast CSCs (BCSCs), and Her2- and CD44-positive EVs have been associated with tumor recurrence and metastasis [25]. EV-released biomolecules can change the metabolism/physiology of cells, conferring information capable of inducing a drug-resistant phenotype, such as the miR-181 family involved in drug resistance and metastasis progression in BC [88]. As shown in mouse models, CD63(+) cancer-associated fibroblasts (CAFs) confer tamoxifen resistance, and the sequestration of exosomal miR-22 induces overcoming tamoxifen resistance [89]. On the other hand, in an MCF7 xenograft mammary fat pad mouse model, [PERSON] et al. demonstrated the emergence from quiescence of metabolically dormant cells during hormonal treatment thanks to the transfer of exosome-derived mtDNA from CAFs to BC [90]. Previous evidence shows a controversial role for EVs, which, due to their content, are able to promote cancer progression and a drug resistance phenomenon or the inhibition of cancer progression [91; 92; 93; 94; 95; 96; 97; 98; 99; 100]. Based on the evidence that EVs' cargo is fundamental to cellular functions and also influences their responsiveness to drugs, the idea of engineering these nanovesicles into drug vehicles or molecules capable of directing cells towards a phenotype of sensitivity to the chosen treatment is currently a great challenge in precision medicine [101; 102; 103].
## 4 Results
### Datasets
#### 4.1.4 Open Dataset  
Part of the dataset (Group 6) [36] of Konya University, Turkey, was also used for evaluation purposes. This dataset was created by the University of Konya in Turkey using a DJI Phantom 4 Pro drone. The flight took place on 29 June 2018, capturing the campus of the university. The produced dataset consists of 440 files (geo-tagged images), has a total size of 3.59 GB, and constitutes the largest dataset used in our experiments. The size and the area of interest of this dataset are the main reasons for its selection.
# A Hybrid Degradation Modeling and Prognostic Method for the Multi-Modal System
## 2 Degradation Modeling and Prognostic Hybrid
### Long-Term Cumulative Degradation Assessment with Cdd-Hi
#### 2.1.3 Cumulative Degradation Assessment with Gated Recurrent Unit  
The recurrent neural network has good performance when processing arbitrary time-sequences, based on the network structure in which neurons could connect to themselves across time. Because the problem of long-term dependencies, it is hard for the recurrent neural network to learn to store information for very long [35]. To solve the above problems, this paper adopts the gated recurrent unit, which is another recurrent neural network variant, to estimate remaining useful life with a long-term cumulative degradation health indicator.  
In the gated recurrent unit, the gradient disappearance or explosion problem is alleviated by introducing an additive structure of the gate. There are two gates to process data information. The reset gate \(r(t)\) is used to adjust the incorporation of current input with the previous memory. The preservation of the previous memory is controlled by the update gate \(u(t)\). The transition function is formulated as Equations (8) and (9),  
\[u(t)=\sigma(W_{u}H(t)+V_{u}\hat{y}(t-1)+b_{u}), \tag{8}\]  
\[r(t)=\sigma(W_{r}H(t)+V_{r}\hat{y}(t-1)+b_{r}), \tag{9}\]  
where \(W\) and \(V\) represent the weight matrices of \(H(t)\) and \(\hat{y}(t-1)\), respectively, and \(b\) is the bias vector for the update gate and reset gate. Since the reset gate controls the degree of ignoring the information and the update gate controls the impact of the previous information, the output of current status can be represented as Equation (10):  
\[\begin{array}{l}\tilde{y}(t)=\tanh(W_{h}H(t)+V_{h}(r(t)\odot\hat{y}(t-1)))\\ \hat{y}(t)=(1-u(t))\odot\hat{y}(t-1)+U(t)\odot\tilde{y}(t).\end{array} \tag{10}\]  
Regarding the cumulative dynamic differential health indicator \(H(t)\) as input, the corresponding forecast result \(\hat{y}(t)\) could be obtained, which is the long-term cumulative degradation assessment \(\hat{y}(t)_{c}\).
## 3 Materials and Methods
### Learning Models and Training Data Preprocessing
#### 3.4.2 Building a Learning Model  
Traditional RNN-based models suffer from prolonged calculation times due to single-word sequential operation. Launched by Google in 2017, the transformer model addresses this issue by employing attention mechanisms to process entire sentences in parallel, thereby reducing memory and computational demands. This simultaneous processing enhances performance and facilitates efficient training [28]. We used transformer-based BERT and ELECTRA models for the experiments.  
