The Non-Linear Relationship Between CEO Compensation Incentives And Corporate Tax Avoidance

This study examines the effect of CEO compensation incentives on corporate tax avoidance. Unlike prior literature that assumes a monotonic relation between executive compensation incentives and tax avoidance, we find a non-linear relation between the two. Specifically, we find that CEO compensation incentives exhibit a positive relation with corporate tax avoidance at low levels of compensation incentives, whereas they show a negative relation at high levels of compensation incentives. We further find that the non-linear relationship between CEO compensation incentives and corporate tax avoidance does not exist for the subsample of S&P500 firms. Collectively, we provide evidence of the two counter effective forces, namely, - the incentive alignment effect and the risk-reducing effect, - that help explain the effect of CEO compensation incentives on tax avoidance

AD-YOLO: You Look ONly Once in Training Multiple Sound Event Localization and Detection

Sound event localization and detection (SELD) combines the identification of sound events with the corresponding directions of arrival (DOA). Recently, event-oriented track output formats have been adopted to solve this problem; however, they still have limited generalization toward real-world problems in an unknown polyphony environment. To address the issue, we proposed an angular-distance-based multiple SELD (AD-YOLO), which is an adaptation of the "You Look Only Once" algorithm for SELD. The AD-YOLO format allows the model to learn sound occurrences location-sensitively by assigning class responsibility to DOA predictions. Hence, the format enables the model to handle the polyphony problem, regardless of the number of sound overlaps. We evaluated AD-YOLO on DCASE 2020-2022 challenge Task 3 datasets using four SELD objective metrics. The experimental results show that AD-YOLO achieved outstanding performance overall and also accomplished robustness in class-homogeneous polyphony environments.Comment: 5 pages, 3 figures, accepted for publication in IEEE ICASSP 202

Reliable diameter control of carbon nanotube nanowires using withdrawal velocity

Carbon nanotube (CNT) nanobundles are widely used in nanoscale imaging, fabrication, and electrochemical and biological sensing. The diameter of CNT nanobundles should be controlled precisely, because it is an important factor in determining electrode performance. Here, we fabricated CNT nanobundles on tungsten tips using dielectrophoresis (DEP) force and controlled their diameters by varying the withdrawal velocity of the tungsten tips. Withdrawal velocity pulling away from the liquid-air interface could be an important, reliable parameter to control the diameter of CNT nanobundles. The withdrawal velocity was controlled automatically and precisely with a one-dimensional motorized stage. The effect of the withdrawal velocity on the diameter of CNT nanobundles was analyzed theoretically and compared with the experimental results. Based on the attachment efficiency, the withdrawal velocity is inversely proportional to the diameter of the CNT nanobundles; this has been demonstrated experimentally. Control of the withdrawal velocity will play an important role in fabricating CNT nanobundles using DEP phenomena.110Ysciescopu

One-directional flow of ionic solutions along fine electrodes under an alternating current electric field

Electric fields are widely used for controlling liquids in various research fields. To control a liquid, an alternating current (AC) electric field can offer unique advantages over a direct current (DC) electric field, such as fast and programmable flows and reduced side effects, namely the generation of gas bubbles. Here, we demonstrate one-directional flow along carbon nanotube nanowires under an AC electric field, with no additional equipment or frequency matching. This phenomenon has the following characteristics: First, the flow rates of the transported liquid were changed by altering the frequency showing Gaussian behaviour. Second, a particular frequency generated maximum liquid flow. Third, flow rates with an AC electric field (approximately nanolitre per minute) were much faster than those of a DC electric field (approximately picolitre per minute). Fourth, the flow rates could be controlled by changing the applied voltage, frequency, ion concentration of the solution and offset voltage. Our finding of microfluidic control using an AC electric field could provide a new method for controlling liquids in various research fields

