Hard Decision Research Articles

Fairness and Equality During Coronavirus

People are currently living in a world of uncertainty. Within a matter of weeks, the life that everyone knew has been taken from them. People are struggling to retain a semblance of normality within their daily lives as their routines are stripped from them. People can no longer hang out with friends, sit down in a restaurant, go to a park, safely go grocery shopping, and even go to work. This is all due to the Coronavirus, or COVID-19, that is spreading across the world. This global pandemic has halted life for everyone. Although people are mourning the loss of their daily routines, some people are mourning something much greater; the loss of their loved ones. Hundreds of thousands of people are infected with the coronavirus, and tens of thousands have died in the United States alone.1 This surge of COVID cases has left many people sick and in need of care. However, it has also left hospitals overwhelmed and underprepared. Due to the large number of patients in need of ventilators and a low number of ventilators available, many hospitals have been left with an exceptionally hard decision: who gets ventilators and who doesn’t? Essentially, that question boils down to a much more grim one. Who gets to live and who doesn’t?

Impacts of COVID-19 Pandemic on Tourism & Hospitality Industry in Bangladesh

This article tries to find out the impacts of COVID-19 on Tou ism and Hospitality Industry in Bangladesh and presented some recovery plans for minimizing those negative impacts. This study is carried out based on the secondary data sou ces, various literature, newspapers, organizational and government publications, website mat rials and other sources are used to find out and complete the analysis. COVID-19 has cr at d an anxiety around the globe. Bangladesh has already started to face its horrible look. The conomy of Bangladesh is just sunk and like other industries tourism industry is facing most awful situation ever before. Due to lockdown people cannot move from one place to another. As the number of COVID-19 positive cases increasing day by day, government bound to take this hard decision (lockdown). All international flights have been cancelled and s opped for long days; accommodation business (hotels/motels/resort owners) already started to face a countless loss. Small hotels/motels/restaurants/travel agencies already closed their businesses; thousands of people working in tourism industry have started to become jobless. Already government of Bangladesh offered different special packages for recovery and ensuring sustainability of different sector. Tourism industry also needs to offer different stimulus and discount packages for attracting domestic tourist which will help to recover the losses. This study also presents the real scenario of tourism industry during COVID-19 pandemic which will help to know the update knowledge regarding the mpacts of COVID-19 on tourism and hospitality industry and to take proper recovery steps.

US – EU: new priorities in the transatlantic relations

The European Union is an important player in the U.S. policies aimed at maintaining liberal world order. The US-EU interaction has been shaped by a number of key variables, including international environment, specific goals of the U.S. administrations, institutional maturing of the EU, and a complex interplay of American and European diverging and overlapping interests. President D. Trump’s tenure was the most strained period in the US-EU relations, with an erosion of mutual trust and a ghost of trade war looming large. The Biden administration has reversed the U.S. policy towards the European Union. The US-EU summit held last June signified a return of normality in the relations of the transatlantic partners. Washington and Brussels outlined a broad agenda that embraces security, trade and economic issues, coordination in international affairs (concerning, in particular, Russia, China, and Iran), cooperation in decarbonizing of the world economy and promotion of a climate-friendly environment. A broad web of links between the United States and Europe facilitates their joining efforts in addressing global and regional challenges. Although, the US and the EU reached a series of compromises to mitigate their most acrimonious disagreements (Boeing–Airbus subsidies dispute is a vivid example in this regard), there are still unsettled major issues like a comprehensive free trade treaty unsuccessfully negotiated since the Obama presidency. The “Chinese factor” may turn out to be the most divisive one in the transatlantic relations, since the U.S. tough policy towards Beijing makes the EU countries to take hard decisions they prefer to avoid. While it is premature to predict precise implications of the Biden administration’s policy, one can reasonably expect the EU to become a more helpful partner to Washington in diplomatic and economic affairs.

Iterative Pseudo-Soft-Reliability-Based Majority-Logic Decoding for NAND Flash Memory

This paper proposes a decoding algorithm for nonbinary low-density parity-check (NB-LDPC) codes, aiming to improve the error rate performance for NAND flash memory. Several NB-LDPC decoding methods for NAND flash memory have been studied. Some approaches rely on hard decisions, and these are relatively simple but do not have a good error rate performance. Others are based on soft decisions that require multiple reads for each flash memory cell, leading to significant memory throughput degradation. To improve the error rate performance without suffering performance degradation owing to multiple reads, an iterative pseudo-soft-reliability-based decoding algorithm is proposed. Using Galois field addition to calculate the Hamming distance at the initialization, the proposed algorithm not only improves the error rate performance but also reduces the average number of iterations compared with those of conventional hard-decision-based decoding algorithms.

