Data Dropout: Optimizing Training Data for Convolutional Neural Networks

Deep learning models learn to fit training data while they are highly expected to generalize well to testing data. Most works aim at finding such models by creatively designing architectures and fine-tuning parameters. To adapt to particular tasks, hand-crafted information such as image prior has also been incorporated into end-to-end learning. However, very little progress has been made on investigating how an individual training sample will influence the generalization ability of a model. In other words, to achieve high generalization accuracy, do we really need all the samples in a training dataset? In this paper, we demonstrate that deep learning models such as convolutional neural networks may not favor all training samples, and generalization accuracy can be further improved by dropping those unfavorable samples. Specifically, the influence of removing a training sample is quantifiable, and we propose a Two-Round Training approach, aiming to achieve higher generalization accuracy. We locate unfavorable samples after the first round of training, and then retrain the model from scratch with the reduced training dataset in the second round. Since our approach is essentially different from fine-tuning or further training, the computational cost should not be a concern. Our extensive experimental results indicate that, with identical settings, the proposed approach can boost performance of the well-known networks on both high-level computer vision problems such as image classification, and low-level vision problems such as image denoising

Instance-based Deep Transfer Learning

Deep transfer learning recently has acquired significant research interest. It makes use of pre-trained models that are learned from a source domain, and utilizes these models for the tasks in a target domain. Model-based deep transfer learning is probably the most frequently used method. However, very little research work has been devoted to enhancing deep transfer learning by focusing on the influence of data. In this paper, we propose an instance-based approach to improve deep transfer learning in a target domain. Specifically, we choose a pre-trained model from a source domain and apply this model to estimate the influence of training samples in a target domain. Then we optimize the training data of the target domain by removing the training samples that will lower the performance of the pre-trained model. We later either fine-tune the pre-trained model with the optimized training data in the target domain, or build a new model which is initialized partially based on the pre-trained model, and fine-tune it with the optimized training data in the target domain. Using this approach, transfer learning can help deep learning models to capture more useful features. Extensive experiments demonstrate the effectiveness of our approach on boosting the quality of deep learning models for some common computer vision tasks, such as image classification.Comment: Accepted to WACV 2019. This is a preprint versio

Top-Quark FCNC Decay t->cgg in Topcolor-assisted Technicolor Model

The topcolor-assisted technicolor (TC2) model predicts several pseudo-scalars called top-pions and at loop level they can induce the FCNC top quark decay t->cgg which is extremely suppressed in the Standard Model (SM). We find that in the allowed parameter space the TC2 model can greatly enhance such a FCNC decay and push the branching ratio up to 10^{-3}, which is much larger than the predictions in the SM (10^{-9}) and in the minimal supersymmetric model (10^{-4}). We also compare the result with the two-body FCNC decay t-> cg and find that the branching ratio of t-> cgg is slightly larger than t-> cg. Such enhanced FCNC top quark decays may serve as a good probe of TC2 model at the future top quark factory.Comment: 11 pages, 4 figure

Measuring the Effectiveness of Guilt Appeals in the Promotion of Certified Products

The purpose of the research aims to examine the effectiveness of anticipatory guilt appeals on Fairtrade certified products. The results show consumers’ willingness to pay more for Fairtrade certified products when aroused by anticipatory guilt. The results have also shown the importance of varying the levels of perceived inferences of manipulative intent to desired levels of guilt arousal. Self-efficacy has been shown to moderate the relationship between anticipatory guilt arousal and willingness to pay more

