by jsendak | Aug 21, 2025 | Science
It’s common among Austen fans (or Janites, as we like to be called) to find ourselves in long and spirited debates about which adaptations of her novels are the best. 1995 was an enormous year for Jane Austen on screen. Most enduringly beloved, perhaps, is the six part BBC adaptation of Pride and Prejudice, starring Jennifer Ehle and Colin Firth. Lesser known—but much revered—was the subtle and tender film of Austen’s most mature novel, Persuasion, starring Ciaran Hinds as Captain Wentworth. Then we come to my favourite, Clueless—a modernisation of 1815’s Emma that skilfully translates the social politics of the English countryside to the colourful high school world of Beverly Hills.
The Austen film that swept Best Adapted Screenplay categories across award ceremonies that season, however, was none other than Emma Thompson’s Sense and Sensibility (dir. Ang Lee). Praise for the film continues to this day, with fans returning again and again for Thompson’s deft script, Ang Lee’s careful eye on the beautiful Devon landscapes and the much beloved performances by Thompson (Elinor) and then 19-year-old Kate Winslet (Marianne). On the 31 August 2025, Sense and Sensibility will be screened at Pictureville to celebrate its 30th anniversary.
The 30th anniversaries of these films are not the only ones to celebrate this year, however. 2025 also marks the 250th anniversary of Jane herself, who was born in 1775. Celebratory exhibitions, events and even regency balls have been happening up and down the country, including locally at Harewood House. There, two costumes made by Cosprop and worn by Thompson and Winslet in Sense and Sensibility are on display as part of their Austen and Turner exhibition, among many other exciting artefacts. Was there ever a better time to revisit this iconic adaptation?
Sense and Sensibility is Austen’s most high-stakes story. Our heroines are living with a genuine threat of poverty and are entirely reliant on the kindness and charity of others (namely, men)—a circumstance Jane Austen and her sister were all too familiar with. The film understands the economics and society of the era perfectly, which is vital for a story so wrapped up in these stakes, but it never forgets to find time for comedy in among the drama.
Sisterhood is at the heart of Austen’s stories, and as her many surviving letters tell us, her own life too. Cassandra Austen was undoubtedly the most important person in Jane’s life, with the two sisters having lived together until Jane’s early death in 1817. Their close bond is reflected in Sense and Sensibility’s Dashwood sisters, and the real strength of Thompson’s adaptation is the way in which it centres this relationship above all others.
Many moments between the Dashwood sisters (little Margaret included) will ring true to women today. They squabble, and tease one another about boys, and when they find themselves in trouble, blame the other. Meaningful conversations happen in a glance. When Willoughby carries Marianne into Barton cottage, Elinor looks at her pointedly, and the understanding between them, portrayed so expertly in this glance, is one that transcends eras. A simple and momentary but wholly recognisable moment of sorority. As Willoughby leaves, they hiss “His name. His name!” to their mother, in much the same tone a woman in 1995 may have hissed “His digits. Get his digits!”
The marriage mart is, of course, an economic issue for the Dashwoods, but thanks to Thompson’s commitment to injecting Austen’s trademark humour, it’s also rather fun. The script also takes ample opportunities for gossip, which Austen herself famously adored (so much so that many of the letters between Jane and Cassandra were posthumously censored by relatives, in fear of causing offence to those discussed).
In the film, classic Austen dialogue is infused with naturalistic action and asides. Bonnet ribbons too tightly knotted break up plot-driven conversation, and a fallen shawl helps to display the beginnings of tenderness and romantic feeling. Throughout, Thompson inserts extra little moments of humanity, like Elinor wrapping herself in blankets on her first cold night at Barton cottage, and complaining that Marianne’s feet are cold. Or the sweet and amusing sequence of Elinor and Marianne washing their little sister’s unruly hair in a bowl.
Lee’s direction gives the film beauty in its quietest moments. At Norland Park, sounds of the country filter through even in internal scenes: light wind and songbirds, accompanied beautifully by Marianne’s diegetic piano music. It takes us right into the vast rooms of the country house, with their open windows and endless surrounding greenery. Once we move to Barton Cottage, that greenery takes centre stage. With his wide lens, Lee presents Austen’s beloved English countryside as gorgeous and expansive (and even, at times, a little dangerous).
The London sequences are largely made up of internal sets, an environment that proves to be stuffy and oppressive for our heroines—the spirited Marianne in particular. Lee does well to make her appear out of place outside her usual romantic country landscape.
For many fans, Sense and Sensibility is made of its performances. Winslet is appropriately youthful and dramatic, and Hugh Laurie skilfully balances sarcasm and sincerity. Alan Rickman’s quiet and ruminative portrayal of Colonel Brandon is pathos-inducing. His whispered “Give me an occupation, Miss Dashwood. Or I shall run mad,” is a particular favourite among romantically inclined viewers. Most iconic of all, of course, is the performance of the film’s lead (a role Ang Lee insisted she take).