Distributed Decision Fusion with M-ary Signaling and k-bit Transmission on Sensor Observation in Wireless Sensor Networks

Multi-level decisions on sensor detection is able to improve the detection performance on the final decision made at the fusion center (FC) in wireless sensor networks (WSN). In this paper, the performance analysis of an M-ary signaling (MS) scheme using analog transmission and a k-bit transmission (KB) scheme is both examined for distributed binary detection. Under the multi-level decision algorithms, each sensor sends a signal carrying the information of a quantized version of a local decision statistic such as the conditional mean or the log-likelihood ratio. In MS, the output of the quantizer is transmitted directly without digitalizing and coding process, while in KB, each quantization output is coded with k bits and hereby a sensor sends a k-bit hard local decision to the FC. At the FC, the linear combiner detection rule on the transmission schemes is both adopted to make the final decision. The effects of the sensor decision and the transmission errors are incorporated in the analysis of the erroneous performance of the final decision. The goal of the proposed schemes is to minimize the final errors at the FC via optimizing the region allocation on the multi-level decision at the sensor. The numerical results illustrate that the proposed schemes achieve significant improvement in error performance over the conventional schemes under either additive white Gaussian noise (AWGN) channel or Rayleigh faded channel.

A Novel and Efficient Exact Technique for Integrated Staffing, Assignment, Routing, and Scheduling of Home Care Services Under Uncertainty

We model and solve an integrated multi-period staffing, assignment, routing, and scheduling for home care services under uncertainty. The goal is to construct a weekly schedule that adheres to related operational considerations and determines optimal staffing of caregivers by minimizing caregivers’ fixed- and overtime costs. For tractability, we incorporate a priori generated visit patterns—an existing practical approach that deals effectively with hard assignment decisions in. First, we propose a novel mixed-integer program (MIP) for the nominal problem. We then incorporate uncertainty in service and travel times and develop a robust counterpart by hybridizing interval and polyhedral uncertainty sets. Second, we show that there is a special mathematical structure within the model that allows us to develop a novel logic-based Benders branching-decomposition algorithm that systematically delays the resolution of difficult routing/ scheduling problems and efficiently solves both the nominal and robust MIP models. Using a dataset from the literature, we show that CPLEX can solve our nominal and robust models with an average optimality gaps of 44.56% and 45.53%, respectively. Using the same dataset, we demonstrate that our new exact technique can solve our nominal and robust mixed-integer models to an average optimality gap of 2.8% and 4.5%, respectively. Third, we provide practical insights into (i) the price of robustness and (ii) the impacts of nurse flexibility and overtime. The average total cost does not increase beyond 12.7% than the nominal solution and the cost-savings of nurse flexibility is approximately five times higher than that of overtime.

Predictive Syndrome Based Low Complexity Joint Iterative Detection-Decoding Algorithm for Non-Binary LDPC Codes

This paper addresses the problem of decoding non-binary low density parity check codes(LDPC) over finite field GF(q) using symbol flipping approach. To achieve low complexity reliable communication, three new algorithms for improving the bit error rate performance of the non-binary LDPC decoder are presented. The first type is the symbol flipping decoding algorithm using a flipping function based on the channel reliability to identify the least reliable symbol position. In this algorithm, if the predicted symbol value satisfies the check sum, then the value is declared as correct otherwise the value is adjusted and sent back to the QAM detector. Algorithms 2 in this paper is an improvement to iterative joint detection-decoding algorithm by using the method of iterative hard decision based majority logic to select the new candidate symbol value. The feedback value to the QAM detector is adjusted by using Euclidean distance between the current symbol and the newly selected symbol value. Algorithm 3 is a low complexity version of Algorithm 2 which is derived by applying a majority voting scheme. In the majority voting scheme, symbols are short listed first by voting and all the computation are carried out only for the short listed least reliable symbols which significantly lowers the processing complexity. Numerical results and complexity analysis show that the proposed methods have good bit error rate versus complexity trade-off for various applications when compared with some existing algorithms.

