Do-Operation Guided Causal Representation Learning with Reduced Supervision Strength

Causal representation learning has been proposed to encode relationships between factors presented in the high dimensional data. However, existing methods suffer from merely using a large amount of labeled data and ignore the fact that samples generated by the same causal mechanism follow the same causal relationships. In this paper, we seek to explore such information by leveraging do-operation to reduce supervision strength. We propose a framework that implements do-operation by swapping latent cause and effect factors encoded from a pair of inputs. Moreover, we also identify the inadequacy of existing causal representation metrics empirically and theoretically and introduce new metrics for better evaluation. Experiments conducted on both synthetic and real datasets demonstrate the superiorities of our method compared with state-of-the-art methods.Comment: NeurIPS 2022 Workshop CML4Impact Workshop Camera Read

SW-VAE: Weakly Supervised Learn Disentangled Representation Via Latent Factor Swapping

Representation disentanglement is an important goal of representation learning that benefits various downstream tasks. To achieve this goal, many unsupervised learning representation disentanglement approaches have been developed. However, the training process without utilizing any supervision signal have been proved to be inadequate for disentanglement representation learning. Therefore, we propose a novel weakly-supervised training approach, named as SW-VAE, which incorporates pairs of input observations as supervision signals by using the generative factors of datasets. Furthermore, we introduce strategies to gradually increase the learning difficulty during training to smooth the training process. As shown on several datasets, our model shows significant improvement over state-of-the-art (SOTA) methods on representation disentanglement tasks

Automated enumeration of block cipher differentials: An optimized branch-and-bound GPU framework

Block ciphers are prevalent in various security protocols used daily such as TLS, OpenPGP, and SSH. Their primary purpose is the protection of user data, both in transit and at rest. One of the de facto methods to evaluate block cipher security is differential cryptanalysis. Differential cryptanalysis observes the propagation of input patterns (input differences) through the cipher to produce output patterns (output differences). This probabilistic propagation is known as a differential; the identification of which is a measure of a block cipher’s security margins. This paper introduces an optimized GPU-based branch-and-bound framework for differential search. We optimize search efficiency by parallelizing all branch-and-bound operations, completing the entire search on the GPU without communicating with the CPU. The meet-in-the-middle (MITM) approach is also adopted for further performance gains. We analyze the financial and computational costs of the proposed framework using Google Cloud VM to showcase its practicality. When optimized for performance, we can attain up to 90x speedup while saving up to 47% of the running cost as compared to a single CPU core. When optimized for cost, the proposed framework can save up to 83% of financial costs while retaining a speedup of up to 40x. As a proof of concept, the proposed framework was then applied on 128-bit TRIFLE-BC, 64-bit PRESENT, and 64-bit GIFT. Notably, we identified the best differentials for PRESENT (16 rounds) and 64-bit GIFT (13 rounds) to date, with estimated probabilities of $2^{-61.7964}$ and $2^{-60.66}$ respectively. Although the differential results for TRIFLE-BC were incremental, the proposed framework was able to construct differentials for 43 rounds that consisted of approximately 5.8x more individual trails than previous work, making it one of the most efficient approaches for larger block ciphers

Shadow Datasets, New challenging datasets for Causal Representation Learning

Discovering causal relations among semantic factors is an emergent topic in representation learning. Most causal representation learning (CRL) methods are fully supervised, which is impractical due to costly labeling. To resolve this restriction, weakly supervised CRL methods were introduced. To evaluate CRL performance, four existing datasets, Pendulum, Flow, CelebA(BEARD) and CelebA(SMILE), are utilized. However, existing CRL datasets are limited to simple graphs with few generative factors. Thus we propose two new datasets with a larger number of diverse generative factors and more sophisticated causal graphs. In addition, current real datasets, CelebA(BEARD) and CelebA(SMILE), the originally proposed causal graphs are not aligned with the dataset distributions. Thus, we propose modifications to them

Ion Doping Effects on the Lattice Distortion and Interlayer Mismatch of Aurivillius-Type Bismuth Titanate Compounds

