We consider a modified gravity framework for inflation by adding to the Einstein-Hilbert action a direct $f(phi)T$ term, where $phi$ is identified as the inflaton and $T$ is the trace of the energy-momentum tensor. The framework goes to Einstein grav
ity naturally when inflaton decays out. We investigate inflation dynamics in this $f(phi)T$ gravity (not to be confused with torsion-scalar coupled theories) on a general basis, and then apply it to three well-motivated inflationary models. We find that the predictions for the spectral tilt and the tensor-to-scalar ratio are sensitive to this new $f(phi)T$ term. This $f(phi)T$ gravity brings both chaotic and natural inflation into better agreement with data. For Starobinsky inflation, the coupling constant $alpha$ in $[-0.0026,0.0031]$ for $N=60$ is in Planck-allowed $2sigma$ region.
In this paper, we present a systematic investigation on simple inverse seesaw models for neutrino masses and flavor mixing based on the modular $S^{}_4$ symmetry. Two right-handed neutrinos and three extra fermion singlets are introduced to account f
or light neutrino masses through the inverse seesaw mechanism, and to provide a keV-mass sterile neutrino as the candidate for warm dark matter in our Universe. Considering all possible modular forms with weights no larger than four, we obtain twelve models, among which we find one is in excellent agreement with the observed lepton mass spectra and flavor mixing. Moreover, we explore the allowed range of the sterile neutrino mass and mixing angles, by taking into account the direct search of $X$-ray line and the Lyman-$alpha$ observations. The model predictions for neutrino mixing parameters and the dark matter abundance will be readily testable in future neutrino oscillation experiments and cosmological observations.
This paper addresses the problem of picking up only one object at a time avoiding any entanglement in bin-picking. To cope with a difficult case where the complex-shaped objects are heavily entangled together, we propose a topology-based method that
can generate non-tangle grasp positions on a single depth image. The core technique is entanglement map, which is a feature map to measure the entanglement possibilities obtained from the input image. We use the entanglement map to select probable regions containing graspable objects. The optimum grasping pose is detected from the selected regions considering the collision between robot hand and objects. Experimental results show that our analytic method provides a more comprehensive and intuitive observation of entanglement and exceeds previous learning-based work in success rates. Especially, our topology-based method does not rely on any object models or time-consuming training process, so that it can be easily adapted to more complex bin-picking scenes.
Model extraction increasingly attracts research attentions as keeping commercial AI models private can retain a competitive advantage. In some scenarios, AI models are trained proprietarily, where neither pre-trained models nor sufficient in-distribu
tion data is publicly available. Model extraction attacks against these models are typically more devastating. Therefore, in this paper, we empirically investigate the behaviors of model extraction under such scenarios. We find the effectiveness of existing techniques significantly affected by the absence of pre-trained models. In addition, the impacts of the attackers hyperparameters, e.g. model architecture and optimizer, as well as the utilities of information retrieved from queries, are counterintuitive. We provide some insights on explaining the possible causes of these phenomena. With these observations, we formulate model extraction attacks into an adaptive framework that captures these factors with deep reinforcement learning. Experiments show that the proposed framework can be used to improve existing techniques, and show that model extraction is still possible in such strict scenarios. Our research can help system designers to construct better defense strategies based on their scenarios.
Heterogeneous information networks(HINs) become popular in recent years for its strong capability of modelling objects with abundant information using explicit network structure. Network embedding has been proved as an effective method to convert inf
ormation networks into lower-dimensional space, whereas the core information can be well preserved. However, traditional network embedding algorithms are sub-optimal in capturing rich while potentially incompatible semantics provided by HINs. To address this issue, a novel meta-path-based HIN representation learning framework named mSHINE is designed to simultaneously learn multiple node representations for different meta-paths. More specifically, one representation learning module inspired by the RNN structure is developed and multiple node representations can be learned simultaneously, where each representation is associated with one respective meta-path. By measuring the relevance between nodes with the designed objective function, the learned module can be applied in downstream link prediction tasks. A set of criteria for selecting initial meta-paths is proposed as the other module in mSHINE which is important to reduce the optimal meta-path selection cost when no prior knowledge of suitable meta-paths is available. To corroborate the effectiveness of mSHINE, extensive experimental studies including node classification and link prediction are conducted on five real-world datasets. The results demonstrate that mSHINE outperforms other state-of-the-art HIN embedding methods.
