WaveBound: Dynamic Error Bounds for Stable Time Series Forecasting

Time series forecasting has become a critical task due to its high practicality in real-world applications such as traffic, energy consumption, economics and finance, and disease analysis. Recent deep-learning-based approaches have shown remarkable success in time series forecasting. Nonetheless, due to the dynamics of time series data, deep networks still suffer from unstable training and overfitting. Inconsistent patterns appearing in real-world data lead the model to be biased to a particular pattern, thus limiting the generalization. In this work, we introduce the dynamic error bounds on training loss to address the overfitting issue in time series forecasting. Consequently, we propose a regularization method called WaveBound which estimates the adequate error bounds of training loss for each time step and feature at each iteration. By allowing the model to focus less on unpredictable data, WaveBound stabilizes the training process, thus significantly improving generalization. With the extensive experiments, we show that WaveBound consistently improves upon the existing models in large margins, including the state-of-the-art model.Comment: NeurIPS 202

AutoCycle-VC: Towards Bottleneck-Independent Zero-Shot Cross-Lingual Voice Conversion

This paper proposes a simple and robust zero-shot voice conversion system with a cycle structure and mel-spectrogram pre-processing. Previous works suffer from information loss and poor synthesis quality due to their reliance on a carefully designed bottleneck structure. Moreover, models relying solely on self-reconstruction loss struggled with reproducing different speakers' voices. To address these issues, we suggested a cycle-consistency loss that considers conversion back and forth between target and source speakers. Additionally, stacked random-shuffled mel-spectrograms and a label smoothing method are utilized during speaker encoder training to extract a time-independent global speaker representation from speech, which is the key to a zero-shot conversion. Our model outperforms existing state-of-the-art results in both subjective and objective evaluations. Furthermore, it facilitates cross-lingual voice conversions and enhances the quality of synthesized speech

MAPK/ERK Signaling in Osteosarcomas, Ewing Sarcomas and Chondrosarcomas: Therapeutic Implications and Future Directions

The introduction of cytotoxic chemotherapeutic drugs in the 1970's improved the survival rate of patients with bone sarcomas and allowed limb salvage surgeries. However, since the turn of the century, survival data has plateaued for a subset of metastatic, nonresponding osteo, and/or Ewing sarcomas. In addition, most high-grade chondrosarcoma does not respond to current chemotherapy. With an increased understanding of molecular pathways governing oncogenesis, modern targeted therapy regimens may enhance the efficacy of current therapeutic modalities. Mitogen-Activated Protein Kinases (MAPK)/Extracellular-Signal-Regulated Kinases (ERK) are key regulators of oncogenic phenotypes such as proliferation, invasion, angiogenesis, and inflammatory responses; which are the hallmarks of cancer. Consequently, MAPK/ERK inhibitors have emerged as promising therapeutic targets for certain types of cancers, but there have been sparse reports in bone sarcomas. Scattered papers suggest that MAPK targeting inhibits proliferation, local invasiveness, metastasis, and drug resistance in bone sarcomas. A recent clinical trial showed some clinical benefits in patients with unresectable or metastatic osteosarcomas following MAPK/ERK targeting therapy. Despite in vitro proof of therapeutic concept, there are no sufficient in vivo or clinical data available for Ewing sarcomas or chondrosarcomas. Further experimental and clinical trials are awaited in order to bring MAPK targeting into a clinical arena

Deep Imbalanced Time-series Forecasting via Local Discrepancy Density

Time-series forecasting models often encounter abrupt changes in a given period of time which generally occur due to unexpected or unknown events. Despite their scarce occurrences in the training set, abrupt changes incur loss that significantly contributes to the total loss. Therefore, they act as noisy training samples and prevent the model from learning generalizable patterns, namely the normal states. Based on our findings, we propose a reweighting framework that down-weights the losses incurred by abrupt changes and up-weights those by normal states. For the reweighting framework, we first define a measurement termed Local Discrepancy (LD) which measures the degree of abruptness of a change in a given period of time. Since a training set is mostly composed of normal states, we then consider how frequently the temporal changes appear in the training set based on LD. Our reweighting framework is applicable to existing time-series forecasting models regardless of the architectures. Through extensive experiments on 12 time-series forecasting models over eight datasets with various in-output sequence lengths, we demonstrate that applying our reweighting framework reduces MSE by 10.1% on average and by up to 18.6% in the state-of-the-art model.Comment: Accepted at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD) 202