The Biophysical and Physiological Properties of TMEM150C and TMEM16H

학위논문 (박사) -- 서울대학교 대학원 : 약학대학 약학과, 2020. 8. 이미옥.TMEM150C, also known as TTN3, is a cation channel which can be stimulated by mechanical stimulation. The inactivation of TTN3 is a slow adaptation (SA) MA type compared to the rapid inactivation mechanics of Piezo1 or Piezo2. It has previously been reported that TTN3 is expressed in muscle spindle afferents and mediates muscle coordination. Since TTN3 is a MA channel, I hypothesized that TTN3 may be involved in detecting blood pressure changes in baroreceptor. Here I show that TTN3 is expressed in the nerve endings of aortic arch and nodose ganglia (NG) neurons. Ttn3 KO promotes peripheral hypertension, tachycardia, large fluctuations in blood pressure, and impaired baroreceptor function. Chemogenetic silencing or stimulation of Ttn3 positive neurons in NG can cause an increase or decrease in blood pressure and heart rate, respectively. More importantly, overexpression of Ttn3 in Ttn3-/- mouse NG rescued cardiovascular changes in Ttn3-/- mice. My conclusion is that TTN3 is a molecular component that contributing to sensing the dynamic changes of blood pressure in baroreceptors. TMEM16, also known as Anoctamin (ANO) gene family consists of ten isoforms. ANO1 and ANO2 are recognized as anion channels activated by Ca2+. ANO6 is a scramblase that destroys polarized phospholipids in the membrane. However, the function of TMEM16H (ANO8) is still unknown. Here I found that ANO8 is a cation channel activated by intracellular cAMP. Inward currents in ANO8 overexpressing HEK cells were observed when intracellular cAMP. The cAMP dependent currents were inhibited by a protein kinase-A inhibitor, which indicates that protein kinase A plays an active role in its activation mechanism. Cholera toxin, an activating agent of adenylate cyclase also activated ANO8. The currents in ANO8 expression cells induced by cAMP were cationic because they did not discriminate among cations. ANO8 is highly expressed in neurons in the brain regions as well as dorsal root ganglion (DRG) neurons. Knock down of Ano8 causes a decrease in cAMP dependent currents in DRG neurons as well as nociceptive behaviors in the formalin pain mice model. These results now suggest that ANO8 is a cation channel activated by the cAMP/pathway and involved in nociception in the pain pathway.TTN3으로도 알려진 TMEM150C는 기계적 자극에 의해 활성화 될 수 있는 양이온 채널이다. TTN3의 비활성화는 Piezo1 또는 Piezo2가 빠르게 비활성화 되는 것과 비교하여 느리게 일어난다. TTN3이 근육방추의 구심성신경에서 발현되고 근육 운동을 조절하는 것으로 이전에 보고 된 바 있다. 그 이후, 본 저자는 TTN3이 Baroreceptor 구심성섬유 (Nodose ganglia, NG)의 신경세포에서 현저하게 발현되는 것을 발견했다. TTN3은 기계적 감각에 의해 반응하는 채널이기 때문에, TTN3이 압력수용기가 혈압 변화를 감지하는 데 관여 할 수 있다는 가설을 세우고 실험하였다. 이 논문에서 TTN3가 대동맥 궁과 구심성섬유 뉴런의 신경 말단에서 발현되는 것을 확인하였다. Ttn3 KO 쥐에서 말초 고혈압, 빈맥, 혈압의 큰 변동 등이 일어나고, 압력 수용기의 기능이 망가진 것을 확인되었다. Chemogenetic을 이용하여 Ttn3가 발현된 NG의 뉴런을 억제 혹은 자극 시킬 때, 혈압 및 심박수가 증가 혹은 감소되는 것을 확인하였다. 또, Ttn3 KO 쥐의 NG에서 Ttn3를 다시 재발현시킨 경우, KO에서 일어났던 심혈관 변화가 회복되었다. 이러한 결과로 미루어볼 때, 본 논문은 TTN3이 압력수용체에서 혈압의 동적 변화를 감지하는 데 중요하게 기여한다고 보고한다. 아녹타민 (ANO) 유전자 패밀리로도 알려진 TMEM16은 10개의 이성체로 구성된다. ANO1 및 ANO2는 Ca2+에 의해 활성화 되는 음이온 채널이다. ANO6는 막에서 분극된 인지질을 파괴하는 지질 파괴 효소로 알려졌다. 그러나 아직까지 TMEM16H (ANO8)의 기능에 대해서는 알려지지 않았다. 