Short-Sale Constraints and Corporate Investment

In a sample of non-U.S. regulatory regime shifts, we find that expanded short selling is associated with stock price declines, reductions in capital expenditure, and lower asset growth. In a reversal of results found for U.S. stocks in a study of Regulation SHO by Grullon, Michenaud, and Weston (2015), our results are stronger for large firms than for small firms. We also show that this investment effect is stronger for firms that previously relied on outside financing. Our results suggest that short-sale policies affect corporate investment and that this effect is not driven by capital constraints

Differentially 4-Uniform Bijections by Permuting the Inverse Function

Block ciphers use Substitution boxes (S-boxes) to create confusion into the cryptosystems. Functions used as S-boxes should have low differential uniformity, high nonlinearity and algebraic degree larger than 3 (preferably strictly larger). They should be fastly computable; from this viewpoint, it is better when they are in even number of variables. In addition, the functions should be bijections in a Substitution-Permutation Network. Almost perfect nonlinear (APN) functions have the lowest differential uniformity 2 and the existence of APN bijections over \F_{2^n} for even $n\ge 8$ is a big open problem. In the present paper, we focus on constructing differentially 4-uniform bijections suitable for designing S-boxes for block ciphers. Based on the idea of permuting the inverse function, we design a construction providing a large number of differentially 4-uniform bijections with maximum algebraic degree and high nonlinearity. For every even $n\ge 12$, we mathematically prove that the functions in a subclass of the constructed class are CCZ-inequivalent to known differentially 4-uniform power functions and to quadratic functions. This is the first mathematical proof that an infinite class of differentially 4-uniform bijections is CCZ-inequivalent to known differentially 4-uniform power functions and to quadratic functions. We also get a general lower bound on the nonlinearity of our functions, which can be very high in some cases, and obtain three improved lower bounds on the nonlinearity for three special subcases of functions which are extremely large

Construction of Balanced Boolean Functions with High Nonlinearity and Good Autocorrelation Properties

Boolean functions with high nonlinearity and good autocorrelation properties play an important role in the design of block ciphers and stream ciphers. In this paper, we give a method to construct balanced Boolean functions on $n$ variables, where $n\ge 10$ is an even integer, satisfying strict avalanche criterion (SAC). Compared with the known balanced Boolean functions with SAC property, the constructed functions possess the highest nonlinearity and the best global avalanche characteristics (GAC) property

Highly Nonlinear Boolean Functions with Optimal Algebraic Immunity and Good Behavior Against Fast Algebraic Attacks

In this paper, we present a new combinatorial conjecture about binary strings. Based on the new conjecture, two classes of Boolean functions of $2k$ variables with optimal algebraic immunity are proposed, where $k\ge 2$. The first class contains unbalanced functions having high algebraic degree and nonlinearity. The functions in the second one are balanced and have maximal algebraic degree and high nonlinearity. It is checked that, at least for small numbers of variables, both classes of functions have a good behavior against fast algebraic attacks. Compared with the known Boolean functions resisting algebraic attacks and fast algebraic attacks, the two classes of functions possess the highest lower bounds on nonlinearity. These bounds are however not enough for ensuring a sufficient nonlinearity for allowing resistance to the fast correlation attack. Nevertheless, as for previously found functions with the same features, there is a gap between the bound that we can prove and the actual values computed for small numbers of variables. Moreover, these values are very good and much better than for the previously found functions having all the necessary features for being used in the filter model of pseudo-random generators

GaitGS: Temporal Feature Learning in Granularity and Span Dimension for Gait Recognition

Gait recognition is an emerging biological recognition technology that identifies and verifies individuals based on their walking patterns. However, many current methods are limited in their use of temporal information. In order to fully harness the potential of gait recognition, it is crucial to consider temporal features at various granularities and spans. Hence, in this paper, we propose a novel framework named GaitGS, which aggregates temporal features in the granularity dimension and span dimension simultaneously. Specifically, Multi-Granularity Feature Extractor (MGFE) is proposed to focus on capturing the micro-motion and macro-motion information at the frame level and unit level respectively. Moreover, we present Multi-Span Feature Learning (MSFL) module to generate global and local temporal representations. On three popular gait datasets, extensive experiments demonstrate the state-of-the-art performance of our method. Our method achieves the Rank-1 accuracies of 92.9% (+0.5%), 52.0% (+1.4%), and 97.5% (+0.8%) on CASIA-B, GREW, and OU-MVLP respectively. The source code will be released soon.Comment: 14 pages, 6 figure

Balanced Boolean Functions with (Almost) Optimal Algebraic Immunity and Very High Nonlinearity

