High-resolution distributed sampling of bandlimited fields with low-precision sensors

The problem of sampling a discrete-time sequence of spatially bandlimited fields with a bounded dynamic range, in a distributed, communication-constrained, processing environment is addressed. A central unit, having access to the data gathered by a dense network of fixed-precision sensors, operating under stringent inter-node communication constraints, is required to reconstruct the field snapshots to maximum accuracy. Both deterministic and stochastic field models are considered. For stochastic fields, results are established in the almost-sure sense. The feasibility of having a flexible tradeoff between the oversampling rate (sensor density) and the analog-to-digital converter (ADC) precision, while achieving an exponential accuracy in the number of bits per Nyquist-interval per snapshot is demonstrated. This exposes an underlying ``conservation of bits'' principle: the bit-budget per Nyquist-interval per snapshot (the rate) can be distributed along the amplitude axis (sensor-precision) and space (sensor density) in an almost arbitrary discrete-valued manner, while retaining the same (exponential) distortion-rate characteristics. Achievable information scaling laws for field reconstruction over a bounded region are also derived: With N one-bit sensors per Nyquist-interval, $\Theta(\log N)$ Nyquist-intervals, and total network bitrate $R_{net} = \Theta((\log N)^2)$ (per-sensor bitrate $\Theta((\log N)/N)$), the maximum pointwise distortion goes to zero as $D = O((\log N)^2/N)$ or $D = O(R_{net} 2^{-\beta \sqrt{R_{net}}})$. This is shown to be possible with only nearest-neighbor communication, distributed coding, and appropriate interpolation algorithms. For a fixed, nonzero target distortion, the number of fixed-precision sensors and the network rate needed is always finite.Comment: 17 pages, 6 figures; paper withdrawn from IEEE Transactions on Signal Processing and re-submitted to the IEEE Transactions on Information Theor

The uncertainty channel of contagion

The 2007 subprime crisis in the United States has triggered a succession of financial crises around the globe, reigniting interest in the contagion phenomenon. Not all crises, however, are contagious. This paper models a new channel of contagion where the degree of anticipation of crises, through its impact on investor uncertainty, determines the occurrence of contagion. Incidences of surprise crises lead investors to doubt the accuracy of their information-gathering technology, which endogenously increases the probability of crises elsewhere. Anticipated crises, instead, have the opposite effect. Importantly, this channel is empirically shown to have an independent effect beyond other contagion channels.Debt Markets,,Emerging Markets,Labor Policies,Currencies and Exchange Rates

Regional reserve pooling arrangements

Recently, several emerging market countries in East Asia and Latin America have initiated intra-regional reserve pooling mechanisms. This is puzzling from a traditional risk-diversification perspective, because country-level shocks are more correlated within rather than across regions. This paper provides a novel rationale for intra-regional pooling: if non-contingent reserve assets can be used to support production during a crisis, then a country's reserve accumulation decision affects not only its own production and consumption, but also its trading partners. If consumption through terms of trade effects. These terms of trade adjustments can be fully internalized only by a reserve pool among trading partners. If trade linkages are stronger within rather than across regions, then intra-regional reserve pooling may dominate inter-regional pooling, even if shocks are more correlated within regions.

New evidence on cyclical and structural sources of unemployment

We provide cross-country evidence on the relative importance of cyclical and structural factors in explaining unemployment, including the sharp rise in U.S. long-term unemployment during the Great Recession of 2007-09. About 75% of the forecast error variance of unemployment is accounted for by cyclical factors—real GDP changes (“Okun’s Law”), monetary and fiscal policies, and the uncertainty effects emphasized by Bloom (2009). Structural factors, which we measure using the dispersion of industry-level stock returns, account for the remaining 25 percent. For U.S. long-term unemployment the split between cyclical and structural factors is closer to 60-40, including during the Great Recession.Unemployment

Does strengths of a positive direct antiglobulin test predicts the need for phototherapy and duration of phototherapy? – a single center, retrospective study