In 2018, Google released a language model pretrained on a large amount of training data. During pretraining, the model learns using the masked language model and next sentence prediction. It demonstrated high performance with the addition of a layer, minimal data, and fine tuning of the training time. This is referred to as transfer learning. To enhance the performance of BERT in a specific field, the collection of language data from that field and additional training are necessary [29]. ELECTRA is more resource-efficient than BERT; thus, it efficiently learns with relatively fewer resources. It improves training efficiency by using a new pretraining task called replaced token detection which allows for the generation of multiple sentences with partially changed words from a sampled sentence. This enhancement is achieved concerning the size of the prepared dataset [30].  
In this study, the experiments were conducted on a server equipped with four NVIDIA RTX A5000 GPUs. PyTorch served as the deep learning framework. KPF-BERT and KR-ELECTRA were used as the BERT and ELECTRA models, respectively.  
KPF-BERT is a BERT model released by the Korea Press Foundation (KPF), trained on over 40 million articles from 20 years of Bigkinds data [31]. The Computational Linguistics Lab at Seoul National University released the KR-ELECTRA model after training it on 34 GB of Korean text data, including Wikipedia articles, news, legal texts, news comments, and product reviews [32].  
We performed tokenization for the KPF-BERT and KR-ELECTRA models using the WordPiece tokenizer, a subword tokenizer. The vocabulary size was 36,439 for KPF-BERT and 30,000 for KR-ELECTRA. For the experiments, we used the AdamW optimizer, a learning rate of \(5\times 10^{-5}\), 30 training epochs, and batch sizes of 4 and 24. We also set the maximum sequence length to 128 and 256 tokens when training on a sentence and dialog, respectively.
## 4 Mathematical Model
### Governing Equations  
To simplify the calculations, the system was assumed to flow isothermally, the liquid was incompressible, and the particles were spherical with a single size. The model regarded the liquid-solid two phases as a continuous medium filled with flow field and mutual interaction, and the Reynolds-averaged mass and momentum conservation equations of each phase were solved. The governing equation [20] is:  
Continuity Equation:  
\[\frac{\partial}{\partial t}(\rho_{l}\dot{\varphi}_{l})+\
abla\cdot(\rho_{l} \varphi_{i}\dddot{u}_{i})=0 \tag{2}\]  
where i represents the liquid phase (\(L\)) or solid phase (\(S\)); \(\rho\) is the density; \(\varphi\) is the phase volume fraction; and \(\dddot{u}\) is the velocity vector. The momentum conservation equation:  
The momentum conservation equation for the liquid phase is:  
\[\frac{\partial}{\partial t}(\varphi_{L}\dddot{u}_{L})+\
abla\cdot(\varphi_{L }\rho_{L}\dddot{u}_{L}\dddot{u}_{L}) =-\varphi_{L}\
abla P+\varphi_{L}\
abla\cdot\dddot{\tau}_{L}+K( \dddot{u}_{S}-\dddot{u}_{L})+\varphi_{L}\rho_{L}g \tag{3}\]  
\[\dddot{\tau}_{L} =\varphi_{L}\mu_{L}(\
abla\dddot{u}_{L}+\
abla\dddot{u}_{L}^{T})+ \varphi_{L}(\lambda_{L}-\frac{2}{3}\mu_{L})\
abla\cdot\dddot{u}_{L} \tag{4}\]  
The momentum conservation equation for the solid phase is:  
\[\frac{\partial}{\partial t}(\varphi_{S}\rho_{S}\dddot{u}_{S})+ \
abla\cdot(\varphi_{S}\rho_{S}\dddot{u}_{S}\dddot{u}_{S}) =-\varphi_{S}\
abla P-\
abla P_{S}+\
abla\cdot\dddot{\tau}_{S}+K (\dddot{u}_{L}-\dddot{u}_{S})+\varphi_{S}\rho_{S}g \tag{5}\] \[\dddot{\tau}_{S} =\varphi_{S}\mu_{S}(\
abla\dddot{u}_{S}+\
abla\dddot{u}_{S}^{T})+ \varphi_{S}(\lambda_{S}-\frac{2}{3}\mu_{S})\
abla\cdot\dddot{u}_{S} \tag{6}\] \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Symbol** & **Value** \\ \hline Liquid density & \(\rho_{L}\) & 1150 kg/m\({}^{3}\) \\ Hydrodynamic viscosity & \(\mu\) & 0.001 pa-s \\ Solid particle density & \(\rho_{S}\) & 2485 kg/m\({}^{3}\) \\ Solid particle diameter & \(d_{S}\) & 3 mm \\ Solid volume fraction & \(\varphi_{S}\) & 5.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The physical parameters. \[p_{S}=\varphi_{S}\rho_{S}\theta_{S}+2\rho_{S}(1+e_{SS})\varphi_{S}^{2}g_{0,SS} \theta_{S} \tag{7}\]  
\[g_{0,SS}=\left[1-\left(\frac{\varphi_{S}}{\varphi_{S,\max}}\right)^{1/3}\right]^ {-1} \tag{8}\]  
where \(p\) is the thermodynamic pressure; \(p_{S}\) is the solid phase pressure; \(K\) is the drag coefficient between the solid and liquid phases, calculated from the resistance coefficient model; \(\overset{=}{r_{L}}\) and \(\overset{=}{\pi}_{S}\) are the liquid and solid phase shear stress tensor, respectively; \(\theta_{S}\) is the granular temperature for the solid phase; \(\lambda_{S}\) and \(\mu_{S}\) are the particle phase bulk viscosity and shear viscosity, respectively; \(e_{SS}\) is the inelastic collision restitution coefficient of particles; \(g_{0,SS}\) is the particle radial distribution function; and \(\varphi_{S,\max}\) is the maximum particle packing. [PERSON] et al.
