dataesr/scientific-paragraphs-categorization huggingface dataset:
A. CSI-based node authentication In Fig. 8 , for the Quadriga dataset, the average TVD between the first D PCs at any Alice and any of her neighbours is depicted. We observe that D = 1 results in the largest value of TVD, while the point D = 0 corresponds to the original measurements. With an increase in the SNR, there is a slight increase in the TVD; with a decrease in noise, the variance of the first PCs increases, and hence the TVD decreases. To showcase the impact of increasing TVD, in Fig. 9 , we show the variation of the amplitude of the original CSI vs. time and that of the first PC vs. time for four neighbouring Alices. We observe that when compared to the original signal in Fig. 9(a) , the time series corresponding to the first PC in Fig. 9 (b) are clearly distinguishable. Similar results are shown for the Nokia dataset in Fig.
10  and 11 , for which, notably, the increase in the separability is more accentuated. To provide a first validation of the proposed pre-processing, we compare the performance of CSI authentication for the original signal, the first principal component (representing large-scale fading), and the residuals (representing small-scale fading), using the NOKIA dataset. For the evaluation, we  employed a binary √ M -nearest neighbour (NN) classifier (based on the common Euclidean distance) to distinguish between track 6 and track 12  [44]. We considered several sample sizes M = {250, 500, 1000, 2000, 4000} and utilized 70% of each dataset for training and 30% for testing. The results in Fig. 12  clearly demonstrate the superior performance of CSI-based authentication using the first PC (large-scale fading) compared to the original CSI and the small-scale fading  3.
Specifically, for sample sizes up to 1000, the large-scale fading provides an average increase in classification accuracy and f1-score of around 6% and 15% compared to the original CSI and small-scale fading. It is important to note that for larger sample sizes (M = 2000, 4000), the classification performance gap between the large-scale fading and the original channel is lower (up to an average of 5%). This is because the √ M -NN classifier struggles to efficiently distinguish between tracks 6 and 12 due to their close proximity (1 meter). However, even at such larger sample sizes, the large-scale fading is superior to small-scale fading in terms of accuracy and f1-score. We also evaluated the performance of CSI-based authentication using the Quadriga dataset, following the same experimental setup as before, with sample size M = 256 (total  number of snapshots per location).
The results, presented in Table  II , demonstrate that accuracy is significantly improved when keeping only the first PC compared to the original CSI and the small-scale fading. On average, when keeping only the first PC leads to a 20% higher classification accuracy and a 15% higher f1-score when distinguishing between two neighbouring Alices. The table provides an overview of the average √ M -NN classification performance in terms of accuracy and f1-score for each pair of neighbooring Alices in the Quadriga dataset at an SNR of 20 dB. The predictable component achieves an accuracy of 0. 793 and an f1-score of 0. 7582, outperforming the original channel (accuracy: 0. 5629, f1-score: 0. 5901) and the unpredictable component (accuracy:.
B. Secret key generation 1) PCA: First, we study the effect of pre-processing using PCA for SNR = 20 dB in Fig. 13 , starting with the Quadriga dataset. Figs. 13(a) , and (b) illustrate the variation of two metrics: i) the average CC between the locations and their nearest neighbours; and ii) the average MP between the Alices and Bob, respectively, with respect to the variation in the pair { D1 , D2 } in steps of 2. With no pre-processing, the average CC is approximately 0. 30. However, with a sufficient number of dimensions D1 -D2 retained, an increase in the number of dimensions omitted D1 -1 results in a decrease in the CC. Specifically, for D1 = 2 and D2 = 20, we observe a drop in the CC to 0. 18, with no significant increase in MP. We posit that this range of PCs captures sufficiently well small scale fading terms.
A trend similar to CC is observed for ∆ in Fig. 15 , especially for higher values of D1 indicating likely independence. On the other hand, ∆ for D1 < 8 does not follow the CC drop, indicating the limitations of CC compared to ∆ to capture dependence. For example, omitting the first 6 PCs may guarantee a low CC but not a significant decrease in ∆ and statistical independence. The impact of the observation vector length on ∆ will be investigated in detail in future work. Next, we present results for the Nokia dataset starting with SNR = 20 dB in Fig. 16. With no pre-processing, the average CC is approximately 0. 38. However, with a sufficient  number of dimensions retained, an increase in the number of dimensions omitted decreases the CC. Specifically, for D1 = 6 and D2 = 30, we observe a drop in the CC to 0.