Remote health monitoring services in nursing homes

Aged people are challenged by serious complications from chronic diseases, such as mood disorder, diabetes, heart disease, and infectious diseases, which are also the most common causes of death in older people. Therefore, elderly care facilities are more important than ever. The most common causes of death in elderly care facilities were reported to be diabetes, cardiovascular disease, and pneumonia. Recently, the coronavirus disease 2019 (COVID-19) pandemic have a great impact on blind spots of safety where aged people were isolated from society. Elderly care facilities were one of the blind spots in the midst of the pandemic, where major casualties were reported from COVID-19 complications because most people had one or two mortality risk factors, such as diabetes or cardiovascular disease. Therefore, medical governance of public health center and hospital, and elderly care facility is becoming important issue of priority. Thus, remote health monitoring service by the Internet of Medical Things (IoMT) sensors is more important than ever. Recently, technological breakthroughs have enabled healthcare professionals to have easy access to patients in medical blind spots through the use of IoT sensors. These sensors can detect medically urgent situations in a timely fashion and make medical decisions for aged people in elderly care facilities. Real-time electrocardiogram and blood sugar monitoring sensors are approved by the medical insurance service. Real-time monitoring services in medical blind spots, such as elderly care facilities, has been suggested. Heart rhythm monitoring could play a role in detecting early cardiovascular disease events and monitoring blood glucose levels in the management of chronic diseases, such as diabetes, in aged people in elderly care facilities. This review presents the potential usefulness of remote monitoring with IoMT sensors in medical blind spots and clinical suggestions for applications

The dispensability of VH-VL pairing and the indispensability of VL domain integrity in the IgG1 secretion process

Introduction: The CH1 domain of IgG antibodies controls assembly and secretion, mediated by the molecular chaperone BiP via the endoplasmic reticulum protein quality control (ERQC) mechanism. However, it is not clear whether the variable domains are necessary for this process.Methods: Here, we generated IgG1 antibodies in which the V domain (VH and/or VL) was either removed or replaced, and then assessed expression, assembly, and secretion in HEK293 cells.Results: All Ig variants formed a covalent linkage between the Cγ1 and Cκ, were successfully secreted in an assembled form. Replacement of the cognate Vκ with a non-secretory pseudo Vκ (ψVκ) hindered secretion of individual or assembled secretion of neither heavy chains (HCs) nor light chains (LCs). The ψLC (ψVκ-Cκ) exhibited a less folded structure compared to the wild type (wt) LC, as evidenced by enhanced stable binding to the molecular chaperone BiP and susceptibility to proteolytic degradation. Molecular dynamics simulation demonstrated dramatic alterations in overall structure of ψFab (Fd-ψLC) from wt Fab.Discussion: These findings suggest that V domains do not initiate HC:LC assembly and secretion; instead, the critical factor governing IgG assembly and secretion is the CH-CL pairing. Additionally, the structural integrity of the VL domain is crucial for IgG secretion. These data offer valuable insight into the design of bioactive molecules based on an IgG backbone

IPCC, 2023: Climate Change 2023: Synthesis Report, Summary for Policymakers. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, H. Lee and J. Romero (eds.)]. IPCC, Geneva, Switzerland.