Taking Bismuth Titanate (Bi4Ti3O12) as a Aurivillius-type compound with m = 3 for example, the ion (W6+/Cr3+) doping effect on the lattice distortion and interlayer mismatch of Bi4Ti3O12 structure were investigated by stress analysis, based on an elastic model. Since oxygen-octahedron rotates in the ab-plane, and inclines away from the c-axis, a lattice model for describing the status change of oxygen-octahedron was built according to the substituting mechanism of W6+/Cr3+ for Ti4+, which was used to investigate the variation of orthorhombic distortion degree (a/b) of Bi4Ti3O12 with the doping content. The analysis shows that the incorporation of W6+/Cr3+ into Bi4Ti3O12 tends to relieve the distortion of pseudo-perovskite layer, which also helps it to become more stiff. Since the bismuth-oxide layer expands while the pseudo-perovskite layer tightens, an analytic model for the plane stress distribution in the crystal lattice of Bi4Ti3O12 was developed from the constitutive relationship of alternating layer structure. The calculations reveal that the structural mismatch of Bi4Ti3O12 is constrained in the ab-plane of a unit cell, since both the interlayer mismatch degree and the total strain energy vary with the doping content in a similar trend to the lattice parameters of ab-plane

The Preservation And Restoration Of Tokyo Railway Station In The Process Of Urban Redevelopment And Strategic Planning

  The recently restored Tokyo Station and its neighboring Marunouchi District, in the urban core of Tokyo, have experienced significant redevelopment in the past two decades. The successful restoration of Tokyo Station presents a valuable case for preservationists to reflect on how saving historic modern architecture is possible in an urban area where the land value is extremely high. As the business of real estate in Japan increases in significance and economic development is linked to new buildings in people's perception of progress, modern-era architecture that lacks designation is endangered by the process of urban renewal. This thesis explores the decision-making process behind the restoration of Tokyo Station and the redevelopment of its surrounding area and discusses the rationale of this restoration and redevelopment. The restoration of Tokyo Station is not merely a preservation effort. It should also be placed within the context of the re-developing transportation industry, the transformation of the local and the national economies, and the preservation climate in Tokyo. By reviewing secondary documents and data to construct the basic framework for the analysis of the restoration, this work demonstrates that the Tokyo Station project was associated with the changing economic conditions of Marunouchi District and the transformation of the railway industry; and that the restoration of Tokyo Station met the core interests of the major stakeholders in the district. Although the preservation group successfully attracted broad attention to the need for the preservation of Tokyo Station it played only a limited role in the actual process of redevelopment.   ii

Combined inhibition of epidermal growth factor receptor and cyclooxygenase-2 leads to greater anti-tumor activity of docetaxel in advanced prostate cancer.

The epidermal growth factor receptor (EGFR) and cyclooxygenase-2(COX-2) play a critical role in disease progression, relapse and therapeutic resistance of advanced prostate cancer (PCa). In this paper, we evaluated, for the first time, the therapeutic benefit of blocking EGRF and/or COX-2 (using gefitinib and NS-398, respectively) in terms of improving the efficacy of the conventional clinical chemotherapeutic drug docetaxel in vitro and vivo. We showed that EGFR and COX-2 expression was higher in metastatic than non-metastatic PCa tissues and cells. Docetaxel, alone or in combination with gefitinib or NS-398, resulted in a small decrease in cell viability. The three drug combination decreased cell viability to a greater extent than docetaxel alone or in combination with gefitinib or NS-398. Docetaxel resulted in a modest increase in apoptotic cell in metastatic and non-metastatic cell lines. NS-398 markedly enhanced docetaxel-induced cell apoptosis. The combination of the three drugs caused even more marked apoptosis and resulted in greater suppression of invasive potential than docetaxel alone or in association with gefitinib or NS-398. The combination of all three drugs also resulted in a more marked decrease in NF-ΚB, MMP-9 and VEGF levels in PC-3M cells. These in vitro findings were supported by in vivo studies showing that docetaxel in combination with gefitinib and NS-398 was significantly more effective than any individual agent. Based on previous preclinical research, we conclude that simultaneously blocking EGFR and COX-2 by gefitinib and NS-398 sensitizes advanced PCa cells to docetaxel-induced cytotoxicity