이 논문에서 본 저자는 ANO8/TMEM16H가 세포 내 cAMP에 의해 활성화 된 양이온 채널이라는 것을 발견했다. 세포 내 cAMP에 의해 ANO8가 과발현 된 HEK 세포에서 전류가 유발되었다. cAMP-의존적 전류는 Protein kinase A 억제제에 의해 억제되는데, 이는 PKA가 ANO8을 활성화 시킨다는 것을 보여준다. Adenylyl cyclase를 활성화시키는 콜레라 독소도 ANO8을 활성화시켰다. cAMP에 의해 유도 된 ANO8 발현 세포의 전류는 양이온이었고, 이는 선택적으로 특정 양이온을 투과시키지는 않았다. ANO8은 뇌에서의 뉴런 및 배근 신경절 (DRG) 뉴런에서 고도로 발현되어 있다. Ano8의 knockdown을 시킨 경우, 포르말린 통증 마우스 모델에서 통각이 감소되고, DRG 뉴런에서 cAMP- 의존적 전류의 감소도 일어났다. 이러한 결과는 ANO8이 cAMP 경로에 의해 활성화되고 통증 경로에서의 통각에 관여하는 양이온 채널 임을 시사한다.INTRODUCTION 1 1. Ion channels 1 1.1. Overview 1 1.2. Classification of ion channels 4 1.3. TMEM16 / Anoctamin Family 6 1.3.1. Overview 6 1.3.2. Physiology function of Anoctamins 8 1.3.2.1. ANO1 8 1.3.2.2 ANO2 9 1.3.2.3. ANO3 10 1.3.2.4. ANO5 10 1.3.2.5. ANO6 11 1.3.2.6. ANO9 12 1.3.2.7. ANO10 12 2. Baroreceptor reflex 13 2.1. Overview 13 2.2. Baroreflex pathway 17 2.3. Baroreceptors 19 3. Candidates for MA ion channels in Baroreceptor 20 3.1. Enac and Ascic2 20 3.2. TRPC5 23 3.3. TRPV1 24 3.4. Piezos channel 25 3.5. TMEM150c(TTN3) channel 29 4. Anoctamins in nociception 31 5. cAMP-PKA signaling pathway in DRG 32 PURPOSE OF THE STUDY 34 METHODS 35 1. Cell culture and transfection 35 2. Patch clamp 35 3. Mechanical stimulation 36 4. Animals and Ttn3cre mice 37 5. Immunofluorescence 39 6. RT-PCR 40 7. Dil-labeling of aortic BR neurons 41 8. Primary culture of NG or DRG neurons 42 9. Tissue clearing and staining 43 10. Recoding of aortic depressor nerve activity 44 11. 24-hour recoding of blood pressure and heart rate 46 12. Whole-body plethymography test 46 13. Baroreflex response test 47 14. Chemogenetic inhibition or acitvation of TTN3+ neurons 47 15. SiRNA 48 16. AAV infection of nodose ganglion or DRG 49 17. Formalin induced pain beavioral test 50 18. c-Fos immune-postive neurons counting 50 19. Statistical analysis 51 RESULTS 52 1. TTN3 expresses in baroreceptor neurons 52 2. TTN3 is responsible for SA MA currents in baroreceptor 55 3. TTN3 is expressed on AND in the aortic arch 58 4. TTN3 is required for pressure-evoked action potentials 61 5. TTN3-/- mice show hypertension and AP instability 65 6. Ttn3-/- mice shows normal locomotor activity 66 7. Ttn3-/- mice shows normal respiratiory functions 69 8. Ttn3 ablation impairs baroreflex sensitiveity 71 9. Overexpression of TTN3 in NG of Ttn3-/- mice rescues the impaired baroreceptor in Ttn3-/- mice 73 10. Chemogenetic inhibition or stimulation of TTN3+ neurons in NG induces hypertension or hypotension, respectively 76 11. ANO8 was localized in plasma membrane 79 12. ANO8 is activated by intracellular cAMP 81 13. ANO8 is a cation channel and not sensitive to voltage 83 14. Intracellular Calcium enhances CAMP-induced current 85 15. ANO8 is highly expressed in cortex, brainstem, cerebellum spinal cord and dorsal-root ganglia 87 16. ANO8 antibody specific confirm 90 17. ANO8 is highly expressed in nociceptive neurons 92 18. ANO8 confers cAMP-dependent channl current in DRG 94 19. ANO8 mediates pain sensitivity in nociceptive pain model 96 20. ANO8 knock-down reduces acitvities of dorsal horn neurons 99 DISCUSSION 101 1. The role of TMEM150C in baroreceptor function. 101 2. The role of TMEM16H in Nociceptive function 107 REFERENCES 112 국문초록 123Docto