The Relationship Between An Auditing Firm's Characteristics And The Incidence Rate Of Its Clients Subject To AAERs

This paper examines the relationship between an auditors characteristics and the incidence rate of its client subject to the Accounting and Auditing Enforcement Release. Using the sample of AAERs from 2002 to 2006, we find that when a firm is audited from a large accounting firm, there is a significantly less incidence rate subject to AAERs. Also, we find that the audit time of AAERs firms is significantly less than that of non-AAERs firms. Because AAER is related with audit quality, it implies that AAER depends on audit time and audit firm size, and that a firm is affected by the incidence rate of subjects toward AAERs. However, there is no difference between the audit fee of AAERs firms audit fee and that of non-AAERs firms. Although audit time leads to a high audit fee, audit firms are very competitive and therefore, there are some limitations with receiving a high audit fee according to audit time. Therefore, the audit fee is significantly affected by the incidence rate of subjects toward AAERs. Additionally, we also examine the effectiveness of AAERs and the difference of audit efforts depending on the cause of AAERs and the degree of penalties imposed by FSS. Overall, the results suggest that depending on the auditors characteristics, such as the size of accounting firm, audit time, and audit fee, a company is affected by the incidence rate subject to AAERs

Multi-view Temporal Ensemble for Classification of Non-Stationary Signals

In the classification of non-stationary time series data such as sounds, it is often tedious and expensive to get a training set that is representative of the target concept. To alleviate this problem, the proposed method treats the outputs of a number of deep learning sub-models as the views of the same target concept that can be linearly combined according to their complementarity. It is proposed that the view’s complementarity be the contribution of the view to the global view, chosen in this work to be the Laplacian eigenmap of the combined data. Complementarity is computed by alternate optimization, a process that involves the cost function of the Laplacian eigenmap and the weights of the linear combination. By blending the views in this way, a more complete view of the underlying phenomenon can be made available to the final classifier. Better generalization is obtained, as the consensus between the views reduces the variance while the increase in the discriminatory information reduces the bias. Data experiment with artificial views of environment sounds formed by deep learning structures of different configurations shows that the proposed method can improve the classification performance

Miuraea migitae, a new record of the order Bangiales (Bangiophyceae, Rhodophyta) from Korea

Abstract We found specimens of foliose Bangiales from the subtidal zone of Udo, Jeju Island, Korea. In molecular analyses of rbcL sequences, these Korean specimens were almost identical to Miuraea migitae from Osaka, Japan. In the morphological comparison, Korean specimens were consistent with habitat, color, and vegetative characteristics with the description of M. migitae. This is the first record of M. migitae outside the type locality and Nagasaki in Japan. This study confirms that new or unrecorded species of the order Bangiales may be discovered from subtidal habitats

Tool to visualize and evaluate operator proficiency in laser hair-removal treatments

BACKGROUND: The uniform delivery of laser energy is particularly important for safe and effective laser hair removal (LHR) treatment. Although it is necessary to quantitatively assess the spatial distribution of the delivered laser, laser spots are difficult to trace owing to a lack of visual cues. This study proposes a novel preclinic tool to evaluate operator proficiency in LHR treatment and applies this tool to train novice operators and compare two different treatment techniques (sliding versus spot-by-spot). METHODS: A simulation bed is constructed to visualize the irradiated laser spots. Six novice operators are recruited to perform four sessions of simulation while changing the treatment techniques and the presence of feedback (sliding without feedback, sliding with feedback, spot-by-spot without feedback, and spot-by-spot with feedback). Laser distribution maps (LDMs) are reconstructed through a series of images processed from the recorded video for each simulation session. Then, an experienced dermatologist classifies the collected LDMs into three different performance groups, which are quantitatively analyzed in terms of four performance indices. RESULTS: The performance groups are characterized by using a combination of four proposed indices. The best-performing group exhibited the lowest amount of randomness in laser delivery and accurate estimation of mean spot distances. The training was only effective in the sliding treatment technique. After the training, omission errors decreased by 6.32% and better estimation of the mean spot distance of the actual size of the laser-emitting window was achieved. Gels required operators to be trained when the spot-by-spot technique was used, and imposed difficulties in maintaining regular laser delivery when the sliding technique was used. CONCLUSIONS: Because the proposed system is simple and highly affordable, it is expected to benefit many operators in clinics to train and maintain skilled performance in LHR treatment, which will eventually lead to accomplishing a uniform laser delivery for safe and effective LHR treatment

7-Dimethyl­amino-2-phenyl-1,2,4-triazolo[1,5-a][1,3,5]triazin-5-amine methanol solvate1

7-Dimethyl­amino-2-phenyl-1,2,4-triazolo[1,5-a][1,3,5]triazin-5-amine crystallized with one mol­ecule of methanol to give the title compound, C12H13N7·CH3OH. The triazolo[1,5-a][1,3,5]triazine heterocyclic core is essentially planar as are both amino groups that are involved in π-electron delocalization with the triazolo[1,5-a][1,3,5]triazine nucleus. The methyl groups of the dimethyl­amino fragment are involved in the formation of weak intra­molecular C—H⋯N hydrogen bonds with the N atoms of the heterocyclic system. The crystal packing is stabilized by inter­molecular N—H⋯N hydrogen bonds between the triazolo[1,5-a][1,3,5]triazine mol­ecules. The methanol solvent mol­ecule also participates in the formation of the crystal structure via inter­molecular O—H⋯N, N—H⋯O and weak C—H⋯O hydrogen bonds, linking the layers of triazolo[1,5-a][1,3,5]triazine mol­ecules