We propose a supervised principal component regression method for relating functional responses with high dimensional covariates. Unlike the conventional principal component analysis, the proposed method builds on a newly defined expected integrated
residual sum of squares, which directly makes use of the association between functional response and predictors. Minimizing the integrated residual sum of squares gives the supervised principal components, which is equivalent to solving a sequence of nonconvex generalized Rayleigh quotient optimization problems and thus is computationally intractable. To overcome this computational challenge, we reformulate the nonconvex optimization problems into a simultaneous linear regression, with a sparse penalty added to deal with high dimensional predictors. Theoretically, we show that the reformulated regression problem recovers the same supervised principal subspace under suitable conditions. Statistically, we establish non-asymptotic error bounds for the proposed estimators. Numerical studies and an application to the Human Connectome Project lend further support.
Although natural inflation is a theoretically well-motivated model for cosmic inflation, it is in tension with recent Planck cosmic microwave background anisotropy measurements. We present a way to alleviate this tension by considering a very weak no
nminimal coupling of the inflaton field to gravity in both contexts of metric and Palatini formulations of general relativity. We start our discussions with a generic form of the inflaton coupling to the Ricci scalar, then focus on a simple form to do phenomenological study. Our results show that such an extension can bring natural inflations predictions to a good agreement with the Planck data. Depending on values of the coupling constant $xi$ and the symmetry breaking scale $f$, we find that with $|xi|sim 10^{-3}$ and $fgtrsim 2.0 M_{mathrm{pl}}$ predictions of the model stay inside $68%$ CL allowed region until $f$ increases up to $7.7 M_{mathrm{pl}}$, then only inside $95%$ CL region after $f$ exceeds the latter value. The predictions from the metric and the Palatini theories are very similar due to the simple form of the coupling function we use and the small magnitude of the coupling $xi$. Successful reheating can also be realized in this model.
We consider natural inflation in a warm inflation framework with a temperature-dependent dissipative coefficient $Gamma propto T^3$. Natural inflation can be compatible with the Planck 2018 results with such warm assistance. With no a priori assumpti
ons on the dissipative effects magnitude, we find that the Planck results prefer a weak dissipative regime for our benchmark scale $f=5 M_{rm pl}$, which lies outside the $2sigma$ region in the cold case. The inflation starts in the cold regime and evolves with a growing thermal fluctuation that dominates over quantum fluctuation before the end of the inflation. The observed spectral tilt puts stringent constraints on the models parameter space. We find that $f< 1 M_{rm pl}$ is excluded. A possible origin of such dissipative coefficient from axion-like coupling to gauge fields and tests of the model are also discussed.
Hierarchical multi-label text classification (HMTC) has been gaining popularity in recent years thanks to its applicability to a plethora of real-world applications. The existing HMTC algorithms largely focus on the design of classifiers, such as the
local, global, or a combination of them. However, very few studies have focused on hierarchical feature extraction and explore the association between the hierarchical labels and the text. In this paper, we propose a Label-based Attention for Hierarchical Mutlti-label Text Classification Neural Network (LA-HCN), where the novel label-based attention module is designed to hierarchically extract important information from the text based on the labels from different hierarchy levels. Besides, hierarchical information is shared across levels while preserving the hierarchical label-based information. Separate local and global document embeddings are obtained and used to facilitate the respective local and global classifications. In our experiments, LA-HCN outperforms other state-of-the-art neural network-based HMTC algorithms on four public HMTC datasets. The ablation study also demonstrates the effectiveness of the proposed label-based attention module as well as the novel local and global embeddings and classifications. By visualizing the learned attention (words), we find that LA-HCN is able to extract meaningful information corresponding to the different labels which provides explainability that may be helpful for the human analyst.
We perform a thermal unflavored leptogenesis analysis on minimal left-right symmetric models with discrete left-right symmetry identified as generalized parity or charge conjugation. When left-right symmetry is unbroken in the lepton Yukawa sector, t
he neutrino Dirac coupling matrix is completely determined by neutrino masses and mixing angles, allowing CP violation needed to generate leptogenesis totally resides in the low-energy sector. With two lepton asymmetry generation ways, both type I and mixed type I$+$II neutrino mass generation mechanisms are considered. After solving the Boltzmann equations numerically, we find that the low-energy CP phases in the lepton mixing matrix can successfully produce the observed baryon asymmetry, and in some cases, the Dirac CP phase can be the only source of CP violation. Finally, we discuss the interplay among low-energy CP phase measurements, leptogenesis, and neutrinoless double beta decay. We show that the viable models for successful leptogenesis can be probed in next-generation neutrinoless double-beta decay experiments.
