Learning to Quantize Deep Networks by Optimizing Quantization Intervals with Task Loss

Reducing bit-widths of activations and weights of deep networks makes it efficient to compute and store them in memory, which is crucial in their deployments to resource-limited devices, such as mobile phones. However, decreasing bit-widths with quantization generally yields drastically degraded accuracy. To tackle this problem, we propose to learn to quantize activations and weights via a trainable quantizer that transforms and discretizes them. Specifically, we parameterize the quantization intervals and obtain their optimal values by directly minimizing the task loss of the network. This quantization-interval-learning (QIL) allows the quantized networks to maintain the accuracy of the full-precision (32-bit) networks with bit-width as low as 4-bit and minimize the accuracy degeneration with further bit-width reduction (i.e., 3 and 2-bit). Moreover, our quantizer can be trained on a heterogeneous dataset, and thus can be used to quantize pretrained networks without access to their training data. We demonstrate the effectiveness of our trainable quantizer on ImageNet dataset with various network architectures such as ResNet-18, -34 and AlexNet, on which it outperforms existing methods to achieve the state-of-the-art accuracy

Efficient Privacy Preserving Logistic Regression Inference and Training

Recently, privacy-preserving logistic regression techniques on distributed data among several data owners drew attention in terms of their applicability in federated learning environment. Many of them have been built upon cryptographic primitives such as secure multiparty computations(MPC) and homomorphic encryptions(HE) to protect the privacy of data. The secure multiparty computation provides fast and secure unit operations for arithmetic and bit operations but they often does not scale with large data well enough due to large computation cost and communication overhead. From recent works, many HE primitives provide their operations in a batch sense so that the technique can be an appropriate choice in a big data environment. However computationally expensive operations such as ciphertext slot rotation or refreshment(so called bootstrapping) and large public key size are hurdles that hamper widespread of the technique in the industry-level environment. In this paper, we provide a new hybrid approach of a privacy-preserving logistic regression training and a inference, which utilizes both MPC and HE techniques to provide efficient and scalable solution while minimizing needs of key management and complexity of computation in encrypted state. Utilizing batch sense properties of HE, we present a method to securely compute multiplications of vectors and matrices using one HE multiplication, compared to the naive approach which requires linear number of multiplications regarding to the size of input data. We also show how we used a 2-party additive secret sharing scheme to control noises of expensive HE operations such as bootstrapping efficiently

Notch signaling is required for maintaining stem-cell features of neuroprogenitor cells derived from human embryonic stem cells

<p>Abstract</p> <p>Background</p> <p>Studies have provided important findings about the roles of Notch signaling in neural development. Unfortunately, however, most of these studies have investigated the neural stem cells (NSCs) of mice or other laboratory animals rather than humans, mainly owing to the difficulties associated with obtaining human brain samples. It prompted us to focus on neuroectodermal spheres (NESs) which are derived from human embryonic stem cell (hESC) and densely inhabited by NSCs. We here investigated the role of Notch signaling with the hESC-derived NESs.</p> <p>Results</p> <p>From hESCs, we derived NESs, the <it>in-vitro </it>version of brain-derived neurospheres. NES formation was confirmed by increased levels of various NSC marker genes and the emergence of rosette structures in which neuroprogenitors are known to reside. We found that Notch signaling, which maintains stem cell characteristics of <it>in-vivo</it>-derived neuroprogenitors, is active in these hESC-derived NESs, similar to their <it>in-vivo </it>counterpart. Expression levels of Notch signaling molecules such as NICD, DLLs, JAG1, HES1 and HES5 were increased in the NESs. Inhibition of the Notch signaling by a γ-secretase inhibitor reduced rosette structures, expression levels of NSC marker genes and proliferation potential in the NESs, and, if combined with withdrawal of growth factors, triggered differentiation toward neurons.</p> <p>Conclusion</p> <p>Our results indicate that the hESC-derived NESs, which share biochemical features with brain-derived neurospheres, maintain stem cell characteristics mainly through Notch signaling, which suggests that the hESC-derived NESs could be an <it>in-vitro </it>model for <it>in-vivo </it>neurogenesis.</p