BackgroundUse of Direct Antiglobulin test (DAT) in management of neonatal hyperbilirubinemia is conflicting.Objectivewhether strength of positive DAT predicts the need for phototherapy, duration of phototherapy and need for major interventions.MethodsWe retrospectively collected data on all DAT positive neonates with birth gestational age ≥32 weeks over six years (2014–2019). Data regarding blood group, DAT and clinical details were obtained from a hospital database. We also collected data on serial hemoglobin and other relevant laboratory parameters. We also collected data on infants receiving major interventions such as exchange transfusion, in-utero transfusion, immunoglobulins, and postnatal transfusion for the duration of the study period. All of these infants were electronically followed up for a period of 6 weeks. This study was approved by institutional audit authority. All the statistics were performed using SPSS software.ResultsOut of 1285 DAT tests performed, only 91 infants were positive (7%), and 78 DAT positive infants were available for analysis. There were 54 infants with DAT (1+), 15 infants with DAT (2+), 7 infants with DAT (3+) and 2 infants with DAT (4+). There was no significant statistical difference in terms of need for phototherapy, duration of phototherapy, need for major interventions and hemoglobin levels at different time points between the groups (DAT 1+ Vs DAT ≥2+; DAT ≤2+ Vs DAT >2). A Total of 10 infants received major intervention, with one infant receiving all three interventions (DAT 3+ with significant maternal antibodies), 2 additional infants (both DAT1+) received exchange transfusion, 6 additional infants received immunoglobulin (2 infants: DAT 2+; 4 infants: DAT 1+) and one additional infant (DAT 1+) with significant maternal antibodies received a postnatal transfusion.ConclusionStrength of a DAT did not predict the need for phototherapy, duration of phototherapy, and the need for major hemolysis related intervention in the first 6 weeks of life

Chaining text-to-image and large language model: A novel approach for generating personalized e-commerce banners

Text-to-image models such as stable diffusion have opened a plethora of opportunities for generating art. Recent literature has surveyed the use of text-to-image models for enhancing the work of many creative artists. Many e-commerce platforms employ a manual process to generate the banners, which is time-consuming and has limitations of scalability. In this work, we demonstrate the use of text-to-image models for generating personalized web banners with dynamic content for online shoppers based on their interactions. The novelty in this approach lies in converting users' interaction data to meaningful prompts without human intervention. To this end, we utilize a large language model (LLM) to systematically extract a tuple of attributes from item meta-information. The attributes are then passed to a text-to-image model via prompt engineering to generate images for the banner. Our results show that the proposed approach can create high-quality personalized banners for users.Comment: 10 page

CRISPR Cas/Exosome Based Diagnostics: Future of Early Cancer Detection

Trending and Thriving, CRISPR/Cas has expanded its wings towards diagnostics in recent years. The potential of evading off targeting has not only made CRISPR/Cas an effective therapeutic aid but also an impressive diagnostic tool for various pathological conditions. Exosomes, 30 - 150nm sized extracellular vesicle present and secreted by almost all type of cells in body per se used as an effective diagnostic tool in early cancer detection. Cancer being the leading cause of global morbidity and mortality can be effectively targeted if detected in the early stage, but most of the currently used diagnostic tool fails to do so as they can only detect the cancer in the later stage. This can be overcome by the use of combo of the two fore mentioned diagnostic aids, CRISPR/Cas alongside exosomes, which can bridge the gap compensating the cons. This chapter focus on two plausible use of CRISPR/Cas, one being the combinatorial aid of CRISPR/Cas and Exosome, the two substantial diagnostic tools for successfully combating cancer and other, the use of CRISPR in detecting and targeting cancer exosomes, since they are released in a significant quantity in early stage by the cancer cells

Development of Deep Learning based Intelligent Approach for Credit Card Fraud Detection

Credit card fraud (CCF) has long been a major concern of institutions of financial groups and business partners, and it is also a global interest to researchers due to its growing popularity. In order to predict and detect the CCF, machine learning (ML) has proven to be one of the most promising techniques. But, class inequality is one of the main and recurring challenges when dealing with CCF tasks that hinder model performance. To overcome this challenges, a Deep Learning (DL) techniques are used by the researchers. In this research work, an efficient CCF detection (CCFD) system is developed by proposing a hybrid model called Convolutional Neural Network with Recurrent Neural Network (CNN-RNN). In this model, CNN acts as feature extraction for extracting the valuable information of CCF data and long-term dependency features are studied by RNN model. An imbalance problem is solved by Synthetic Minority Over Sampling Technique (SMOTE) technique. An experiment is conducted on European Dataset to validate the performance of CNN-RNN model with existing CNN and RNN model in terms of major parameters. The results proved that CNN-RNN model achieved 95.83% of precision, where CNN achieved 93.63% of precision and RNN achieved 88.50% of precision