Is devaluation contractionary? empirical evidence for Pakistan

The paper investigates the effect of real devaluation on economic growth. In the empirical model we also include other theoretically justified variables in the case of Pakistan, such as foreign remittances, money supply, and government spending. The paper implements the ADF method to test check the stationarity of the series; and the ARDL bounds testing approach to cointegration to establish a long run relationship. The findings affirm cointegration among the series. Real devaluation exerts contractionary effect on economic growth. The results from variance decomposition and impulse response-function show unidirectional causality from foreign remittances to economic growth; and bidirectional causality between money supply and foreign remittances. Furthermore, money supply Granger causes government spending; while devaluation Granger causes economic growth, albeit, weakly. The results should help in formulating a comprehensive trade policy including the use of competitive devaluation as a tool to correct balance of payments problems.Devaluation, Contractionary, Cointegration

Pre-text Representation Transfer for Deep Learning with Limited Imbalanced Data : Application to CT-based COVID-19 Detection

Annotating medical images for disease detection is often tedious and expensive. Moreover, the available training samples for a given task are generally scarce and imbalanced. These conditions are not conducive for learning effective deep neural models. Hence, it is common to 'transfer' neural networks trained on natural images to the medical image domain. However, this paradigm lacks in performance due to the large domain gap between the natural and medical image data. To address that, we propose a novel concept of Pre-text Representation Transfer (PRT). In contrast to the conventional transfer learning, which fine-tunes a source model after replacing its classification layers, PRT retains the original classification layers and updates the representation layers through an unsupervised pre-text task. The task is performed with (original, not synthetic) medical images, without utilizing any annotations. This enables representation transfer with a large amount of training data. This high-fidelity representation transfer allows us to use the resulting model as a more effective feature extractor. Moreover, we can also subsequently perform the traditional transfer learning with this model. We devise a collaborative representation based classification layer for the case when we leverage the model as a feature extractor. We fuse the output of this layer with the predictions of a model induced with the traditional transfer learning performed over our pre-text transferred model. The utility of our technique for limited and imbalanced data classification problem is demonstrated with an extensive five-fold evaluation for three large-scale models, tested for five different class-imbalance ratios for CT based COVID-19 detection. Our results show a consistent gain over the conventional transfer learning with the proposed method.Comment: Best paper at IVCN

Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions

Medical Image Analysis is currently experiencing a paradigm shift due to Deep Learning. This technology has recently attracted so much interest of the Medical Imaging community that it led to a specialized conference in `Medical Imaging with Deep Learning' in the year 2018. This article surveys the recent developments in this direction, and provides a critical review of the related major aspects. We organize the reviewed literature according to the underlying Pattern Recognition tasks, and further sub-categorize it following a taxonomy based on human anatomy. This article does not assume prior knowledge of Deep Learning and makes a significant contribution in explaining the core Deep Learning concepts to the non-experts in the Medical community. Unique to this study is the Computer Vision/Machine Learning perspective taken on the advances of Deep Learning in Medical Imaging. This enables us to single out `lack of appropriately annotated large-scale datasets' as the core challenge (among other challenges) in this research direction. We draw on the insights from the sister research fields of Computer Vision, Pattern Recognition and Machine Learning etc.; where the techniques of dealing with such challenges have already matured, to provide promising directions for the Medical Imaging community to fully harness Deep Learning in the future

PyMAiVAR: An open-source Python suit for audio-image representation in human action recognition

We present PyMAiVAR, a versatile toolbox that encompasses the generation of image representations for audio data including Wave plots, Spectral Centroids, Spectral Roll Offs, Mel Frequency Cepstral Coefficients (MFCC), MFCC Feature Scaling, and Chromagrams. This wide-ranging toolkit generates rich audio-image representations, playing a pivotal role in reshaping human action recognition. By fully exploiting audio data\u27s latent potential, PyMAiVAR stands as a significant advancement in the field. The package is implemented in Python and can be used across different operating systems

Is devaluation contractionary? empirical evidence for Pakistan

The paper investigates the effect of real devaluation on economic growth. In the empirical model we also include other theoretically justified variables in the case of Pakistan, such as foreign remittances, money supply, and government spending. The paper implements the ADF method to test check the stationarity of the series; and the ARDL bounds testing approach to cointegration to establish a long run relationship. The findings affirm cointegration among the series. Real devaluation exerts contractionary effect on economic growth. The results from variance decomposition and impulse response-function show unidirectional causality from foreign remittances to economic growth; and bidirectional causality between money supply and foreign remittances. Furthermore, money supply Granger causes government spending; while devaluation Granger causes economic growth, albeit, weakly. The results should help in formulating a comprehensive trade policy including the use of competitive devaluation as a tool to correct balance of payments problems

Piperidinium 4-hydr­oxy-3-methoxy­carbon­yl-1,2-benzothia­zin-2-ide 1,1-dioxide

In the anion of the title compound, C5H12N+·C10H8NO5S−, the thia­zine ring adopts a distorted half-chair conformation and the enolic H atom is involved in an intra­molecular O—H⋯O hydrogen bond, forming a six-membered ring. The anions and cations are connected via N—H⋯N and N—H⋯O inter­actions

Hepatitis C cross-genotype immunity and implications for vaccine development.

While about a quarter of individuals clear their primary hepatitis C (HCV) infections spontaneously, clearance (spontaneous or treatment-induced) does not confer sterilizing immunity against a future infection. Since successful treatment does not prevent future infections either, an effective vaccine is highly desirable in preventing HCV (re)infection. However, development of an effective vaccine has been complicated by the diversity of HCV genotypes, and complexities in HCV immunological responses. Smaller studies on humans and chimpanzees reported seemingly opposing results regarding cross-neutralizing antibodies. We report a lack of cross-genotype immunity in the largest cohort of people to date. In the adjusted Cox proportional hazards model, reinfection with a heterologous HCV genotype (adjusted Hazard Ratio [aHR]: 0.45, 95% CI: 0.25-0.84) was associated with a 55% lower likelihood of re-clearance. Among those who cleared their first infection spontaneously, the likelihood of re-clearance was 49% lower (aHR: 0.51, 95% CI: 0.27-0.94) when reinfected with a heterologous HCV genotype. These findings indicate that immunity against a particular HCV genotype does not offer expanded immunity to protect against subsequent infections with a different HCV genotype. A prophylactic HCV vaccine boosted with multiple HCV genotype may offer a broader and more effective protection

Movie Tags Prediction and Segmentation Using Deep Learning

The sheer volume of movies generated these days requires an automated analytics for ef cient classi cation, query-based search, and extraction of desired information. These tasks can only be ef ciently performed by a machine learning based algorithm. We address the same issue in this paper by proposing a deep learning based technique for predicting the relevant tags for a movie and segmenting the movie with respect to the predicted tags. We construct a tag vocabulary and create the corresponding dataset in order to train a deep learning model. Subsequently, we propose an ef cient shot detection algorithm to nd the key frames in the movie. The extracted key frames are analyzed by the deep learning model to predict the top three tags for each frame. The tags are then assigned weighted scores and are ltered to generate a compact set of most relevant tags. This process also generates a corpus which is further used to segment a movie based on a selected tag. We present a rigorous analysis of the segmentation quality with respect to the number of tags selected for the segmentation. Our detailed experiments demonstrate that the proposed technique is not only ef cacious in predicting the most relevant tags for a movie, but also in segmenting the movie with respect to the selected tags with a high accuracy