15, with no significant increase in MP. As in the Quadriga dataset, we posit that this is the regime in which the predominant large scale predictable components have been removed, and the small scale fading components have been retained. Importantly, a trend similar to CC is observed in the average ∆ in Fig. 17 , especially for D1 ≥ 4 after which value the dependence level collapses. Finally, similarly to Quadriga, for a low SNR=5 dB, any pre-processing would induce high MP and is therefore advised to be omitted. 2) KPCA: Next, we evaluate the performance of KPCA. Recall that, in this case, we only apply the parameter D to derive the residuals that will be used as SKG seeds. Tables III and IV indicate that an increase in D leads to lower values of ∆ and higher values of MP. More precisely, focusing on Table  III , ∆ undergoes a significant decrease for D ⩾ 2, leading to a slight increase in MP.
Moreover, comparing the outcomes of the PCA and the KPCA, KPCA seems to lead to a "faster" decrease of ∆. The results provided in Table IV concern the NOKIA dataset and similarly show a significant decrease in ∆; overall, compared to PCA, KPCA seems slightly more efficient in decreasing dependencies. 3) AE: The layers and the activation function of the AE are given in Table  V  and follow  [45]. For brevity, the AE with  MSE loss function is referred to as AE1 and that with dotproduct loss function is referred to as AE2. The input to the AE2 is formed by grouping the 200 × 1 CSI vector (100 real and 100 imaginary) of each spatial location with 200 × 1 long CSI vector from each of the 8 nearest neighbours surrounding the location. In other words, the dimension at the input and the output is 400 × 1.
This ensures that the loss function can minimize the correlation between the users while minimizing the reconstruction error between the input and the output. From Tables VI and VII, note that, as in the case of PCA, the lower the SNR, the lower the CC and the higher the MP. Moreover, with an increase in the encoding dimensions D, the AE has more freedom to represent the predictable components. Therefore, with an increase in D, we observe a drop in the CC. For the Quadriga dataset, AE2 achieves a CC of 0. Also, the ∆ of the residuals shows a significant decrease from that of the original components, especially for SNR= 20 dB. Observe that in AE2, since the loss function is the dot product between residuals instead of the MSE, we can observe a significant drop in CC of the residuals for AE2 compared to AE1.
However, this is accompanied by an increase in the MP, especially for localised training. Also, as expected, centralised training results in a much lower MP when compared to localised training. A very similar trend is observable in the case of the Nokia dataset also. In this case, the residual CC for D = 8 and SNR= 20 dB, is much lower than what PCA attains for the same data set for D1 = 6 and D2 = 30.
This architecture is based on the ResNet convolution and allows weights learned from a ResNet model to be transferred to another ResNet model as well as selective training of layers. The performance of LatentCADx using a publicly available dataset of 2,620 mammogram case files reached 0. 97 AUROC. In addition, qualitative evaluation of the segmentation showed great clarity and specificity with weakly-supervised segmentation, which ensures the interpretability of the model. In Fujihara et al. , a research team from Japan developed a ML model to predict changes in future weight in the subsequent 3 years using 67,021 Japanese individuals who had initial health examinations at the Niigata Association of Occupational Health. The predictors include body mass index, smoking and alcohol intake status, exercise habit, skipping breakfast, walking speed, eating speed, and other medical conditions such as history of dyslipidemia, diabetes, and hypertension.
Funding ZHe was funded in part by the  National Institutes of Health (NIH) ,  National Library of Medicine  grant  5R21LM013911-02 ,  NIH National Institute on Aging  grants  3R21AG061431-02S1 ,  5R21AG061431-02 , and  5R01AG064529-05 ,  NIH National Institute on Alcohol Abuse and Alcoholism  grant  5P01AA029547-02 , and  US Environmental Protection Agency  grant  84063201. RZ was funded in part by the  National Center for Complementary and Integrative Health (NCCIH)  under grant number  2R01AT009457  and  NIA  under grant number  1R01AG078154 , and  National Cancer Institute  under grant number  1R01CA287413.
