We present a framework for specifying, training, evaluating, and deploying machine learning models. Our focus is on simplifying cutting edge machine learning for practitioners in order to bring such technologies into production. Recognizing the fast evolution of the field of deep learning, we make no attempt to capture the design space of all possible model architectures in a domain- specific language (DSL) or similar configuration language. We allow users to write code to define their models, but provide abstractions that guide develop- ers to write models in ways conducive to productionization. We also provide a unifying Estimator interface, making it possible to write downstream infrastructure (e.g. distributed training, hyperparameter tuning) independent of the model implementation. We balance the competing demands for flexibility and simplicity by offering APIs at different levels of abstraction, making common model architectures available out of the box, while providing a library of utilities designed to speed up experimentation with model architectures. To make out of the box models flexible and usable across a wide range of problems, these canned Estimators are parameterized not only over traditional hyperparameters, but also using feature columns, a declarative specification describing how to interpret input data. We discuss our experience in using this framework in re- search and production environments, and show the impact on code health, maintainability, and development speed. …
In this paper we introduce the class of beta seasonal autoregressive moving average ($beta$SARMA) models for modeling and forecasting time series data that assume values in the standard unit interval. It generalizes the class of beta autoregressive moving average models [Rocha and Cribari-Neto, Test, 2009] by incorporating seasonal dynamics to the model dynamic structure. Besides introducing the new class of models, we develop parameter estimation, hypothesis testing inference, and diagnostic analysis tools. We also discuss out-of-sample forecasting. In particular, we provide closed-form expressions for the conditional score vector and for the conditional Fisher information matrix. We also evaluate the finite sample performances of conditional maximum likelihood estimators and white noise tests using Monte Carlo simulations. An empirical application is presented and discussed. …
A long line of literature has focused on the problem of selecting a team of individuals from a large pool of candidates, such that certain constraints are respected, and a given objective function is maximized. Even though extant research has successfully considered diverse families of objective functions and constraints, one of the most common limitations is the focus on the single-team paradigm. Despite its well-documented applications in multiple domains, this paradigm is not appropriate when the team-builder needs to partition the entire population into multiple teams. Team-partitioning tasks are very common in an educational setting, in which the teacher has to partition the students in her class into teams for collaborative projects. The task also emerges in the context of organizations, when managers need to partition the workforce into teams with specific properties to tackle relevant projects. In this work, we extend the team formation literature by introducing the Guided Team-Partitioning (GTP) problem, which asks for the partitioning of a population into teams such that the centroid of each team is as close as possible to a given target vector. As we describe in detail in our work, this formulation allows the team-builder to control the composition of the produced teams and has natural applications in practical settings. Algorithms for the GTP need to simultaneously consider the composition of multiple non-overlapping teams that compete for the same population of candidates. This makes the problem considerably more challenging than formulations that focus on the optimization of a single team. In fact, we prove that GTP is NP-hard to solve and even to approximate. The complexity of the problem motivates us to consider efficient algorithmic heuristics, which we evaluate via experiments on both real and synthetic datasets. …
We present SplineNets, a practical and novel approach for using conditioning in convolutional neural networks (CNNs). SplineNets are continuous generalizations of neural decision graphs, and they can dramatically reduce runtime complexity and computation costs of CNNs, while maintaining or even increasing accuracy. Functions of SplineNets are both dynamic (i.e., conditioned on the input) and hierarchical (i.e., conditioned on the computational path). SplineNets employ a unified loss function with a desired level of smoothness over both the network and decision parameters, while allowing for sparse activation of a subset of nodes for individual samples. In particular, we embed infinitely many function weights (e.g. filters) on smooth, low dimensional manifolds parameterized by compact B-splines, which are indexed by a position parameter. Instead of sampling from a categorical distribution to pick a branch, samples choose a continuous position to pick a function weight. We further show that by maximizing the mutual information between spline positions and class labels, the network can be optimally utilized and specialized for classification tasks. Experiments show that our approach can significantly increase the accuracy of ResNets with negligible cost in speed, matching the precision of a 110 level ResNet with a 32 level SplineNet. …
Exclusive-Toshiba’s No.2 shareholder calls for immediate resignation of board chair, 3 directors
By Makiko Yamazaki
TOKYO (Reuters) -Toshiba Corp’s second-biggest shareholder on Sunday demanded the board chairman and three other directors immediately resign after an investigation found the company had colluded with the Japanese government to pressure foreign investors.
The letter, seen by Reuters, is from 3D Investment Partners, which owns a 7.2% stake in Toshiba. It was sent to the four on Sunday, according to people with direct knowledge of the process.
It is likely to heighten scrutiny into governance at Toshiba, a renowned industrial conglomerate in crisis sparked by Thursday’s report. The shareholder-commissioned report marked an explosive turn in a long battle between the Japanese company’s management and foreign shareholders.
In addition to 3D, these shareholders include activist investors and Harvard University’s endowment fund.
The revelations in the report “are deeply troubling and represent one of the most prominent and shocking corporate governance failures among large public companies anywhere in the world in the last decade,” the 3D letter says.
