TILDE-Q: A Transformation Invariant Loss Function for Time-Series Forecasting

Time-series forecasting has caught increasing attention in the AI research field due to its importance in solving real-world problems across different domains, such as energy, weather, traffic, and economy. As shown in various types of data, it has been a must-see issue to deal with drastic changes, temporal patterns, and shapes in sequential data that previous models are weak in prediction. This is because most cases in time-series forecasting aim to minimize $L_p$ norm distances as loss functions, such as mean absolute error (MAE) or mean square error (MSE). These loss functions are vulnerable to not only considering temporal dynamics modeling but also capturing the shape of signals. In addition, these functions often make models misbehave and return uncorrelated results to the original time-series. To become an effective loss function, it has to be invariant to the set of distortions between two time-series data instead of just comparing exact values. In this paper, we propose a novel loss function, called TILDE-Q (Transformation Invariant Loss function with Distance EQuilibrium), that not only considers the distortions in amplitude and phase but also allows models to capture the shape of time-series sequences. In addition, TILDE-Q supports modeling periodic and non-periodic temporal dynamics at the same time. We evaluate the effectiveness of TILDE-Q by conducting extensive experiments with respect to periodic and non-periodic conditions of data, from naive models to state-of-the-art models. The experiment results indicate that the models trained with TILDE-Q outperform those trained with other training metrics (e.g., MSE, dynamic time warping (DTW), temporal distortion index (TDI), and longest common subsequence (LCSS)).Comment: 9 pages paper, 2 pages references, and 7 pages appendix. Submitted as conference paper to ICLR 202

A SIMULTANEOUS VIEW INTERPOLATION AND MULTIPLEXING METHOD USING STEREO IMAGE PAIRS FOR LENTICULAR DISPLAY

ABSTRACT Nowadays, the slanted lenticular display becomes a representative one among the commercially introduced autostereoscopic displays. The paper presents a simple method to correct the lenticular alignment error by compensating the correction coefficients to the view number determination formula. Then, based on the corrected view numbers, the proposed algorithm simultaneously performs floating-point viewpoint generation and multiplexing on the scanline using the stereo image pairs and its depth information. Experimental results show that lenticular images, in which distortion and artifact due to lenticular alignment error are considerably reduced, are generated rapidly by using the proposed algorithm. Index Terms-Three-dimensional displays, Stereo vision

Congenital miliary tuberculosis in an 18-day-old boy

Congenital tuberculosis (TB) is a rare disease that is associated with high mortality. Mycobacterium tuberculosis, the causative agent, may be transmitted from the infected mother to the fetus by the transplacental route or by aspiration of infected amniotic fluid. Clinical symptoms and signs are not specific. Miliary patterns are the most common findings in the chest X-rays of many infants with congenital TB. In this case, an 18-day-old boy had jaundice on the fifth day of birth, and fever and respiratory distress appeared on the 18th day. Chest X-ray showed diffuse fine bilateral infiltration. Clinically, pneumonia or sepsis was suspected. Respiratory symptoms and chest X-ray findings worsened despite empirical antibiotic therapy. The lungs showed miliary infiltration suggestive of TB. Gastric aspirates were positive for M. tuberculosis. Respiratory distress and fever were gradually improved after anti-TB medication. Congenital TB is difficult to detect because of minimal or no symptoms during pregnancy and nonspecific symptoms in neonates. Hence, clinicians should suspect the possibility of TB infection even if neonates have non-specific symptoms. Early diagnosis and meticulous treatment are required for the survival of neonates with TB

Superior outcomes of kidney transplantation compared with dialysis An optimal matched analysis of a national population-based cohort study between 2005 and 2008 in Korea

Data regarding kidney transplantation (KT) and dialysis outcomes are rare in Asian populations. In the present study, we evaluated the clinical outcomes associated with KT using claims data from the Korean national public health insurance program. Among the 35,418 adult patients with incident dialysis treated between 2005 and 2008 in Korea, 1539 underwent KT. An optimal balanced risk set matching was attempted to compare the transplant group with the control group in terms of the overall survival and major adverse cardiac event-free survival. Before matching, the dialysis group was older and had more comorbidities. After matching, there were no differences in age, sex, dialysis modalities, or comorbidities. Patient survival was significantly better in the transplant group than in the matched control group (P<0.001). In addition, the transplant group showed better major adverse cardiac event-free survival than the dialysis group (P<0.001; hazard ratio, 0.49; 95% confidence interval, 0.32-0.75). Korean patients with incident dialysis who underwent long-term dialysis had significantly more cardiovascular events and higher all-cause mortality rates than those who underwent KT. Thus, KT should be more actively recommended in Korean populations.OAIID:RECH_ACHV_DSTSH_NO:T201619962RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A079841CITE_RATE:1.206FILENAME:Superior_outcomes_of_kidney_transplantation.14.pdfDEPT_NM:컴퓨터공학부EMAIL:[email protected]_YN:YFILEURL:https://srnd.snu.ac.kr/eXrepEIR/fws/file/91ab95e8-39ce-4d72-8fb3-f6b12c82256d/linkCONFIRM:

Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecasting

Traffic forecasting is a challenging problem due to complex road networks and sudden speed changes caused by various events on roads. Several models have been proposed to solve this challenging problem, with a focus on learning the spatio-temporal dependencies of roads. In this work, we propose a new perspective for converting the forecasting problem into a pattern-matching task, assuming that large traffic data can be represented by a set of patterns. To evaluate the validity of this new perspective, we design a novel traffic forecasting model called Pattern-Matching Memory Networks (PM-MemNet), which learns to match input data to representative patterns with a key-value memory structure. We first extract and cluster representative traffic patterns that serve as keys in the memory. Then, by matching the extracted keys and inputs, PM-MemNet acquires the necessary information on existing traffic patterns from the memory and uses it for forecasting. To model the spatio-temporal correlation of traffic, we proposed a novel memory architecture, GCMem, which integrates attention and graph convolution. The experimental results indicate that PM-MemNet is more accurate than state-of-the-art models, such as Graph WaveNet, with higher responsiveness. We also present a qualitative analysis describing how PM-MemNet works and achieves higher accuracy when road speed changes rapidly