Thompson’s performance throughout is contained and stoic, and she allows Elinor only the briefest lapses of emotion. Her subtle horror, upon hearing Lucy Steel’s secret, is mesmerising. Her face is straight enough to conceal her own truth from from Lucy, but her eyes are just fragile enough to let the audience into the depth of her personal heartbreak.
When all is finally resolved, and Elinor is given her happy ending in the form of a proposal from the once unreachable Edward Ferrars (Hugh Grant), we are treated both to Thompson’s landmark emotional performance, in which she breaks down crying, and to Grant’s genuine reaction. (Thompson, according to interviews, had neglected to tell Grant of this creative choice, leaving him rather surprised on the day). In this moment, Thompson gives Elinor, and everything she’s bravely suffered though, the respect she deserves. Again and again, we have watched her quietly put aside her own happiness for the sake of others. Never allowing herself to fuss. In this moment, Thompson allows Elinor to feel and express all of it. To take a break (for once in her life) from sense, and display a little of her sister’s sensibility.
Find out more about Pictureville Cinema and get tickets for Sense and Sensibility here.
by jsendak | Aug 21, 2025 | AI
Discover how Blue J is transforming tax research with AI-powered tools built on GPT-4.1. By combining domain expertise with Retrieval-Augmented Generation, Blue J delivers fast, accurate, and fully-cited tax answers—trusted by professionals across the US, Canada, and the UK.
by jsendak | Aug 20, 2025 | Art
The Intersection of Technology and Music: A Historical Perspective
In today’s digital age, technology has become an integral part of nearly every aspect of our lives. From the way we communicate to the way we consume media, there’s no denying that technology has revolutionized the way we interact with the world around us. This is particularly evident in the realm of music, where advancements in technology have fundamentally transformed the creation, distribution, and consumption of music.
One cannot discuss the relationship between technology and music without acknowledging the groundbreaking work of German electronic music pioneers Kraftwerk. Formed in the early 1970s, Kraftwerk’s innovative use of synthesizers and electronic instruments paved the way for the genre of electronic music, influencing countless artists and shaping the sound of popular music for decades to come.
The Influence of Technology on Musical Expression
With the advent of digital audio workstations and music production software, artists now have unprecedented control over every aspect of their music, allowing for greater experimentation and creativity in the studio. From the use of sampled loops to algorithm-generated compositions, technology has opened up new possibilities for musical expression that were once unimaginable.
Challenges and Opportunities in the Digital Age
While technology has undoubtedly democratized the music industry, allowing artists to self-produce and distribute their music online, it has also presented new challenges. Issues of copyright infringement, streaming royalties, and the rise of algorithm-driven playlists have all raised concerns about the future of the music industry and the livelihood of musicians.
Despite these challenges, one thing remains clear: the intersection of technology and music is a dynamic and ever-evolving landscape that continues to shape the way we experience and engage with music. In this article, we will explore the impact of technology on the music industry, from the rise of electronic music to the challenges and opportunities presented by the digital age.
Exhibition & Music Mensch Maschine
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by jsendak | Aug 20, 2025 | DS Articles
Another interesting decision has been made, this time for my paper titled:
“Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation”. By European Actuarial Journal. First time, 7 years ago, it was for Swap curve construction for insurance pricing, based on no arbitrage short rate models – which works perfectly. I might decide to publish the review one day.
I think, as it’s been suggested to me (after being, institutionally and not only, knocked down several times), that documenting even the subtle harassment is important, especially when my reality (and reality in general) is questioned in order to create auto-prophecies.
What is astonishing (suprisingly only to me, so far) is that people do not hesitate to write absurdities down, even when facing compelling receipts that will remain written virtually forever. Is this the era of post-truth I’ve been hearing about? Or is there another hidden reality that I am not aware of (which doesn’t even justify the gigantic institutional, and not only, gaslighting)?
The paper introduces a general machine learning framework for yield curve modeling, in which classical parametric models such as Nelson-Siegel and Svensson serve as special cases within a broader class of functional regression approaches. By linearizing the bond pricing/swap valuation equation, I reformulate the estimation of spot rates as a supervised regression problem, where the response variable is derived from observed bond prices and cash flows, and the regressors are constructed as flexible functions of time-to-maturities. I show that this formulation supports a wide range of modeling strategies — including polynomial expansions, Laguerre polynomials, kernel methods, and regularized linearmodels — all within a unified framework that could preserve economic interpretability. This enables not only curve calibration but also static interpolation and extrapolation. By abstracting away from any fixed parametric structure, my framework bridges the gap between traditional yield curve modeling and modern supervised learning, offering a robust, extensible, and data-driven tool for financial applications ranging from asset pricing to regulatory (?) reporting.