The letter, addressed to board chair Osamu Nagayama and three current audit committee members, describes Nagayama as “ultimately responsible for Toshiba’s recent governance failures, including the flawed internal investigation and the board’s determination to oppose an outside, independent investigation.”
“It is also troubling that you have been silent about the investigative report and have failed to accept responsibility for the misconduct that occurred under your oversight as chair of the board,” the letter says.
Toshiba declined to comment on the letter, telling Reuters in a statement it was “carefully reviewing the content of the investigation report and plans to announce its comments towards this investigation result after the review.”
The company was holding an emergency meeting on Sunday to discuss reassigning the candidates for three key board committees ahead of a June 25 shareholder meeting. Major shareholder advisory firms recommended against some of the candidates, including the four addressed in the 3D letter.
Four independent directors, all non-Japanese, have said in a sign of revolt that they were no longer in support of the full slate of director candidates nominated by Toshiba.
(Reporting by Makiko Yamazaki; Editing by William Mallard)
Image Credit: Reuters
The rising importance of Fintech innovation in the new age
The rise of fintech has opened an array of opportunities for smart cities to develop and thrive. Its importance has actually increased in the age of the pandemic that calls for social distancing or contactless transactions.
The leading global payment solutions provider Visa recently indicated the increasing role of digital payments. Thanks to the expanding role of fintech, digital payments are expected to enter different smart city sectors.
Reportedly, fintech application is going to be instrumental in the transportation sector. It will come to people in different forms of contactless payments. It will also ease the process of paying for parking or hiring bikes and scooters.
More than that, whether it’s about loans, money transfer, investment, accounting and bookkeeping, airtime or fundraising. Smart cities and businesses are going to hugely rely on fintech in the coming future.
Going ahead, we are delving into understanding the fintech situation in three smart cities. All three are important fintech hubs that the entire world looks upon.
In the smart city culture, London has the reputation of being the ‘fintech capital’ of the world. The number of fintech giants in the city is valued at more than $1 billion.
However, the pandemic has caused a number of businesses to shut down. At the same time, it has also catalysed the shift to digital and contactless. Businesses are now adopting new ways to support their customers.
Even in this time of crisis, London is at the foremost position of producing the next generation of fintech leaders. This is as per the Ed Lane, VP of Sales for the EMEA region at nCino, a US-based cloud banking provider.
Remote work is becoming a necessity due to COVID-19. Hence, investments in different technologies and solutions in financial organisations and service providers are “more important than ever”. And so Lane claims that this has increased the adoption of cloud-based banking software developed by his firm.
The UK recently introduced the Bounce Back Loan Scheme and the Coronavirus Business Interruption Loan Scheme (CBILS). This is helping Lane’s company nCino and others. They are offering a Bank Operating System to aid SMEs with effective processing of loan applications.
Fintech companies are surviving and tapping into benefits in the COVID-19 age due to their disruptive mindset. The dot.com crash of 2001 and the financial crash of 2008 are drivers that lead them to become proactive.
Innovatively, fintech companies started offering mobile banking, online money management tools and other personalised solutions. Today, the same is enabling them to prevail during this pandemic. Besides all, partnerships have proven to be key strategies in achieving even the impossible, as experts say.
Singapore is showcasing a pioneering move in the fintech industry. Fintech is at the core of Singapore’s vision to become a ‘Smart Nation’ with a “Smart Financial Centre.”
To achieve the dream, the city-state has been showing constant efforts by using innovative technology. With this, it intends to pave the way for new opportunities, enhance efficiency and improve national management of financial risks.
Until 2019, Singapore was already home to over 600 fintech firms. These companies attracted more than half of the total funding for the same year. And amidst the COVID-19 pandemic, the Monetary Authority of Singapore (MAS) introduced two major support packages.
First on April 8, 2020, it announced a S$125 million COVID-19 care package for the financial and fintech sectors. This package aims at aiding the sectors in fighting the challenges from the COVID-19 health crisis. It will help in supporting workers, accelerate digitalisation, and improve operational readiness and resilience.
Second, on May 13, 2020, MAS, the Singapore Fintech Association (SFA) and AMTD Foundation launched the MAS-SFA-AMTD Fintech Solidarity Grant. The S$6 million grant proposes to support Singapore-based fintech firms.
A specific focus is on managing cash flow, producing new sales and seeking growth strategies. At the individual level, many industry participants have launched their own initiatives to support the sector.
HongKong’s fintech startup sector tells us a different story which involves the role of blockchain. Blockchain-based companies are dominating the city’s startup sector.
In 2019, enterprise DLT and crypto-assets exchanges earned rankings as the most popular sectors in Hong Kong’s fintech industry. The report comes from the Financial Services and Treasury Bureau. It confirms that blockchain startups make up 40% of the 57 Fintech firms established in the city in 2019.
As per reports, 45% of new companies are focused on developing applications for large businesses. This is the reason that enterprise blockchain firms were the most popular. Another 27% account for blockchain-related firms in Hong Kong involved in digital currency.
The increase in the number of blockchain-based fintech startups is due to the Special Administrative Region of the People’s Republic of China. The authority introduced new policies towards blockchain tech development – making it a priority.
