Generative Modeling for Tabular Data via Penalized Optimal Transport Network

The task of precisely learning the probability distribution of rows within tabular data and producing authentic synthetic samples is both crucial and non-trivial. Wasserstein generative adversarial network (WGAN) marks a notable improvement in generative modeling, addressing the challenges faced by its predecessor, generative adversarial network. However, due to the mixed data types and multimodalities prevalent in tabular data, the delicate equilibrium between the generator and discriminator, as well as the inherent instability of Wasserstein distance in high dimensions, WGAN often fails to produce high-fidelity samples. To this end, we propose POTNet (Penalized Optimal Transport Network), a generative deep neural network based on a novel, robust, and interpretable marginally-penalized Wasserstein (MPW) loss. POTNet can effectively model tabular data containing both categorical and continuous features. Moreover, it offers the flexibility to condition on a subset of features. We provide theoretical justifications for the motivation behind the MPW loss. We also empirically demonstrate the effectiveness of our proposed method on four different benchmarks across a variety of real-world and simulated datasets. Our proposed model achieves orders of magnitude speedup during the sampling stage compared to state-of-the-art generative models for tabular data, thereby enabling efficient large-scale synthetic data generation.Comment: 37 pages, 23 figure

Inhibition of Bacterial Ammonia Oxidation by Organohydrazines in Soil Microcosms

Hydroxylamine oxidation by hydroxylamine oxidoreductase (HAO) is a key step for energy-yielding in support of the growth of ammonia-oxidizing bacteria (AOB). Organohydrazines have been shown to inactivate HAO from Nitrosomonas europaea, and may serve as selective inhibitors to differentiate bacterial from archaeal ammonia oxidation due to the absence of bacterial HAO gene homolog in known ammonia-oxidizing archaea (AOA). In this study, the effects of three organohydrazines on activity, abundance, and composition of AOB and AOA were evaluated in soil microcosms. The results indicate that phenylhydrazine and methylhydrazine at the concentration of 100 μmol g−1 dry weight soil completely suppressed the activity of soil nitrification. Denaturing gradient gel electrophoresis fingerprinting and sequencing analysis of bacterial ammonia monooxygenase subunit A gene (amoA) clearly demonstrated that nitrification activity change is well paralleled with the growth of Nitrosomonas europaea-like AOB in soil microcosms. No significant correlation between AOA community structure and nitrification activity was observed among all treatments during the incubation period, although incomplete inhibition of nitrification activity occurred in 2-hydroxyethylhydrazine-amended soil microcosms. These findings show that the HAO-targeted organohydrazines can effectively inhibit bacterial nitrification in soil, and the mechanism of organohydrazine affecting AOA remains unclear

Nano-yarn carbon nanotube fiber based enzymatic glucose biosensor

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2010 IOP Publishing Ltd.A novel brush-like electrode based on carbon nanotube (CNT) nano-yarn fiber has been designed for electrochemical biosensor applications and its efficacy as an enzymatic glucose biosensor demonstrated. The CNT nano-yarn fiber was spun directly from a chemical-vapor-deposition (CVD) gas flow reaction using a mixture of ethanol and acetone as the carbon source and an iron nano-catalyst. The fiber, 28 µm in diameter, was made of bundles of double walled CNTs (DWNTs) concentrically compacted into multiple layers forming a nano-porous network structure. Cyclic voltammetry study revealed a superior electrocatalytic activity for CNT fiber compared to the traditional Pt–Ir coil electrode. The electrode end tip of the CNT fiber was freeze-fractured to obtain a unique brush-like nano-structure resembling a scale-down electrical 'flex', where glucose oxidase (GOx) enzyme was immobilized using glutaraldehyde crosslinking in the presence of bovine serum albumin (BSA). An outer epoxy-polyurethane (EPU) layer was used as semi-permeable membrane. The sensor function was tested against a standard reference electrode. The sensitivities, linear detection range and linearity for detecting glucose for the miniature CNT fiber electrode were better than that reported for a Pt–Ir coil electrode. Thermal annealing of the CNT fiber at 250 °C for 30 min prior to fabrication of the sensor resulted in a 7.5 fold increase in glucose sensitivity. The as-spun CNT fiber based glucose biosensor was shown to be stable for up to 70 days. In addition, gold coating of the electrode connecting end of the CNT fiber resulted in extending the glucose detection limit to 25 µM. To conclude, superior efficiency of CNT fiber for glucose biosensing was demonstrated compared to a traditional Pt–Ir sensor.Brunel University, the Royal Society and the National Institute of Health

Necessary and Sufficient Conditions for Galois NFSRs Equivalent to Fibonacci Ones and Their Application to the Stream Cipher Trivium

Many recent stream ciphers use Galois NFSRs as their main building blocks, such as the hardware-oriented finalists Grain and Trivium in the eSTREAM project. Previous work has found some types of Galois NFSRs equivalent to Fibonacci ones, including that used in Grain. Based on the observability of an NFSR on [0,N-1], which means any two initial states of an NFSR are distinguishable from their corresponding output sequences of length N, the paper first presents two easily verifiable necessary and sufficient conditions for Galois NFSRs equivalent to Fibonacci ones. It then validates both conditions by the Galois NFSRs previously found (not) equivalent to Fibonacci ones. As an application, the paper finally reveals that the 288-stage Galois NFSR used in Trivium is neither equivalent to a 288-stage Fibonacci NFSR, nor observable on [0,287], theoretically verifying Trivium\u27s good design criteria of confusion and diffusion