A Practical Post-Quantum Public-Key Cryptosystem Based on spLWE

The Learning with Errors (LWE) problem has been widely used as a hardness assumption to construct public-key primitives. In this paper, we propose an efficient instantiation of a PKE scheme based on LWE with a sparse secret, named as spLWE. We first construct an IND-CPA PKE and convert it to an IND-CCA scheme in the quantum random oracle model by applying a modified Fujisaki-Okamoto conversion of Unruh. In order to guarantee the security of our base problem suggested in this paper, we provide a polynomial time reduction from LWE with a uniformly chosen secret to spLWE. We modify the previous attacks for LWE to exploit the sparsity of a secret key and derive more suitable parameters. We can finally estimate performance of our scheme supporting 256-bit messages: our implementation shows that our IND-CCA scheme takes 313 micro seconds and 302 micro seconds respectively for encryption and decryption with the parameters that have 128-quantum bit security

Gapped Ferromagnetic Graphene Nanoribbons

We theoretically design a graphene-based all-organic ferromagnetic semiconductor by terminating zigzag graphene nanoribbons (ZGNRs) with organic magnets. A large spin-split gap with 100% spin polarized density of states near the Fermi energy is obtained, which is of potential application in spin transistors. The interplays among electron, spin and lattice degrees of freedom are studied using the first-principles calculations combined with fundamental model analysis. All of the calculations consistently demonstrate that although no d electrons existing, the antiferromagnetic \pi-\pi exchange together with the strong spin-lattice interactions between organic magnets and ZGNRs make the ground state ferromagnetic. The fundamental physics makes it possible to optimally select the organic magnets towards practical applications.Comment: 5 pages, 3 figure

Methionine deprivation suppresses triple-negative breast cancer metastasis in vitro and in vivo

Nutrient deprivation strategies have been proposed as an adjuvant therapy for cancer cells due to their increased metabolic demand. We examined the specific inhibitory effects of amino acid deprivation on the metastatic phenotypes of the human triple-negative breast cancer (TNBC) cell lines MDA-MB-231 and Hs 578T, as well as the orthotopic 4T1 mouse TNBC tumor model. Among the 10 essential amino acids tested, methionine deprivation elicited the strongest inhibitory effects on the migration and invasion of these cancer cells. Methionine deprivation reduced the phosphorylation of focal adhesion kinase, as well as the activity and mRNA expression of matrix metalloproteinases MMP-2 and MMP-9, two major markers of metastasis, while increasing the mRNA expression of tissue inhibitor of metalloproteinase 1 in MDA-MB-231 cells. Furthermore, methionine restriction downregulated the metastasis-related factor urokinase plasminogen activatior and upregulated plasminogen activator inhibitor 1 mRNA expression. Animals on the methionine-deprived diet showed lower lung metastasis rates compared to mice on the control diet. Taken together, these results suggest that methionine restriction could provide a potential nutritional strategy for more effective cancer therapy

Modulation of Radiation-Induced Disturbances of Antioxidant Defense Systems by Ginsan

There are numerous studies to indicate that irradiation induces reactive oxygen species (ROS), which play an important causative role in radiation damage of the cell. We evaluated the effects of ginsan, a polysaccharide fraction extracted from Panax ginseng, on the γ-radiation induced alterations of some antioxidant systems in the spleen of Balb/c mice. On the 5th day after sublethal whole-body irradiation, homogenized spleen tissues of the irradiated mice expressed only marginally increased mRNA levels of Mn-SOD (superoxide dimutase) in contrast to Cu/Zn-SOD, however, catalase mRNA was decreased by ∼50% of the control. In vivo treatment of non-irradiated mice with ginsan (100 mg kg(−1), intraperitoneal administration) had no significant effect, except for glutathione peroxidase (GPx) mRNA, which increased to 144% from the control. However, the combination of irradiation with ginsan effectively increased the SODs and GPx transcription as well as their protein expressions and enzyme activities. In addition, the expression of heme oxygenase-1 and non-protein thiol induced by irradiation was normalized by the treatment of ginsan. Evidence indicated that transforming growth factor-β and other important cytokines such as IL-1, TNF and IFN-γ might be involved in evoking the antioxidant enzymes. Therefore, we propose that the modulation of antioxidant enzymes by ginsan was partly responsible for protecting the animal from radiation, and could be applied as a therapeutic remedy for various ROS-related diseases