Solution-Processed Cu2_2S Nanostructures for Solar Hydrogen Production

Cu$_2$S is a promising solar energy conversion material due to its suitable optical properties, high elemental earth abundance, and nontoxicity. In addition to the challenge of multiple stable secondary phases, the short minority carrier diffusion length poses an obstacle to its practical application. This work addresses the issue by synthesizing nanostructured Cu$_2$S thin films, which enables increased charge carrier collection. A simple solution-processing method involving the preparation of CuCl and CuCl$_2$ molecular inks in a thiol-amine solvent mixture followed by spin coating and low-temperature annealing was used to obtain phase-pure nanostructured (nanoplate and nanoparticle) Cu$_2$S thin films. The photocathode based on the nanoplate Cu$_2$S (FTO/Au/Cu$_2$S/CdS/TiO$_2$/RuO$_x$) reveals enhanced charge carrier collection and improved photoelectrochemical water-splitting performance compared to the photocathode based on the non-nanostructured Cu$_2$S thin film reported previously. A photocurrent density of 3.0 mA cm$^{–2}$ at −0.2 versus a reversible hydrogen electrode (V$_{RHE}$) with only 100 nm thickness of a nanoplate Cu$_2$S layer and an onset potential of 0.43 V$_{RHE}$ were obtained. This work provides a simple, cost-effective, and high-throughput method to prepare phase-pure nanostructured Cu$_2$S thin films for scalable solar hydrogen production

Downregulated miR-18b-5p triggers apoptosis by inhibition of calcium signaling and neuronal cell differentiation in transgenic SOD1 (G93A) mice and SOD1 (G17S and G86S) ALS patients

Abstract Background MicroRNAs (miRNAs) are endogenous non-coding RNAs that regulate gene expression at the post-transcriptional level and are key modulators in neurodegenerative diseases. Overexpressed miRNAs play an important role in ALS; however, the pathogenic mechanisms of deregulated miRNAs are still unclear. Methods We aimed to assess the dysfunction of RNAs or miRNAs in fALS (SOD1 mutations). We compared the RNA-seq of subcellular fractions in NSC-34 WT (hSOD1) and MT (hSOD1 (G93A)) cells to find altered RNAs or miRNAs. We identified that Hif1α and Mef2c were upregulated, and Mctp1 and Rarb were downregulated in the cytoplasm of NSC-34 MT cells. Results SOD1 mutations decreased the level of miR-18b-5p. Induced Hif1α which is the target for miR-18b increased Mef2c expression as a transcription factor. Mef2c upregulated miR-206 as a transcription factor. Inhibition of Mctp1 and Rarb which are targets of miR-206 induces intracellular Ca2+ levels and reduces cell differentiation, respectively. We confirmed that miR-18b-5p pathway was also observed in G93A Tg, fALS (G86S) patient, and iPSC-derived motor neurons from fALS (G17S) patient. Conclusions Our data indicate that SOD1 mutation decreases miR-18b-5p, which sequentially regulates Hif1α, Mef2c, miR-206, Mctp1 and Rarb in fALS-linked SOD1 mutation. These results provide new insights into the downregulation of miR-18b-5p dependent pathogenic mechanisms of ALS

COMMA: Propagating Complementary Multi-Level Aggregation Network for Polyp Segmentation

Colonoscopy is an effective method for detecting polyps to prevent colon cancer. Existing studies have achieved satisfactory polyp detection performance by aggregating low-level boundary and high-level region information in convolutional neural networks (CNNs) for precise polyp segmentation in colonoscopy images. However, multi-level aggregation provides limited polyp segmentation owing to the distribution discrepancy that occurs when integrating different layer representations. To address this problem, previous studies have employed complementary low- and high- level representations. In contrast to existing methods, we focus on propagating complementary information such that the complementary low-level explicit boundary with abstracted high-level representations diminishes the discrepancy. This study proposes COMMA, which propagates complementary multi-level aggregation to reduce distribution discrepancies. COMMA comprises a complementary masking module (CMM) and a boundary propagation module (BPM) as a multi-decoder. The CMM masks the low-level boundary noises through the abstracted high-level representation and leverages the masked information at both levels. Similarly, the BPM incorporates the lowest- and highest-level representations to obtain explicit boundary information and propagates the boundary to the CMMs to improve polyp detection. CMMs can discriminate polyps more elaborately than prior CMMs based on boundary and complementary representations. Moreover, we propose a hybrid loss function to mitigate class imbalance and noisy annotations in polyp segmentation. To evaluate the COMMA performance, we conducted experiments on five benchmark datasets using five metrics. The results proved that the proposed network outperforms state-of-the-art methods in terms of all datasets. Specifically, COMMA improved mIoU performance by 0.043 on average for all datasets compared to the existing state-of-the-art methods