We tested two techniques to learn the temporal aspect on the latent space, namely vector quantized variational autoencoders  [47]  and variational recurrent neural network  [12]  but these approaches were not successful. We therefore turned to a data free approach, relying on reinforcement learning algorithms to construct trajectories following the dynamics introduced in (2), while guiding it to transition from one well to another one. Reinforcement learning is more convenient than generative approaches learning from a dataset when the construction of the data set is computationally challenging. This work is organized as follows. We introduce the main settings of the molecular dynamics problem we tackle in Section 2. In Section 3, we briefly present variational autoencoders, and the methods used to learn the temporal aspect on the latent space.
, G T -1 ) over 100 randomly chosen trajectories are plotted in Figure  5. The distribution of the values of G for the generated trajectories are much narrower than the corresponding distribution for the trajectories from the training dataset, which corresponds to a standard Gaussian distribution. The analysis could be refined here by computing the path action, as considered in  [19]  to assess the relevance of generated trajectories. The path action is proportional to the sum G 2 0 + • • • + G 2 T -1 , which, unsurprisingly here in view of Figure  5 , turns out to have untypical values (too small). With the VAE architecture we consider, a complete trajectory is encoded in the very low dimensional space R 2 , which is too small to account for the fluctuations of the whole trajectory, and can at best reproduce the average shape of trajectories relating two metastable states.
As the network Q evaluates the actor, it is called a "critic", and the whole learning procedure forms an ensemble called the "actor-critic setting". From a mathematical viewpoint, one aims at solving the following optimization problem: ω * = arg max ω N i=1 Q(q i , P(q i , ω), θ * ), (25) θ * = arg min θ N i=1 Q(q i , P(q i , ω * ), θ) -r P(qi,ω * ) (q i+1 , q i ) + γ max a∈A Q(q i+1 , a, θ) 2. ( 26 ) Note that the policy network implicitly appears in  (26)  through the generation of the dataset. The maximizations in (  25 )-(  26 ) are more challenging than the corresponding maximization problem (24) for finite action spaces. Naive strategies are known to be ill behaved, leading notably to overconfident predictions on the Q networks (i. e. the resulting approximation of the Q-function gives values higher than the actual ones).
Numerical results We trained neural networks using the TD3 Algorithm  [18]  to generate parameters (θ f , ω f ) which are approximations of the optimal parameters (θ * , ω * ) in (  25 )-  (26). We use the open source code provided by the authors of  [18] , available at https://github. com/sfujim/TD3. We fix ∆t = 5 × 10 -3 and α = 0. 071. Precise information on hyper parameters and network architectures can be found in Appendices B and C. The final actor network obtained is depicted in Figure  8  where we plot the final policy P(q, ω f ) alongside with the final value function. We can see that the drift corresponding to the policy network biases the trajectories towards the saddle point of the potential in the vicinity of (0, 1). It does not bias the trajectories towards the saddle point around (0, -0.
Discussion and perspectives The results presented in this work suggest that reinforcement learning provides a way to sample transition paths by finding some biasing force field guiding the system in its excursion out of A and into B. Approaches based on generative methods such as variational autoencoders are intrinsically more limited, especially given that they need a database of transition paths to start with. Even when such a database is available, the generation of new paths may be cumbersome as this requires a large latent space with some structure to reproduce the temporal organization of the components of the latent variables. While writing up this work, we became aware of recent works in the computational statistical physics community making use of reinforcement learning and neural networks to construct effective biases and favor otherwise unlikely transitions, which is particularly useful when computing large deviations rate functionals  [9, 14, 19, 40, 50, 51].
• dataset maximum size: 30, 000 • number of games: 50, 000 • batch size: 512 • number of rounds per game T = 600 • train periodicity (in rounds per game): d = 100 • exploration noise: 0. 1 • ∆t = 5 × 10 -3 • Probability of random step decay coefficient: 0. 99 • Random step decay period (in rounds per game): 2000.