## 4 Experimental Results and Discussion
### Subjective Evaluation
#### 4.5.2 Experimental Environment and Participants  
In the experiment, the stereoscopic projection system was adopted, and the participants needed to wear polarized glasses, which separate the left and right views to the appropriate eyes. The system consisted of two projectors (BenQ PB8250 DLP), DELL real-time 3D graphics workstations, a polarized light bracket, and a metal screen (150 inches). The experiment was conducted in a specific laboratory, in which the illumination, temperature, and other experimental conditions followed ITU-R Recommendation BT.500.  
A total of 20 participants were involved in the study, with an average age of 23 years, and all of the participants underwent the color vision test and met a 20:30 visual acuity test and a stereoscopic visual acuity test at 40 s-arc. They were non-experts whose professional backgrounds are not directly related to image quality. Each participant, needed to score 30 pairs of stereoscopic images. Each pair cost 40 s on display, 10 s on scoring, and 10 s on resting. According to ITU Recommendation BT.500, the display order of the 30 pairs was random. The distance between the participants and the screen was 3 m; that is, three times the height of the screen. In addition, to make the participants be familiar with the scoring process, four other pairs of stereoscopic images were displayed to them before the official scoring.
## 6 Design of the Proposed MPPT Technique
### Identification Mechanism of CCM/DCM  
When the boost converter operates in CCM mode, the volt-second product of the inductor current during \(T_{on}\) and \(T_{off}\) is the same, leading to the voltage relation presented in Equation (7):  
\[V_{o}=\frac{V_{pv}}{1-D}. \tag{7}\]  
This relation is rearranged in Equation (8), where \(V_{pv}\) represents the input voltage when the PV array is connected to the input of boost converter:  
\[V_{pv}=V_{o}\times(1-D). \tag{8}\]  
When the converter enters DCM, the voltage transfer relation presented in (7) becomes invalid. The converter now starts working with the different voltage relation of DCM, as expressed in Equation (9):  
\[V_{o}=V_{pv}\times\sqrt{\frac{k\times R_{L}\times t_{on}}{2L}} \tag{9}\]  
where \(k\) is the dead-time period during which the inductor current becomes zero and \(R_{L}\) is the load resistance.  
Figure 6: I–V curves at different irradiance conditions at 50 \({}^{\circ}\)C with inductor designed at (a) 40% of \(I_{mpp}\), (b) 20% of \(I_{mpp}\), and (c) 10% of \(I_{mpp}\).  
Note that the MPP embedded system contains the 'D' value, while the \(V_{pv}\) and \(V_{o}\) values are taken from sensors. During every iteration, the proposed algorithm first estimates \(V_{pv(CAL)}\) using Equation (10):  
\[V_{pv(CAL)}=V_{o}\times(1-D) \tag{10}\]  
where the \(D\) and \(V_{o}\) values are taken from the memory of the embedded system. Thereafter, this value is compares with the \(V_{pv}\) value measured by the sensor. If the \(V_{pv}\) and \(V_{pv(CAL)}\) values match (with a degree of tolerance \(\eta\) to account for sensor sensitivity), this means that algorithm is in CCM, as it is working with the CCM relation. When \(V_{pv}\) does not match the \(V_{pv(CAL)}\), this implies that algorithm is in DCM. As a corrective measure, the algorithm then triggers a sizeable negative voltage step such that PV array moves towards the MPP or current region and the converter starts operating in CCM again.