In this paper, we present a class of $2k$-variable balanced Boolean functions and a class of $2k$-variable $1$-resilient Boolean functions for an integer $k\ge 2$, which both have the maximal algebraic degree and very high nonlinearity. Based on a newly proposed conjecture by Tu and Deng, it is shown that the proposed balanced Boolean functions have optimal algebraic immunity and the $1$-resilient Boolean functions have almost optimal algebraic immunity. Among all the known results of balanced Boolean functions and $1$-resilient Boolean functions, our new functions possess the highest nonlinearity. Based on the fact that the conjecture has been verified for all $k\le 29$ by computer, at least we have constructed a class of balanced Boolean functions and a class of $1$-resilient Boolean functions with the even number of variables $\le 58$, which are cryptographically optimal or almost optimal in terms of balancedness, algebraic degree, nonlinearity, and algebraic immunity

LUT-NN: Empower Efficient Neural Network Inference with Centroid Learning and Table Lookup

On-device Deep Neural Network (DNN) inference consumes significant computing resources and development efforts. To alleviate that, we propose LUT-NN, the first system to empower inference by table lookup, to reduce inference cost. LUT-NN learns the typical features for each operator, named centroid, and precompute the results for these centroids to save in lookup tables. During inference, the results of the closest centroids with the inputs can be read directly from the table, as the approximated outputs without computations. LUT-NN integrates two major novel techniques: (1) differentiable centroid learning through backpropagation, which adapts three levels of approximation to minimize the accuracy impact by centroids; (2) table lookup inference execution, which comprehensively considers different levels of parallelism, memory access reduction, and dedicated hardware units for optimal performance. LUT-NN is evaluated on multiple real tasks, covering image and speech recognition, and nature language processing. Compared to related work, LUT-NN improves accuracy by 66% to 92%, achieving similar level with the original models. LUT-NN reduces the cost at all dimensions, including FLOPs ($\leq$ 16x), model size ($\leq$ 7x), latency ($\leq$ 6.8x), memory ($\leq$ 6.5x), and power ($\leq$ 41.7%)

Identification of novel proteins interacting with vascular endothelial growth inhibitor 174 in renal cell carcinoma

Background/Aim: Vascular endothelial growth inhibitor (VEGI) is a multipotential cytokine that plays a role in regulating immunity, anti-angiogenesis, and inhibiting tumor growth. However, the proteins that interact with it are still unknown. In the present study, we examined the proteins which interact with VEGI174 and their expressions in renal cell carcinoma (RCC). Materials and Methods: The proteins that interact with VEGI174 were identified using western blot, pull-down assay, and mass spectrometry. The expressions of VEGI174 and the interacting proteins were examined in RCC and were compared with normal renal tissues using immunochemical staining and RNA-seq respectively. Results: The results of the mass spectrometric analysis showed that ACLY, ENO1, ZIK1, AKR1C3, and MYC may interact with VEGI174. When compared with the TCGA database, the expression level of VEGI174 in RCC was lower than that in normal kidney using RNAseq (p<0.001). The expression levels of ACLY, ENO1, ZIK1, AKR1C3 and MYC in RCC were higher than that in normal kidney (p<0.05, all of above factors). Moreover, immunochemical staining results also showed that the expression level of AKR1C3 in RCC was significantly higher than that in normal kidney (p<0.001) and was also positively correlated with higher RCC stage and grade. Conclusion: Taken together, our findings showed that VEGI174 may interact with ACLY, ENO1, ZIK1, AKR1C3, and MYC. The expression of ACLY, ENO1, AKR1C3 and MYC is increased in RCC. AKR1C3 was a new factor that may correlate with the progression of RCC. The results indicated that VEGI174 has more functions than we currently know in the development and progression of RC

Protein of vascular endothelial growth inhibitor 174 inhibits epithelial-mesenchymal transition in renal cell carcinoma in vivo

Background: Vascular endothelial growth inhibitor (VEGI) is a member of the tumor necrosis factor superfamily, identified as an anti-angiogenic cytokine. However, the effect of VEGI on epithelial–mesenchymal transition (EMT) in renal cell carcinoma (RCC) is still unknown. Materials and Methods: In this study, protein VEGI174 was designed and synthesized. Renal cell carcinoma A498 cells were implanted into immune-deficient mice to establish tumor models. Two groups were included: control group treated with saline, and VEGI174-treated group. Data of tumor growth were collected every 3 to 4 days. Two weeks later, the tumor specimens were harvested for immunohistochemical staining of EMT markers (E-cadherin, N-cadherin, vimentin). Results: Compared to the saline-treated group, the VEGI174-treated group showed significant inhibition of tumor growth (p<0.05). The expression of E-cadherin was significantly higher in the VEGI174-treated group compared to the saline-treated group (p<0.01). However, the expression of N-cadherin and vimentin were reduced in the VEGI174-treated group. Conclusion: Our findings indicate that VEGI174 prevents progression and tumor metastasis through inhibiting EMT in RCC in vivo. This may provide a new approach for the treatment of RCC