Indentation Behavior and Mechanical Properties of Tungsten/Chromium co-Doped Bismuth Titanate Ceramics Sintered at Different Temperatures

A sort of tungsten/chromium(W/Cr) co-doped bismuth titanate (BIT) ceramics (Bi4Ti2.95W0.05O12.05 + 0.2 wt % Cr2O3, abbreviate to BTWC) are ordinarily sintered between 1050 and 1150 °C, and the indentation behavior and mechanical properties of ceramics sintered at different temperatures have been investigated by both nanoindentation and microindentation technology. Firstly, more or less Bi2Ti2O7 grains as the second phase were found in BTWC ceramics, and the grain size of ceramics increased with increase of sintering temperatures. A nanoindentation test for BTWC ceramics reveals that the testing hardness of ceramics decreased with increase of sintering temperatures, which could be explained by the Hall–Petch equation, and the true hardness could be calculated according to the pressure-state-response (PSR) model considering the indentation size effect, where the value of hardness depends on the magnitude of load. While, under the application of microsized Vickers, the sample sintered at a lower temperature (1050 °C) gained four linearly propagating cracks, however, they were observed to shorten in the sample sintered at a higher temperature (1125 °C). Moreover, both the crack deflection and the crack branching existed in the latter. The hardness and the fracture toughness of BTWC ceramics presented a contrary variational tendency with increase of sintering temperatures. A high sintering tends to get a lower hardness and a higher fracture toughness, which could be attributed to the easier plastic deformation and the stronger crack inhibition of coarse grains, respectively, as well as the toughening effect coming from the second phase

A Hybrid Model for Carbon Price Forecasting Based on Improved Feature Extraction and Non-Linear Integration

Accurately predicting the price of carbon is an effective way of ensuring the stability of the carbon trading market and reducing carbon emissions. Aiming at the non-smooth and non-linear characteristics of carbon price, this paper proposes a novel hybrid prediction model based on improved feature extraction and non-linear integration, which is built on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), fuzzy entropy (FuzzyEn), improved random forest using particle swarm optimisation (PSORF), extreme learning machine (ELM), long short-term memory (LSTM), non-linear integration based on multiple linear regression (MLR) and random forest (MLRRF), and error correction with the autoregressive integrated moving average model (ARIMA), named CEEMDAN-FuzzyEn-PSORF-ELM-LSTM-MLRRF-ARIMA. Firstly, CEEMDAN is combined with FuzzyEn in the feature selection process to improve extraction efficiency and reliability. Secondly, at the critical prediction stage, PSORF, ELM, and LSTM are selected to predict high, medium, and low complexity sequences, respectively. Thirdly, the reconstructed sequences are assembled by applying MLRRF, which can effectively improve the prediction accuracy and generalisation ability. Finally, error correction is conducted using ARIMA to obtain the final forecasting results, and the Diebold–Mariano test (DM test) is introduced for a comprehensive evaluation of the models. With respect to carbon prices in the pilot regions of Shenzhen and Hubei, the results indicate that the proposed model has higher prediction accuracy and robustness. The main contributions of this paper are the improved feature extraction and the innovative combination of multiple linear regression and random forests into a non-linear integrated framework for carbon price forecasting. However, further optimisation is still a work in progress

Inhibitory effect induced by docetaxel, gefitinib, and NS-398 on the invasive potential of PC3M and DU-145 cells.

<p>The cells were untreated (control) or exposed for 24 h to 0.005 μmol/L docetaxel (D), 10 μmol/L gefitinib (G) or 50 μmol/LNS-398 (N), alone or in combination during in vitro invasion assays, performed using transwell bicameral chambers as described in Materials and Methods. At the end of the 24 h assay, the invading cells were stained and counted by phase-contrast microscopy (x 200). Representative results show the mean number of cells invading through the Matrigel insert membrane in five random microscope fields. *P <0.05, ** P <0.005, ***, P <0.001 compared with control values.</p