## 3 Flexural Capacity Prediction Methods and Applicability Analysis
### Proposals for the Flexural Capacity Prediction Equation
The values of \(\alpha\) range from 1.5 to 3.0, and the effects of loading types are not considered. Table 3 shows the predictions of Equation (10) with different \(\alpha\) values. It can be seen that the mean value index of flexural capacity predictions Mean(Pre./FE) is close to one, and the variability is acceptable. Therefore, \(\alpha=2.1\) is recommended. The comparisons between the predictions and analysis results are shown in Figure 14.  
In summary, we recommend Equation (11) for predicting tendon stress increment in PSCB-IUCFRP. According to the schematic diagram in Figure 10, the axial force equilibrium can be expressed as Equation (12). The ultimate neutral axis height \(c_{\rm u}\) can be calculated with a combination of Equations (11) and (12), and \(\Delta\sigma_{\rm p}\) can also be predicted. The flexural capacity can be calculated by Equation (13). \[\Delta\sigma_{\rm p}==\frac{2.1d_{\rm p}}{L_{0}}E_{\rm p}\varepsilon_{\rm cu} \big{(}d_{\rm p}/c_{\rm u}-1\big{)} \tag{11}\]  
\[ \begin{array}{cc}0.85f_{\rm c}b_{\rm w}\beta c_{\rm u}+0.85f_{\rm c}(b-b_{ \rm w})h_{\rm f}=A_{\rm p}\big{(}\sigma_{\rm pe}+\Delta\sigma_{\rm p}\big{)}& c_{\rm u}>h_{f}\\ 0.85f_{\rm c}b\beta c_{\rm u}=A_{\rm p}\big{(}\sigma_{\rm pe}+\Delta\sigma_{ \rm p}\big{)}&c_{\rm u}\leq h_{f}\end{array}  \tag{12}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{\(\alpha\)} & \multicolumn{2}{c}{**Tendon Stress Increment**} & \multicolumn{2}{c}{**Flexural Capacity**} \\ \cline{2-5}  & **Mean (Pre./FE)** & **COV (Pre./FE)** & **Mean (Pre./FE)** & **COV (Pre./FE)** \\ \hline
1.5 & 0.733 & 0.101 & 0.942 & 0.019 \\
1.6 & 0.772 & 0.100 & 0.953 & 0.017 \\
1.7 & 0.810 & 0.098 & 0.963 & 0.016 \\
1.8 & 0.847 & 0.096 & 0.973 & 0.015 \\
1.9 & 0.884 & 0.095 & 0.983 & 0.015 \\
2.0 & 0.920\[ \begin{split} M_{\text{u}}=A_{\text{p}}\big{(}\sigma_{\text{p}_{\text{e}}}+ \Delta\sigma_{\text{p}}\big{)}\big{(}d_{\text{p}}-\beta c_{\text{u}}/2\big{)}+ 0.85f_{\text{c}}(b-b_{\text{w}})\big{(}\beta c_{\text{u}}-h_{\text{f}}\big{)}h_ {\text{f}}/2&c_{\text{u}}>h_{\text{f}}\\ M_{\text{u}}=A_{\text{p}}\big{(}\sigma_{\text{pe}}+\Delta\sigma_{ \text{p}}\big{)}\big{(}d_{\text{p}}-\beta c_{\text{u}}/2\big{)}&c_{ \text{u}}\leq h_{\text{f}}\end{split}  \tag{13}\]  
The experimental results of specimens BC1 and BC2 reported by [PERSON] et al. are employed here to verify the proposed equations. The predictions made by Equations (2), (3), and (6) are listed for comparison, as shown in Table 4. The predictions by ACI 318-14 [40] and AASHTO LRFD [50] are overly conservative, and ACI 440.4R gives overestimated results. The predictions from the proposed equation show good agreement with the test results. The mean values of the ratio of predictions to experiments are 1.02 and 0.993 for tendon stress increment and flexural capacity, respectively.
## 2 Results
### Correlation of mRNA and Protein Profiles  
A correlation analysis was conducted between the transcriptomic and proteomic data. Correlation analysis indicated a low correlation coefficient (R = 0.390, \(p\) < 0.001) between the expression levels of all quantified proteins and their corresponding mRNAs. However, an about 1.4-fold correlation was revealed between the DEPs and their corresponding mRNAs (R = 0.528, \(p\) < 0.001) (Figure 5), suggesting a certain degree of the biological relevance of protein and mRNA changes in relation to the resin yield.  
