The key transform of the REESSE1+ asymmetrical cryptosystem is Ci = (Ai * W ^ l(i)) ^ d (% M) with l(i) in Omega = {5, 7, ..., 2n + 3} for i = 1, ..., n, where l(i) is called a lever function. In this paper, the authors give a simplified key transfor
m Ci = Ai * W ^ l(i) (% M) with a new lever function l(i) from {1, ..., n} to Omega = {+/-5, +/-6, ..., +/-(n + 4)}, where +/- means the selection of the + or - sign. Discuss the necessity of the new l(i), namely that a simplified private key is insecure if the new l(i) is a constant but not one-to-one function. Further, expound the sufficiency of the new l(i) from four aspects: (1) indeterminacy of the new l(i), (2) insufficient conditions for neutralizing the powers of W and W ^-1 even if Omega = {5, 6, ..., n + 4}, (3) verification by examples, and (4) running times of the continued fraction attack and W-parameter intersection attack which are the two most efficient of the probabilistic polytime attack algorithms so far. Last, the authors elaborate the relation between a lever function and a random oracle.
Training-time safety violations have been a major concern when we deploy reinforcement learning algorithms in the real world. This paper explores the possibility of safe RL algorithms with zero training-time safety violations in the challenging setti
ng where we are only given a safe but trivial-reward initial policy without any prior knowledge of the dynamics model and additional offline data. We propose an algorithm, Co-trained Barrier Certificate for Safe RL (CRABS), which iteratively learns barrier certificates, dynamics models, and policies. The barrier certificates, learned via adversarial training, ensure the policys safety assuming calibrated learned dynamics model. We also add a regularization term to encourage larger certified regions to enable better exploration. Empirical simulations show that zero safety violations are already challenging for a suite of simple environments with only 2-4 dimensional state space, especially if high-reward policies have to visit regions near the safety boundary. Prior methods require hundreds of violations to achieve decent rewards on these tasks, whereas our proposed algorithms incur zero violations.
Based on the Gaia DR2 catalogue of hot subdwarf star candidates, we identified 1587 hot subdwarf stars with spectra in LAMOST DR7. We present atmospheric parameters for these stars by fitting the LAMOST spectra with {sc Tlusty/Synspec} non-LTE synthe
tic spectra. Combining LAMOST radial velocities and Gaia Early Data Release 3 (EDR3) parallaxes and proper motions, we also present the Galactic space positions, velocity vectors, orbital parameters and the Galactic population memberships of the stars. With our He classification scheme, we identify four groups of He rich hot subdwarf stars in the $T_{rm eff}-log,g$ and $T_{rm eff}-log{(n{rm He}/n{rm H})}$ diagrams. We find two extreme He-rich groups ($e$He-1 and $e$He-2) for stars with $log{(n{rm He}/n{rm H})}geq0$ and two intermediate He-rich groups ($i$He-1 and $i$He-2) for stars with $-1lelog{(n{rm He}/n{rm H})}<0$. We also find that over half of the stars in Group $e$He-1 are thick disk stars, while over half of the stars in Group $e$He-2 correspond to thin disk stars. The disk population fractions of Group $i$He-1 are between those of Group $e$He-1 and $e$He-2. Almost all stars in Group $i$He-2 belong to the thin disk. These differences indicate that the four groups probably have very different origins. Comparisons between hot subdwarf stars in the halo and in the Galactic globular cluster $omega$ Cen show that only He-deficient stars with $-2.2lelog{(n{rm He}/n{rm H})}<-1$ have similar fractions. Hot subdwarfs with $log{(n{rm He}/n{rm H})}ge 0$ in $omega$ Cen have no counterparts in the thick disk and halo populations, but they appear in the thin disk.
As Internet of Things (IoT) has emerged as the next logical stage of the Internet, it has become imperative to understand the vulnerabilities of the IoT systems when supporting diverse applications. Because machine learning has been applied in many I
oT systems, the security implications of machine learning need to be studied following an adversarial machine learning approach. In this paper, we propose an adversarial machine learning based partial-model attack in the data fusion/aggregation process of IoT by only controlling a small part of the sensing devices. Our numerical results demonstrate the feasibility of this attack to disrupt the decision making in data fusion with limited control of IoT devices, e.g., the attack success rate reaches 83% when the adversary tampers with only 8 out of 20 IoT devices. These results show that the machine learning engine of IoT system is highly vulnerable to attacks even when the adversary manipulates a small portion of IoT devices, and the outcome of these attacks severely disrupts IoT system operations.
Combining the LAMOST radial velocities with Gaia parallaxes and proper motions, we presented 3D Galactic space motions and the orbits of 182 single-lined hot subdwarf stars. These stars have been identified by Lei et al. (2020) in Gaia DR2 with LAMOS
T DR6 and DR7 spectra. He-rich hot subdwarf stars with log(y)>0 show the largest standard deviations of the Galactic velocity components and orbital parameters, while those with -1<log(y)<0 exhibit the second largest standard deviations. The two groups of He-deficient stars with log(y)<-1 show similar standard deviations, which is systematically lower compared to He-rich stars. We also presented a kinematic population classification of the four hot subdwarf helium groups based on their positions in the U-V velocity diagram, J_z-eccentricity diagram and their Galactic orbits. The overall tendency of the fractional distributions of the four hot subdwarf helium groups in the halo, thin disk and thick disk is largely consistent with the findings reported by Luo et al.(2019) based on LAMOST DR5, which appears to support the predictions of binary population synthesis (Han et al. 2003; 2008). He-deficient stars with -2.2<log(y)<-1 likely origin from stable the Roche lobe overflow channel, He-deficient stars with log(y)<-2.2 from the common envelope ejection channel, and He-rich stars with log(y)>0 from the merger channel of double He white dwarf stars. The fraction of He-rich hot subdwarf stars with -1<log(y)<0 in the thin disk and the halo are far higher than in the thick disk, which implies that these stars have different formation channels in the thin disk and in the halo.