Matching Pursuit Algorithm for Decoding of Binary LDPC Codes

This paper presents a novel hard decision decoding algorithm for low‐density parity‐check (LDPC) codes, in which the stand matching pursuit (MP) is adapted for error pattern recovery from syndrome over GF(2). In this algorithm, the operation of inner product can be converted into XOR and accumulation, which makes the matching pursuit work with a high efficiency. In addition, the maximum iteration is theoretically explored in relation to sparsity and error probability according to the sparse theory. To evaluate the proposed algorithm, two MP‐based decoding algorithms are simulated and compared over an AWGN channel, i.e., generic MP (GMP) and syndrome MP (SMP). Simulation results show that the GMP algorithm outperforms the SMP by 0.8 dB at BER = 10−5.

The impact of the Covid-19 pandemic on the performance of street level bureaucrats in Brazil

Abstract: Dealing with the Covid-19 pandemic requires that the State make hard decisions that involve the action of bureaucrats who interact with the population through the implementation of public policy, the street-level bureaucracy (SLB). In this paper, based on a mixed- method exploratory study, we analyze how the daily performance of street-level bureaucrats in different policy areas- health and social care, access to the justice system, public security and education - has changed during the pandemic. We also explore the repercussions of those changes. Based on the analysis of the perceptions of bureaucrats, changes in their work and in their relationship with the public, we identify three categories that illustrate the dynamics of SLB work during the pandemic: the SLB who faces the crisis on the front lines; the SLB who suffers the effects of the pandemic, but whose work does not require her to face it directly; and the SLB who began to work remotely. We conclude that, during the pandemic, SLB suffered in varying degrees an aggravation of structural problems, such as their removal from decision-making processes - now restricted to the highest government level - and the exacerbation of already existing conflicts and ambiguities.

A deep metric learning approach for weakly supervised loan default prediction1

To deal with the systematic risk of financial institutions and the rapid increasing of loan applications, it is becoming extremely important to automatically predict the default probability of a loan. However, this task is non-trivial due to the insufficient default samples, hard decision boundaries and numerous heterogeneous features. To the best of our knowledge, existing related researches fail in handling these three difficulties simultaneously. In this paper, we propose a weakly supervised loan default prediction model WEAKLOAN that systematically solves all these challenges based on deep metric learning. WEAKLOAN is composed of three key modules which are used for encoding loan features, learning evaluation metrics and calculating default risk scores. By doing so, WEAKLOAN can not only extract the features of a loan itself, but also model the hidden relationships in loan pairs. Extensive experiments on real-life datasets show that WEAKLOAN significantly outperforms all compared baselines even though the default loans for training are limited.

Multi-distributed probabilistically shaped PAM-4 system for intra-data-center networks

Probabilistically shaped (PS) pulse amplitude modulation (PAM) is a promising technique for intra-data-center networks due to its superior performance, for which a low-complexity and cost-effective distributed matching method is critical. In this work, we propose an energy-level-assigned method to yield PS-PAM-4 signals with various bit rates based on variable probabilistic distributions. We experimentally demonstrate the proposed method in a 25 Gbaud PS-PAM-4 transmission over a bandwidth of approximately 10 GHz. Compared to a uniform PAM-4 system, the proposed multi-distributed PS-PAM-4 system approaches the hard decision threshold at a wide range of received optical power for different applications.

An Improved Algorithm for the Detection of Fastening Targets Based on Machine Vision

Object detection plays an important role in the sorting process of mechanical fasteners. Although object detection has been studied for many years, it has always been an industrial problem. Edge-based model matching is only suitable for a small range of illumination changes, and the matching accuracy is low. The optical flow method and the difference method are sensitive to noise and light, and camshift tracking is less effective in complex backgrounds. In this paper, an improved target detection method based on YOLOv3-tiny is proposed. The redundant regression box generated by the prediction network is filtered by soft nonmaximum suppression (NMS) instead of the hard decision NMS algorithm. This not only increases the size of the network structure by 52 × 52 and improves the detection accuracy of small targets but also uses the basic structure block MobileNetv2 in the feature extraction network, which enhances the feature extraction ability with the increased network layer and improves network performance. The experimental results show that the improved YOLOv3-tiny target detection algorithm improves the detection ability of bolts, nuts, screws and gaskets. The accuracy of a single type has been improved, which shows that the network greatly enhances the ability to learn objects with slightly complex features. The detection result of single shape features is slightly improved, which is higher than the recognition accuracy of other types. The average accuracy is increased from 0.813 to 0.839, an increase of two percentage points. The recall rate is increased from 0.804 to 0.821.

Medical School, Cardiovascular Surgery, and Education: how we do it in Brazil's scenario?