Distinguishing and controlling Mottness in 1T-TaS2_2 by ultrafast light

Distinguishing and controlling the extent of Mottness is important for materials where the energy scales of the onsite Coulomb repulsion U and the bandwidth W are comparable. Here we report the ultrafast electronic dynamics of 1T-TaS$_2$ by ultrafast time- and angle-resolved photoemission spectroscopy. A comparison of the electron dynamics for the newly-discovered intermediate phase (I-phase) as well as the low-temperature commensurate charge density wave (C-CDW) phase shows distinctive dynamics. While the I-phase is characterized by an instantaneous response and nearly time-resolution-limited fast relaxation (~200 fs), the C-CDW phase shows a delayed response and a slower relaxation (a few ps). Such distinctive dynamics refect the different relaxation mechanisms and provide nonequilibrium signatures to distinguish the Mott insulating I-phase from the C-CDW band insulating phase. Moreover, a light-induced bandwidth reduction is observed in the C-CDW phase, pushing it toward the Mott insulating phase. Our work demonstrates the power of ultrafast light-matter interaction in both distinguishing and controlling the extent of Mottness on the ultrafast timescale

Road Traffic Law Adaptive Decision-making for Self-Driving Vehicles

Self-driving vehicles have their own intelligence to drive on open roads. However, vehicle managers, e.g., government or industrial companies, still need a way to tell these self-driving vehicles what behaviors are encouraged or forbidden. Unlike human drivers, current self-driving vehicles cannot understand the traffic laws, thus rely on the programmers manually writing the corresponding principles into the driving systems. It would be less efficient and hard to adapt some temporary traffic laws, especially when the vehicles use data-driven decision-making algorithms. Besides, current self-driving vehicle systems rarely take traffic law modification into consideration. This work aims to design a road traffic law adaptive decision-making method. The decision-making algorithm is designed based on reinforcement learning, in which the traffic rules are usually implicitly coded in deep neural networks. The main idea is to supply the adaptability to traffic laws of self-driving vehicles by a law-adaptive backup policy. In this work, the natural language-based traffic laws are first translated into a logical expression by the Linear Temporal Logic method. Then, the system will try to monitor in advance whether the self-driving vehicle may break the traffic laws by designing a long-term RL action space. Finally, a sample-based planning method will re-plan the trajectory when the vehicle may break the traffic rules. The method is validated in a Beijing Winter Olympic Lane scenario and an overtaking case, built in CARLA simulator. The results show that by adopting this method, the self-driving vehicles can comply with new issued or updated traffic laws effectively. This method helps self-driving vehicles governed by digital traffic laws, which is necessary for the wide adoption of autonomous driving

Floquet engineering of black phosphorus upon below-gap pumping

Time-periodic light field can dress the electronic states and lead to light-induced emergent properties in quantum materials. While below-gap pumping is regarded favorable for Floquet engineering, so far direct experimental evidence of momentum-resolved band renormalization still remains missing. Here, we report experimental evidence of light-induced band renormalization in black phosphorus by pumping at photon energy of 160 meV which is far below the band gap, and the distinction between below-gap pumping and near-resonance pumping is revealed. Our work demonstrates light-induced band engineering upon below-gap pumping, and provides insights for extending Floquet engineering to more quantum materials

Prognostic Value of MicroRNA-20b in Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is a highly heterogeneous disease that requires fine-grained risk stratification for the best prognosis of patients. As a class of small non-coding RNAs with important biological functions, microRNAs play a crucial role in the pathogenesis of AML. To assess the prognostic impact of miR-20b on AML in the presence of other clinical and molecular factors, we screened 90 AML patients receiving chemotherapy only and 74 also undergoing allogeneic hematopoietic stem cell transplantation (allo-HSCT) from the Cancer Genome Atlas (TCGA) database. In the chemotherapy-only group, high miR-20b expression subgroup had shorter event-free survival (EFS) and overall survival (OS, both P < 0.001); whereas, there were no significant differences in EFS and OS between high and low expression subgroups in the allo-HSCT group. Then we divided all patients into high and low expression groups based on median miR-20b expression level. In the high expression group, patients treated with allo-HSCT had longer EFS and OS than those with chemotherapy alone (both P < 0.01); however, there were no significant differences in EFS and OS between different treatment subgroups in the low expression group. Further analysis showed that miR-20b was negatively correlated with genes in "ribosome," "myeloid leukocyte mediated immunity," and "DNA replication" signaling pathways. ORAI2, the gene with the strongest correlation with miR-20b, also had significant prognostic value in patients undergoing chemotherapy but not in the allo-HSCT group. In conclusion, our findings suggest that high miR-20b expression is a poor prognostic indicator for AML, but allo-HSCT may override its prognostic impact

Revealing the two-dimensional electronic structure and anisotropic superconductivity in a natural van der Waals superlattice (PbSe)1.14_{1.14}NbSe2_2

Van der Waals superlattices are important for tailoring the electronic structures and properties of layered materials. Here we report the superconducting properties and electronic structure of a natural van der Waals superlattice (PbSe)$_{1.14}$NbSe$_2$. Anisotropic superconductivity with a transition temperature $T_c$ = 5.6 $\pm$ 0.1 K, which is higher than monolayer NbSe$_2$, is revealed by transport measurements on high-quality samples. Angle-resolved photoemission spectroscopy (ARPES) measurements reveal the two-dimensional electronic structure and a charge transfer of 0.43 electrons per NbSe$_2$ unit cell from the blocking PbSe layer. In addition, polarization-dependent ARPES measurements reveal a significant circular dichroism with opposite contrast at K and K' valleys, suggesting a significant spin-orbital coupling and distinct orbital angular momentum. Our work suggests natural van der Waals superlattice as an effective pathway for achieving intriguing properties distinct from both the bulk and monolayer samples.Comment: 8 pages, 4 figure