Dual quadratic differentials and entire minimal graphs in Heisenberg space

We define holomorphic quadratic differentials for spacelike surfaces with constant mean curvature in the Lorentzian homogeneous spaces $\mathbb{L}(\kappa,\tau)$ with isometry group of dimension 4, which are dual to the Abresch-Rosenberg differentials in the Riemannian counterparts $\mathbb{E}(\kappa,\tau)$, and obtain some consequences. On the one hand, we give a very short proof of the Bernstein problem in Heisenberg space, and provide a geometric description of the family of entire graphs sharing the same differential in terms of a 2-parameter conformal deformation. On the other hand, we prove that entire minimal graphs in Heisenberg space have negative Gauss curvature.Comment: 19 page

Manipulation of Rat Movement via Nigrostriatal Stimulation Controlled by Human Visually Evoked Potentials

Here, we report that the development of a brain-to-brain interface (BBI) system that enables a human user to manipulate rat movement without any previous training. In our model, the remotely-guided rats (known as ratbots) successfully navigated a T-maze via contralateral turning behaviour induced by electrical stimulation of the nigrostriatal (NS) pathway by a brain-computer interface (BCI) based on the human controller's steady-state visually evoked potentials (SSVEPs). The system allowed human participants to manipulate rat movement with an average success rate of 82.2% and at an average rat speed of approximately 1.9 m/min. The ratbots had no directional preference, showing average success rates of 81.1% and 83.3% for the left-and right-turning task, respectively. This is the first study to demonstrate the use of NS stimulation for developing a highly stable ratbot that does not require previous training, and is the first instance of a training-free BBI for rat navigation. The results of this study will facilitate the development of borderless communication between human and untrained animals, which could not only improve the understanding of animals in humans, but also allow untrained animals to more effectively provide humans with information obtained with their superior perception.11Ysciescopu

Deep Q‐network implementation for simulated autonomous vehicle control

Deep reinforcement learning is poised to be a revolutionised step towards newer possibilities in solving navigation and autonomous vehicle control tasks. Deep Q‐network (DQN) is one of the more popular methods of deep reinforcement learning that allows the agent that controls the vehicle to learn through its mistakes based on its actions and interactions with the environment. This paper presents the implementation of DQN to an autonomous self‐driving vehicle control in two different simulated environments; first environment is in Python which is a simple 2D environment and then advanced to Unity software separately which is a 3D environment. Based on the scores and pixel inputs, the agent in the vehicle learns and adapts to its surrounding. It develops the best solution strategy to direct itself in the environment where its task is to manoeuvre the vehicle from point to point on a simulated highway scenario. The implemented DQN technique approximates the action value function with convolutional neural network. This evaluates the Q‐function for the Q‐learning architecture and updates the action value function. This paper shows that DQN is an effective learning method for the agent of an autonomous vehicle. In both simulated environments, the autonomous vehicle gradually learnt the manoeuvre operations and progressively gained the ability to successfully navigate itself and avoid obstacles without prior information of the surrounding

Significance of Brainstem Auditory Evoked Potentials as an Initial Evaluation for Dizziness

The authors performed brainstem auditory evoked potential (BAEP) studies on 207 patients with dizziness symptoms, and evaluated the significance of BAEPs in differentiating among various causes of dizziness. The results showed abnormal BAEPs, which were suggestive of brainstem dysfunction, in 20 of 106 of the probable vertebrobasilar transient ischemic attack (VB TIA) group (18.8%), and in 12 of 101 of the vague dizziness (VD) group (11.8%). Additionally, there were abnormal BAEPs, which were suggestive of the end-organ dysfunction, in 4 of 101 of the VD group (3.9%). When we analysed 32 abnormal BAEPs suggestive of brainstem dysfunction, the most frequent BAEP abnormality was the prolongation of I-III interpeak latency OPt) (53.1%). Prolonged I-V IPL was the second most common abnormality (28.2%), with III-V IPL prolongation occurring less commonly (18.7%). The follow-up studies of abnormal BAEPs showed that the initial abnormal BAEPs reverted to normal in three of six patients, but in the remaining three the abnormality persisted during follow-up period of one to four years. Therefore, it is concluded that BAEP tests would be useful in differentiating the dizziness as one of those symptoms of brainstem dysfunction from non-brainstem syndrome which mimics it

MR imaging of hepatic lymphangioma

A case of primary hepatic lymphangioma with a microcystic component was incidentally found in a 75-year-old woman. Although ultrasonography (US) and computed tomography (CT) showed a mixed lesion including cystic and solid components, magnetic resonance imaging (MRI) demonstrated the morphologic characteristics of the lesion better than other modalities