Overall, 3811 DEGs, and 1396 DEPs, were identified in needles. Among these DEGs and DEPs, 411 were commonly regulated both at the mRNA and protein levels. Of these, 361 were regulated with the same trend, suggesting that the expression changes of these proteins are mainly controlled by transcriptional alterations. However, there were a few genes, that were changed with the opposite trend at the transcriptional and proteomic levels, including acetyl-CoA C-acetyltransferase (_AACT_) involved in terpenoid backbone biosynthesis, linoleic 9S-lipoxygenase (_LOX1_) related to linoleic acid metabolism, ribose 5-phosphate isomerase A (_RPIA_) and phosphoserine phosphatase (_PSPH_) associated with biosynthesis of amino acids, and 25.3 kDa heat shock protein (_HSP25.3_) referred to protein processing in the endoplasmic reticulum (Table S2), suggesting the involvement of post-transcriptional regulation for these genes/proteins. Therefore, the integrative analysis of  
Figure 5: Correlation between the expression of proteins and mRNAs. (**A**) Correlation of all quantified proteins and their corresponding mRNAs. (**B**) Correlation of DEPs and their corresponding mRNAs. “R” represents the correlation coefficient calculated by the Pearson correlation test, and “FC” represents fold change.  
proteome and transcriptome would provide more information on the genes involved in the resin yield in masson pine.
# Discrete Semantics-Guided Asymmetric Hashing for Large-Scale Multimedia Retrieval
## 4 Experiments
### Further Study
#### 4.5.5 Effects of Deep Learning Based Representation  
More recently, deep neural networks have achieved promising performance in the field of representation learning. To validate the effectiveness of DSAH, we conduct experiments on  
\begin{table}
\begin{tabular}{c l l l l l l} \hline \hline
**Task** & **Method** & **8 Bits** & **16 Bits** & **32 Bits** & **64 Bits** & **128 Bits** \\ \hline \multirow{2}{*}{I\(\rightarrow\)T} & DSAH & **0.6231** & 0.6432 & 0.6517 & **0.6667** & **0.6791** \\  & DSAH-BERT & 0.6212 & **0.6491** & **0.6521** & 0.6634 & 0.6747 \\ \hline \multirow{2}{*}{T\(\rightarrow\)I} & DSAH & **0.7324** & **0.7596** & 0.7756 & 0.7834 & 0.8024 \\  & DSAH-BERT & 0.7311 & 0.7558 & **0.7801** & **0.7907** & **0.8071** \\ \hline \hline \end{tabular}
\end{table}
Table 6: mAP scores on NUS-WIDE dataset using BERT and bag-of-word embeddings of DSAH.  
\begin{table}
\begin{tabular}{c l l l l l l} \hline \hline
**Task** & **Method** & **8 Bits** & **16 Bits** & **32 Bits** & **64 Bits** & **128 Bits** \\ \hline \multirow{4}{*}{I\(\rightarrow\)T} & DSAH-Re & 0.4558 & 0.4611 & 0.4732 & 0.4798 & 0.4832 \\  & DSAH-Ke & 0.5842 & 0.5911 & 0.6287 & 0.6334 & 0.6499 \\  & DSAH-Nm & 0.6069 & 0.6093 & 0.6421 & 0.6429 & 0.6551 \\  & DSAH & **0.6231** & **0.6432** & **0.6517** & **0.6667** & **0.6791** \\ \hline \multirow{4}{*}{T\(\rightarrow\)I} & DSAH-Re & 0.4568 & 0.4650 & 0.4717 & 0.4823 & 0.4804 \\  & DSAH-Ke & 0.7287 & 0.7452 & 0.7676 & 0.7704 & 0.7799 \\ \cline{1-1}  & DSAH-Nm & 0.7225 & 0.7407 & 0.7589 & 0.7621 & 0.7697 \\ \cline{1-1}  & DSAH & **0.7324** & **0.7596** & **0.7756** & **0.7834** & **0.8024** \\ \hline \hline \end{tabular}
\end{table}
Table 5: mAP scores of different ablated versions of DSAH on NUS-WIDE dataset.  
NUS-WIDE dataset to evaluate the effects of deep learning based representations. The corresponding variant of DSAHis named DSAH-Deep. Specifically, each image is represented as a 4096-D vector extracted by the fc7-layer of VGG-16 net [59]. The mAP scores on NUS-WIDE dataset with the code length varying from 8 bits to 128 bits are shown in Table 7. From the table, we can observe that the use of deep learning based representation for cross-modal retrieval improves the accuracy of retrieving text through images, but reduces the accuracy of retrieving images through text. The reason may be that deep learning-based representation improves the semantics of the image representation, but the difficulty of retrieving images is increased due to the increase of the dimensionality simultaneously.