Improving sparsity of deep neural networks (DNNs) is essential for network compression and has drawn much attention. In this work, we disclose a harmful sparsifying process called filter collapse, which is common in DNNs with batch normalization (BN)
and rectified linear activation functions (e.g. ReLU, Leaky ReLU). It occurs even without explicit sparsity-inducing regularizations such as $L_1$. This phenomenon is caused by the normalization effect of BN, which induces a non-trainable region in the parameter space and reduces the network capacity as a result. This phenomenon becomes more prominent when the network is trained with large learning rates (LR) or adaptive LR schedulers, and when the network is finetuned. We analytically prove that the parameters of BN tend to become sparser during SGD updates with high gradient noise and that the sparsifying probability is proportional to the square of learning rate and inversely proportional to the square of the scale parameter of BN. To prevent the undesirable collapsed filters, we propose a simple yet effective approach named post-shifted BN (psBN), which has the same representation ability as BN while being able to automatically make BN parameters trainable again as they saturate during training. With psBN, we can recover collapsed filters and increase the model performance in various tasks such as classification on CIFAR-10 and object detection on MS-COCO2017.
The direct simulation of the dynamics of second sound in graphitic materials remains a challenging task due to lack of methodology for solving the phonon Boltzmann equation in such a stiff hydrodynamic regime. In this work, we aim to tackle this chal
lenge by developing a multiscale numerical scheme for the transient phonon Boltzmann equation under Callaways dual relaxation model which captures well the collective phonon kinetics. Comparing to traditional numerical methods, the present multiscale scheme is efficient, accurate and stable in all transport regimes attributed to avoiding the use of time and spatial steps smaller than the relaxation time and mean free path of phonons. The formation, propagation and composition of ballistic pulses and second sound in graphene ribbon in two classical paradigms for experimental detection are investigated via the multiscale scheme. The second sound is declared to be mainly contributed by ZA phonon modes, whereas the ballistic pulses are mainly contributed by LA and TA phonon modes. The influence of temperature, isotope abundance and ribbon size on the second sound propagation is also explored. The speed of second sound in the observation window is found to be at most 20 percentages smaller than the theoretical value in hydrodynamic limit due to the finite umklapp, isotope and edge resistive scattering. The present study will contribute to not only the solution methodology of phonon Boltzmann equation, but also the physics of transient hydrodynamic phonon transport as guidance for future experimental detection.
Combing Gaia DR2 with LAMOST DR5, we spectroscopically identified 924 hot subdwarf stars, among which 32 stars exhibit strong double-lined composite spectra. We measured the effective temperature $T_{rm eff}$, surface gravity $log,g$, helium abundanc
e $y=n{rm He}/n{rm H}$, and radial velocities of 892 non-composite spectra hot subdwarf stars by fitting LAMOST observations with Tlusty/Synspec non-LTE synthetic spectra. We outlined four different groups in the $T_{rm eff}-log,g$ diagram with our helium abundance classification scheme and two nearly parallel sequences in the $T_{rm eff}-log(y)$ diagram. 3D Galactic space motions and orbits of 747 hot subdwarf stars with $(G_{BP}-G_{RP})_{0}<-0.36$ mag were computed using LAMOST radial velocities and Gaia parallaxes and proper motions. Based on the $U-V$ velocity diagram, $J_{z}-$eccentricity diagram, and Galactic orbits, we derived Galactic population classifications and the fractional distributions of the four hot subdwarf helium groups in the halo, thin disk and thick disk. Comparisons with the predictions of binary population synthesis calculations (Han 2008) suggest that He-rich hot subdwarf stars with $log(y)ge0$ are from the double helium white dwarfs merger, He-deficient hot subdwarf stars with $-2.2lelog(y)<-1$ from the common envelope ejection, and He-deficient hot subdwarf stars with $log(y)<-2.2$ from the stable Roche lobe overflow channels. The relative number of He-rich hot subdwarf stars with $-1lelog(y)<0$ and $log(y)ge0$ in the halo is more than twice the prediction of Zhang et al.(2017), even more than six times in the thin disk, which implies that the mergers of helium white dwarfs with low mass main sequence stars may not be the main formation channel of He-rich hot subdwarf stars with $-1lelog(y)<0$, specially in younger environments.
Recently, improving the relevance and diversity of dialogue system has attracted wide attention. For a post x, the corresponding response y is usually diverse in the real-world corpus, while the conventional encoder-decoder model tends to output the
high-frequency (safe but trivial) responses and thus is difficult to handle the large number of responding styles. To address these issues, we propose the Atom Responding Machine (ARM), which is based on a proposed encoder-composer-decoder network trained by a teacher-student framework. To enrich the generated responses, ARM introduces a large number of molecule-mechanisms as various responding styles, which are conducted by taking different combinations from a few atom-mechanisms. In other words, even a little of atom-mechanisms can make a mickle of molecule-mechanisms. The experiments demonstrate diversity and quality of the responses generated by ARM. We also present generating process to show underlying interpretability for the result.
Defense strategies have been well studied to combat Byzantine attacks that aim to disrupt cooperative spectrum sensing by sending falsifi