TRACER: Extreme Attention Guided Salient Object Tracing Network (Student Abstract)

Existing studies on salient object detection (SOD) focus on extracting distinct objects with edge features and aggregating multi-level features to improve SOD performance. However, both performance gain and computational efficiency cannot be achieved, which has motivated us to study the inefficiencies in existing encoder-decoder structures to avoid this trade-off. We propose TRACER which excludes multi-decoder structures and minimizes the learning parameters usage by employing attention guided tracing modules (ATMs), as shown in Fig. 1

COMMA: Propagating Complementary Multi-Level Aggregation Network for Polyp Segmentation

Colonoscopy is an effective method for detecting polyps to prevent colon cancer. Existing studies have achieved satisfactory polyp detection performance by aggregating low-level boundary and high-level region information in convolutional neural networks (CNNs) for precise polyp segmentation in colonoscopy images. However, multi-level aggregation provides limited polyp segmentation owing to the distribution discrepancy that occurs when integrating different layer representations. To address this problem, previous studies have employed complementary low- and high- level representations. In contrast to existing methods, we focus on propagating complementary information such that the complementary low-level explicit boundary with abstracted high-level representations diminishes the discrepancy. This study proposes COMMA, which propagates complementary multi-level aggregation to reduce distribution discrepancies. COMMA comprises a complementary masking module (CMM) and a boundary propagation module (BPM) as a multi-decoder. The CMM masks the low-level boundary noises through the abstracted high-level representation and leverages the masked information at both levels. Similarly, the BPM incorporates the lowest- and highest-level representations to obtain explicit boundary information and propagates the boundary to the CMMs to improve polyp detection. CMMs can discriminate polyps more elaborately than prior CMMs based on boundary and complementary representations. Moreover, we propose a hybrid loss function to mitigate class imbalance and noisy annotations in polyp segmentation. To evaluate the COMMA performance, we conducted experiments on five benchmark datasets using five metrics. The results proved that the proposed network outperforms state-of-the-art methods in terms of all datasets. Specifically, COMMA improved mIoU performance by 0.043 on average for all datasets compared to the existing state-of-the-art methods

Numerical model for compression molding process of hybridly laminated thermoplastic composites based on anisotropic rheology

In this work, a numerical model is developed for simulating the compression molding process of a hybrid composite material, alternately laminated with continuous and discontinuous fiber-reinforced layers. Although various process simulation models are already available for plastic materials embedding each type of the reinforcements, they are incapable of simultaneously dealing with the continuity and discontinuity. Here, thermomechanical behavior of the continuous fiber-reinforced layer and rheological behavior of the discontinuous fiber-reinforced layer are separately modeled and eventually integrated assuming perfectly bonded interfaces. The unified process model is applied to the simulation of compression molding of a full-scale battery pack structure of an electric vehicle. A simple yet robust rheology test is utilized to measure rheological properties necessary for the numerical simulation. In the full-scale simulation, thermoforming process of the hybrid charge is successfully simulated and fiber direction changes due to the suspension flow are also predicted. It is found that the reoriented fibers significantly affect stress distributions at the final stage of the process. The process model developed in the present study can be implemented into either the Lagrangian or Eulerian framework