Value of the data • The dataset contains 320 instances for the Hop-Constrained Minimum Spanning Tree problem and other 320 instances for the Delay-Constrained Minimum Spanning Tree problem. These instances can be used as benchmark for these problems or for their biobjective variant and were used as benchmark instances in  [3]. • The dataset also contains the algorithm employed to generate the benchmark instances. This algorithm was developed in C ++ and contain 5 parameters that can be used to customize the generated instance. • There exists 4. csv files containing the raw experimental results of  [3]. These. csv files were employed to generate Tables  1  and 2  and Figs. 4  and 5 of the paper  [3]. • The instances can be used to evaluate algorithms for the Hop-Constrained Minimum Spanning Tree problem, the Delay-Constrained Minimum Spanning Tree problem, or their biobjective variants.
Data description The dataset is divided into three directories, as presented in Fig. 1. Directory "Instances generator" contains the instance generator algorithm developed in C ++ , directory "Instances" contains the benchmark instances generated by the above-mentioned generator algorithm and employed in  [3]  , and directory "Results" contains the raw data used to construct Tables  1  and 2  and Figs. 4 and 5 of  [3]. These directories and their files will be presented in details below.
cpp -o generator. Other compilers, such as clang or Intel C ++ compiler , can also be used. The compiled algorithm makes use of five parameters, as shown in Table  1. They need to be passed to the algorithm in the exact order they are displayed in Table  1. For example, in Linux environments, one could run the algorithm using the command. /generator 20 1 3 2 100 , whereas 20 represents the number of nodes in the instance, 1 denotes the instance's topology, 3 is the seed for the Mersenne Twister  [ 6 ]  pseudo-random number generator algorithm, 2 represents the chosen problem, and 100 is the maximum allowed delay for the DCMST instance (or B-DCMST instance). In the case that the parameters are not properly passed to the algorithm, a help message will be displayed for the user. Every parameter has a predefined set of allowed values: • size : it should be a natural number greater than 2.
When problem is set to 1, this parameter will be discarded. The generated instance will be a complete undirected graph. Every node (except the root) is randomly placed into a 40 × 40 plane using an uniform distribution. Furthermore, the edges' weights are computed as the Euclidean distance between the nodes. For the DCMST (or B-DCMST) instances, the delay associated to every edge is computed randomly using an uniform distribution. The benchmark instance data is displayed for the user in the default output employed by the user. The benchmark instance data is easy-to-read and to understand. In the first line, it displays, respectively, the number of nodes and the number of edges of the instance. Then, every remaining line denotes an edge. The first and second columns give the endpoints of the edge, while the third column shows the edge's weight.
Experimental design, materials and methods The benchmark instances contained in directory "Instances" were artificially generated using the C ++ algorithm give in this dataset. The data of the. csv files of directory "Results" were obtained using two different implementations of the Augmented ε-constraints algorithm (AUG)  [ 7 ]. Each. csv file give the result of a different implementation of AUG for a different problem. The raw data in files "B-HCMST-cost. csv" and "B-HCMST-hop. csv" were employed to generate Table  1  of  [3]  , while the raw data in files "B-DCMST-cost. csv" and "B-DCMST-delay. csv" were used as source for computing Table  2  of  [3]. The raw data of these files were also employed to plot Figs. 4 and 5 of  [3].
Acknowledgments This study was financed in part by the  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brasil (CAPES) -Finance   Code 001 , the  Conselho Nacional de Desenvolvimento Científico e Tecnológico -Brasil (CNPq) , and the  Fundação de Amparo à Pesquisa do Estado de Minas Gerais -Brasil (FAPEMIG).
Experiment 3: Visual search in natural scenes. Experiment 3 tested visual search performance of participants looking for a pedestrian in an urban landscape, using stimuli developed by Ehinger et al. 24. Since the database, including participants' eye movement records, was made available online by the authors, we were able to make detailed comparisons between visual search results obtained with the digit-tracking method and standard eye-tracking. Exploration distance and response time. Natural scenes generated more visual exploration than search arrays and allowed us to test the effects of several variables, notably the effects of target detectability and blur level (as defined by the standard deviation of the Gaussian blur filter). In Experiment 3a, we tested the impact of a large range of blur levels on search performance.