Choosing a surgical specialty can be a hard decision for a medical student. Several studies present data showing that most medical students fear the surgical field and end up switching to another specialty. For cardiovascular surgery, the scenario is very similar. In the last decades, the interest in cardiovascular surgery has been decreasing worldwide and the cardiothoracic surgical societies across the globe have been trying to understand the factors that push away medical students and general surgical residents from the specialty. In this regard, our work aims to focus on describe the access of students to cardiovascular surgery, especially during medical school, as well as to provide a brief report of our current data regarding the specialty.

THE ROLE OF TOURISM AND HOSPITALITY OF COVID – 19 AND WORLD ECONOMY STRATEGIES FOR ECONOMIC RECOVERY AND SUSTAINABILITY

In the Global economy, tourism is one of the most noticeable and growing sectors. This sector plays an important role in boosting a nation’s economy. An increase in tourism flow can bring positive economic outcomes to the nations, especially in gross domestic product GDP. The tourism industry is an engine of economic development and GDP growth. As the number of COVID-19 positive cases increasing day by day, government bound to take this hard decision (lockdown). All international flight have been cancelled and stopped for long days accommodation business, hotels, motels/resort owners, already started to face a countless loss, hotels/motels, restaurant, travel agencies already closed their businesses, thousands of people working in tourism industry have started to become jobless. Already government of Nigeria offered different special packages for recovery and ensuring sustainability of different sector. This study also presents the real scenario of tourism industry during COVID-19 pandemic which will help to know the update knowledge regarding the impacts of COVID-19 in tourism and hospitality industry and to take proper recovery steps.

A High-Performance Stochastic LDPC Decoder Architecture Designed via Correlation Analysis

This paper presents an area-efficient architecture for stochastic low-density parity-check (LDPC) decoder with high throughput and excellent bit-error-rate (BER) performance. The correlation effects of a stochastic Sum-Product Algorithm (SPA) are analyzed. Based on this analysis, a variable node (VN) structure is proposed and its similarity with a correlation divider (CORDIV) is pointed out. Based on the properties of CORDIV, the area of probability tracer in the VN is reduced significantly. In order to achieve more accurate results when the check-to-variable (C2V) messages are not strong enough, the 3-3 input grouping sub-node is replaced by an adder-based 5-1 input grouping sub-node of the degree-6 VN for (2048,1723) code. An unbiased stochastic sequence generator is adopted to get more accurate results from the smaller probability tracer. Furthermore, the soft bit-flipping prior-processing and the C2V-based hard decision updating method are combined in VN to reduce the decoding latency. A (2048,1723) stochastic LDPC decoder is designed in the TSMC 65 nm process to demonstrate the proposed decoder architecture. With the aid of early termination, the decoder occupies 2.34 mm2 core area and can achieve 116.17 Gb/s at 4.4 dB and 461.99 Gb/s at 5.5 dB under 970 MHz with better decoding performance. Compared with the state-of-the-art stochastic IEEE 802.3an LDPC decoders, the proposed architecture can achieve the best throughput, throughput-to-area ratio, and BER performance.

Low-complexity equalizer with a hybrid decision scheme for 50 Gb/s/λ PAM4-PON using a low-cost 10 G receiver.

In this Letter, we experimentally demonstrate a 50Gb/s/λ four-level pulse amplitude modulation-based passive optical network system with a 10 G class receiver. A memory polynomial equalizer (MPE) combined with a decision feedback equalizer (DFE) is applied to eliminate channel distortions in the system. To further improve the performance of the MPE-DFE, for the first time, to the best of our knowledge, a low-complexity hybrid decision scheme (HDS) is proposed, which consists of single-symbol decision (SSD) and multi-symbol decision (MSD). The SSD is exactly the conventional hard decision based on minimum Euclidean distance, whereas MSD is based on a simplified maximum likelihood detection principle with M-algorithm. In terms of complexity, MSD requires 19.1% more multiplications than SSD, but the symbol number of MSD only accounts for less than 20% of the total signal frame when the received optical power is greater than -27dBm. Experimental results show that the proposed MPE-DFE with HDS achieves a 0.7 dB and 1.3 dB sensitivity gain compared with conventional SSD, and up to 35.4 dB and 31.4 dB link power budget, regarding the forward error correction threshold of 10-2 and 10-3, respectively.