Blockchain is thriving in Hong Kong due to a number of reasons. The city has laid down clear regulatory guidelines for blockchain-related businesses. Many have leveraged the benefits of the QMAS program. It enables applicants to settle down in the region before having to look for employment. This has immensely encouraged several blockchain specialists to move to Hong Kong.
The city government is also entering partnerships to expand its fintech footprint in the right direction. For example, in November 2019, the government collaborated with Thailand’s officials to explore the development of Central Bank Digital Currencies (CBDCs). Blockchain is a promising technology for the fintech industry. It supports quick, secure and cost-effective transaction-related services.
More importantly, it provides transparency that other traditional technologies were not capable of. Thanks to the use of encrypted distributed ledgers. These enable real-time verification of transactions without the need for mediators such as correspondent banks.
Why Is Fintech Innovation Important For The Development Of Smart Cities?
Advanced cities that are now smart cities have been using fintech for their development. With that, they are also leading the way for others to follow. Many experts confirm that innovation in fintech is a must for any city to become a ‘smart city.’
It enables easy national as well as international business. For the residents, it makes life more convenient by encouraging contactless, economical, sustainable and efficient payment-related operations.
One important aspect that smart city development and fintech innovation has in common is their determination to cut bureaucracy. A city that manages to enable speedy and inexpensive international transfers will also enable its citizens with greater access to the global market. This is as said by Hans W. Winterhoff from KPMG in one of his articles.
Furthermore, fintech innovations of the past have demonstrated their success. Some fintech applications have simplified procedures that became unnecessarily complex over time. Traditional banking services are one of the biggest examples.
The innovative fintech services opened doors for online shopping and easy international money transfers. Fintech is able to provide the same product or service to consumers. But that’s happening in less time, with fewer steps, and at more affordable rates.
Besides, transparency is another important factor that is allowing consumers to have faith in fintech services. With the current potential of fintech, we can now say that it is one of the essential pillars of successful smart city development. The results are already here in the age of this pandemic.
If you did not already know
In the last decade, a variety of topic models have been proposed for text engineering. However, except Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA), most of existing topic models are seldom applied or considered in industrial scenarios. This phenomenon is caused by the fact that there are very few convenient tools to support these topic models so far. Intimidated by the demanding expertise and labor of designing and implementing parameter inference algorithms, software engineers are prone to simply resort to PLSA/LDA, without considering whether it is proper for their problem at hand or not. In this paper, we propose a configurable topic modeling framework named Familia, in order to bridge the huge gap between academic research fruits and current industrial practice. Familia supports an important line of topic models that are widely applicable in text engineering scenarios. In order to relieve burdens of software engineers without knowledge of Bayesian networks, Familia is able to conduct automatic parameter inference for a variety of topic models. Simply through changing the data organization of Familia, software engineers are able to easily explore a broad spectrum of existing topic models or even design their own topic models, and find the one that best suits the problem at hand. With its superior extendability, Familia has a novel sampling mechanism that strikes balance between effectiveness and efficiency of parameter inference. Furthermore, Familia is essentially a big topic modeling framework that supports parallel parameter inference and distributed parameter storage. The utilities and necessity of Familia are demonstrated in real-life industrial applications. Familia would significantly enlarge software engineers’ arsenal of topic models and pave the way for utilizing highly customized topic models in real-life problems. …
In statistics, the median absolute deviation (MAD) is a robust measure of the variability of a univariate sample of quantitative data. It can also refer to the population parameter that is estimated by the MAD calculated from a sample. Consider the data (1, 1, 2, 2, 4, 6, 9). It has a median value of 2. The absolute deviations about 2 are (1, 1, 0, 0, 2, 4, 7) which in turn have a median value of 1 (because the sorted absolute deviations are (0, 0, 1, 1, 2, 4, 7)). So the median absolute deviation for this data is 1. …
Most work on temporal action detection is formulated in an offline manner, in which the start and end times of actions are determined after the entire video is fully observed. However, real-time applications including surveillance and driver assistance systems require identifying actions as soon as each video frame arrives, based only on current and historical observations. In this paper, we propose a novel framework, Temporal Recurrent Networks (TRNs), to model greater temporal context of a video frame by simultaneously performing online action detection and anticipation of the immediate future. At each moment in time, our approach makes use of both accumulated historical evidence and predicted future information to better recognize the action that is currently occurring, and integrates both of these into a unified end-to-end architecture. We evaluate our approach on two popular online action detection datasets, HDD and TVSeries, as well as another widely used dataset, THUMOS’14. The results show that TRN significantly outperforms the state-of-the-art. …
CDF2PDF is a method of PDF estimation by approximating CDF. The original idea of it was previously proposed in  called SIC. However, SIC requires additional hyper-parameter tunning, and no algorithms for computing higher order derivative from a trained NN are provided in . CDF2PDF improves SIC by avoiding the time-consuming hyper-parameter tuning part and enabling higher order derivative computation to be done in polynomial time. Experiments of this method for one-dimensional data shows promising results. …
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