This Synthesis Report (SYR) of the IPCC Sixth Assessment Report (AR6) summarises the state of knowledge of climate change, its widespread impacts and risks, and climate change mitigation and adaptation. It integrates the main findings of the Sixth Assessment Report (AR6) based on contributions from the three Working Groups1 , and the three Special Reports. The summary for Policymakers (SPM) is structured in three parts: SPM.A Current Status and Trends, SPM.B Future Climate Change, Risks, and Long-Term Responses, and SPM.C Responses in the Near Term.This report recognizes the interdependence of climate, ecosystems and biodiversity, and human societies; the value of diverse forms of knowledge; and the close linkages between climate change adaptation, mitigation, ecosystem health, human well-being and sustainable development, and reflects the increasing diversity of actors involved in climate action. Based on scientific understanding, key findings can be formulated as statements of fact or associated with an assessed level of confidence using the IPCC calibrated language

Wide-Angle Beam-Steering Using an Optical Phased Array with Non-Uniform-Width Waveguide Radiators

We demonstrate wide-angle beam-steering using an optical phased array (OPA) with waveguide radiators designed with non-uniform widths to reduce the crosstalk between waveguides. The OPA consists of a silicon based 1 × 16 array of electro-optic phase shifters and end-fire radiators. The 16 radiators were configured with four different widths and a half-wavelength spacing, which can remove the higher-order diffraction patterns in free space. The waveguides showed a low crosstalk of −10.2 dB at a wavelength of 1540 nm. With phase control, the OPA achieved wide beam-steering of over ±80° with a side-lobe suppression of 7.4 dB

Association of Primary Hypertension and Risk of Cerebrovascular Diseases with Overweight and Physical Activity in Korean Women: A Longitudinal Study

Cerebrovascular diseases include stroke, intracranial stenosis, aneurysms, and vascular malformations; primary hypertension is typically associated with cerebrovascular disease. The incidence of these diseases is higher in men than in women, and low physical activity and obesity are known to increase the risk of cerebrovascular disease. This study aimed to longitudinally analyze the adjusted relative risk (ARR) of primary hypertension and cerebrovascular diseases, in relation to body mass index (BMI) and physical activity (PA), in Korean women. The study retrieved the data of 1,464,377 adult Korean women (aged 50–79 years), who participated in the national health screening program from 2002 to 2003. The participants had no history of primary hypertension or cerebrovascular diseases, and were followed up by the International Statistical Classification of Diseases and Related Health Problems (ICD) until 2013. The participants were divided into the following groups: normal weight (18.5–24.9), overweight (25.0–29.9), and obese (≥30.0) kg/m2, based on the World Health Organization (WHO) classification. The frequency of PA (days) was determined using a physical activity questionnaire, and defined as low (0–2), medium (3–4), and high (5–7) days. The RR was calculated using Cox regression. Three models were created based on the adjusted variables. The ARR for hypertension was 0.933 (95% CI; 0.920–0.955, p < 0.001) in obese patients with medium PA. Primary hypertension was lower (ARR: 0.943; 95% CI; 0.928–0.961, p < 0.001) in overweight participants with medium PA, than in those with low PA. The incidence of cerebrovascular disease was lower in overweight individuals with medium PA (ARR: 0.945, 95% CI; 0.925–0.976, p < 0.001), than in those with low PA. The risk of cerebrovascular disease was reduced in normal-weight participants with medium PA (ARR: 0.889; 95% CI: 0.854–0.919; p < 0.001), than in those with high PA (ARR 0.913; 95% CI; 0.889–0.953, p < 0.001). In the obese group, there was no significant difference in the risk of cerebrovascular disease, based on the frequency of PA. In conclusion, the relative risk of primary hypertension in women was lower with moderate activity than with low activity, in the normal-weight and overweight groups. The relative risk of cerebrovascular disease was lower in the participants with moderate and high activity than in those with low activity, even at normal weight. In obese individuals, moderate and high activity reduced cerebrovascular disease compared to low activity. Therefore, regardless of obesity, PA may contribute to the prevention of primary hypertension and cerebrovascular disease in adult women

Image Retrieval Using the Combination of Global and Local Descriptors

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