Ten trials of 18-item arrays were performed and search performance was compared to the corresponding trials in Experiment 1c in order to test the effect of distractor heterogeneity. In Experiment 1e, participants saw 18-item arrays of colored letters and needed to report the identity of the color singleton in the display. The task comprised 21 trials, ten of which contained the same target color as on the previous trial to test for priming effect on search time. In all these tasks, the trial ended when the participant pressed the appropriate key on the keyboard to report either target presence/absence or target identity (T or L). Stimuli in Experiment 2 were arrays of real-world objects and animals developed by Cimminella et al. 23  using images selected from the Bank of Standardized Stimuli (BOSS) database  46.
We used a subset of 96 7-item arrays (64 target-present arrays and 32 target-absent arrays). In half of the target-present arrays, targets and distractors were semantically related and, in another half, they were semantically unrelated. Since all participants in this experiment were Chinese, we validated the manipulation of Chinese semantic relatedness using sematch library in Python. Applying a knowledge-based approach  47  , we confirmed that semantic similarity between targets and distractors was significantly higher in semantically related than unrelated arrays, t(191) = 10. 422, p < 0. 001, d = 0,75, 95% CI [0. 175, inf]. Figure  2b  shows examples of a semantically related array and a semantically unrelated array. Objects could be used repeatedly, so we separated target-present arrays into two groups of 32 arrays which didn't share any object.
Stimuli in Experiments 3a and 3b were selected from a picture database used in Ehinger et al. 24. This database was published online, including eye-tracking data recorded during a visual search task using these pictures. We selected a subset of the pictures which had to meet two criteria: small standard deviation of reaction time and accuracy over 90 percent, assuring that observers' performance on these images was homogeneous. Experiment 3a included two within-subject variables: blur level and target detectability. Blur level ranged from σ = 0. 13° to 3. 9° in steps of 0. 13° (sigma value of the Gaussian blur filter), yielding thirty levels. Ninety pictures were classified into easy, medium and hard levels of target detectability based on the average response times reported in the published database.
g. , the finger sliding past the undetected target). Response time for detected targets was computed as the time elapsed between the stimulus onset and the time at which the finger settled on the target. Exploration distance was computed as the cumulative travel distance between the first finger contact on the display surface and the time at which the finger settled on the target. In Experiment 3b, 20 pictures were selected respecting the two criteria mentioned above. We chose five relevant blur levels (i. e. 0. 13/0. 26/0. 52/0. 91/1. 43 degree) based on the results of Experiment 3a and assigned them to different pictures across subjects in a Latin square way, assuring that we got 15 data samples for each picture at each level. We used the same procedure as in Experiment 3a except that there was no dwell time requirement.
Participants were instructed to explore the image and a trial ended when the participant pressed the keyboard or the exploration distance reached 70° of visual angle. This modification to the procedure between Experiments 3a and 3b was motivated by the unexpected presence of invalidly terminated trials in Experiment 3a. Indeed, some participants reported that the trial did not end even though they had found the target, most likely because they stopped exploring while the finger was still slightly away from the target. When this happened, they adopted the implicit strategy of terminating stimulus presentation by reaching the maximum travel distance with random movements and then pressing the "target detected" response key. Since one of the main objectives of Experiment 3b was to analyze attention map profiles, we wished to collect digit-tracking data uncontaminated by such non-exploratory movements.
Data analyses. In Experiment 1, we used parametric tests to analyze the reaction time. Experiment 2 was adapted from Cimminella et al. 23. For comparison purpose, we used the same analysis procedure described in their work, which is using linear and generalized linear mixed-effects models to analyze the log-transformed reaction time, search latency and probability of the first attended object being the target. In Experiment 3a, we analyzed reaction times and exploration distances using parametric tests and orthogonal polynomial contrasts. Trials considered as invalid (3. 37% of all recorded trials) were excluded from the analyses. These were improperly terminated trials that yielded unreliable estimates of exploration distance or reaction time, as described in the previous section. In Experiment 3b, we computed the empirically observed exploration maps (i.