Design of Improved BP Decoders and Corresponding LT Code Degree Distribution for AWGN Channels

This paper presents the performance of a hard decision belief propagation (HDBP) decoder used for Luby transform (LT) codes over additive white Gaussian noise channels; subsequently, three improved HDBP decoders are proposed. We first analyze the performance improvement of the sorted ripple and delayed decoding process in a HDBP decoder; subsequently, we propose ripple-sorted belief propagation (RSBP) as well as ripple-sorted and delayed belief propagation (RSDBP) decoders to improve the bit error rate (BER). Based on the analysis of the distribution of error encoded symbols, we propose a ripple-sorted and threshold-based belief propagation (RSTBP) decoder, which deletes low-reliability encoded symbols, to further improve the BER. Degree distribution significantly affects the performance of LT codes. Therefore, we propose a method for designing optimal degree distributions for the proposed decoders. Through simulation results, we demonstrate that the proposed RSBP and RSDBP decoders provide significantly better BER performances than the HDBP decoder. RSDBP and RSTDP combined with the proposed degree distributions outperformed state-of-the-art degree distributions in terms of the number of encoded symbols required to recover an input symbol correctly (NERRIC) and the frame error rate (FER). For a hybrid decoder formulated by combining RSDBP with a soft decision belief propagation decoder, the proposed degree distribution outperforms the other degree distributions in terms of decoding complexity.

Physical Layer Encryption in DMT Based on Digital Multi-Scroll Chaotic System

Physical layer encryption based on digital chaos emerged as a promising methodology for securing data transmission. Discrete multi-tone (DMT) is a widely investigated advanced modulation format in intensity modulation and direct detection (IM/DD) systems, because of its high spectral efficiency, low cost and high dispersion tolerance. In this letter, we successfully performed a physical layer chaotic encryption method on an IM/DD system and experimentally demonstrated the transmission of 100 Gb/s encrypted 32QAM-DMT and 125 Gb/s pre-equalized encrypted 32QAM-DMT signals over 2-km standard single mode fiber (SSMF) with bit error rate (BER) under the hard decision forward error correction (HD-FEC) threshold of 3.8×10 -3 . The proposed encryption algorithm utilizes three independent pseudo-random sequences generated by the multi-scroll chaotic system for secure data transmission, and has no substantial impact on the transmission performance.

The application of analytic hierarchy process to implement collaborative governance process: The allocation of the urban structural funds in the city of Madrid

AbstractLocal governments dispose of powerful tools like development funds that can be distributed among its different constituents to compensate economic disequilibrium originated by a complex array of factors. But it is always one of the hardest decisions to assign handouts to people who could think its own expectations are not being taken properly into account. Each stakeholder has different criteria to evaluate his needs, and to demand a particular investment effort. Since resources are very limited in local levels, city hall governments are pulled apart by those conflicting demands. The challenge of addressing them at the same time can be modelled as a multi‐criteria decision‐making. The aim of this paper is to show that the analytic hierarchy process (AHP) could work out the most acceptable distribution of the funds among the different city districts and neighbourhoods. The application of the model in Madrid shows that AHP: (a) allows to incorporate opposing demands; (b) increases the transparency of the decision‐making process; (c) eases the decentralization of governmental decisions; (d) enhances the legitimation of the final outcome; and (e) it is a scientific approach with great potential to be applied in similar domains where hard political decisions are needed.

Beyond 1.6 Tb/s Net Rate PAM Signal Transmission for Rack-Rack Optical Interconnects With Mode and Wavelength Division Multiplexing

In this article, a beyond 1.6 Tb/s net rate pulse amplitude modulation (PAM) communication system is experimentally demonstrated for 20 m rack-rack optical interconnects with low cost intensity modulation direct detection (IM-DD) architecture, which is enabled by mode division multiplexing (MDM) and wavelength division multiplexing (WDM). The MDM with three linearly polarized modes is realized by a multiplane light conversion (MPLC) mode multiplexer with the maximum modal crosstalk less than - 20 dB. The experimental results show that 2.01 Tb/s (1.84 Tb/s net rate) PAM-6 signal carried on three modes and four wavelengths is successfully transmitted over 20 m standard OM2 multimode fiber (MMF) with the BERs of received signals from all channels below hard decision forward error correction (HD-FEC) threshold of 3.8 × 10-3. Look-up table (LUT) pre-distortion and Volterra nonlinear equalizer (VNLE) are employed for nonlinear impairment mitigation to improve the system performance. Compared to linear feedforward equalizer (FFE), 0.3 dB and 1 dB receiver sensitivity improvements are obtained by LUT and VNLE, respectively. To the best of our knowledge, our proposed scheme achieves the highest bit rate of 167.5 Gb/s per wavelength in MDM system with IM-DD structure. The experimental results show that our proposed PAM MDM communication scheme is a promising candidate for future 1.6 Tb/s short reach rack-rack optical interconnects.