The reviewer says:
”
-
The approach is based on a linearization of the exponential function, which can lead to significant errors
-
The regression uses errors that are correlated with the regressors, which leads to model misspecification
Therefore, I cannot recommend this paper for publication.
“
Ok, so:
- which can lead to significant errors: in theory yes. But in pratice? Have you read the paper? Have you seen the residuals? Have you seen the images? Have you seen the comparison with other published papers? The residuals are less than 1e-18.
- what if I use independent Gaussian or whatever specification of errors instead? Or remove the errors altogether? The reviewer does not even suggest that. He just says “which leads to model misspecification”.
Based on the paper (at least on swaps data, for bond data, could be discussed: what if I use independent Gaussian or whatever specification of errors instead?) linked below + the magnitude of the residuals (< 1e-18) + the images + the comparison with other high-profile published papers, could anyone see what they are talking about? That couldn’t be improved?
At least recognize the novelty and suggest ways to improve, instead of opposing hate and shadow, arbitrary rules. Have you seen that before? Bootstrapping the yield curve + interpolation + extrapolation (almost the same as I did in Swap curve construction for insurance pricing, based on no arbitrage short rate models) all at once? With a model-agnostic approach, meaning a lot of flexibility based on various model capacities? Couldn’t this paper be at least improved?. This is a generalization of many existing approaches. While it’s grotesque to name names, which I won’t, I can see a lot of papers in this same journal which could easily be master’s dissertations. A lot of “[Place any existing- sophisticated-model-created-by-someone else here] applied to insurance” indeed.
https://www.researchgate.net/publication/392507059_Beyond_Nelson-Siegel_and_splines_A_model-_agnostic_Machine_Learning_framework_for_discount_curve_calibration_interpolation_and_extrapolation
From P.22 in:
“@article{andersen2007discount,
title={Discount curve construction with tension splines},
author={Andersen, Leif},
journal={Review of Derivatives Research},
volume={10},
number={3},
pages={227–267},
year={2007},
publisher={Springer}
}”
From my paper:
From P. 242 in “@misc{anderseninterest,
title={Interest Rate Modeling, 2010},
author={Andersen, L and Piterbarg, V},
publisher={Atlantic Financial Press: London}
}”
From my paper:
CAN YOU SPOT THE MODEL MISSPECIFICATION?
Continue reading: Another interesting decision, now for ‘Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation’
Analysis and Future Implications of the Machine Learning Framework for Yield Curve Modelling
The original text discusses a proposed Machine Learning framework for yield curve modelling as in the paper titled ‘Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation’. The framework proposed in the paper presents a technique for bond pricing and swap valuation that reformulates the estimation of spot rates as a supervised regression problem. This new approach is an extension of classical parametric models and accommodates a wide range of modelling strategies.
Long-term Implications
This framework bridges the gap between traditional yield curve modelling and modern supervised learning, offering a robust, flexible, and data-driven tool for financial applications. The range of applications it may provide includes asset pricing to regulatory reporting. As this machine learning model is flexible and can be tailored to any specifications, financial analysts and economists could use it to develop more robust and accurate methods for yield curve modelling and predicting economic trends. This makes it a potentially valuable tool for institutions engaging in economic forecasting and financial decision-making.
Possible Future Developments
While the paper highlights the promise of this method, the proposed approach faced criticism for potential significant errors due to the model’s linearization of the exponential function and correlated errors with the regressors leading to model misspecification. These criticisms must be addressed in future developments of the model. More rigorous testing, refinement of the model and independent validation could lead to wider acceptance and use of the framework in the finance and economics industry.
Actionable Advice
- Authors should take criticism into consideration and work on refining their model to address the issues raised – i.e., the occurrence of significant errors caused by the method’s linearization of the exponential function, and model misspecification due to correlated errors with regression variables.
- Future research should focus on refining the model and enhancing its robustness, flexibility, and extensibility.
- Financial institutions and economists should keep an eye on the development of this framework as it could potentially revolutionize the way yield curves are modeled, providing more accurate and reliable tools for predicting future economic trends.
Conclusion
Overall, the machine-learning framework for yield curve modelling, while criticized by some, has the potential to be a significant tool in the realm of financial economics. However, for the full potential to be realized, authors need to work on improving and refining their model, addressing the issues currently associated with it. For financial practitioners, this signifies an exciting development on the horizon, indicating that future yield curve modelling could possess more accurate and potent predictive power than ever before.
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by jsendak | Aug 20, 2025 | DS Articles
New to running Python in Docker? This step-by-step guide helps you understand and apply debugging techniques in a containerized environment.
Understanding and Applying Debugging Techniques in a Containerized Environment
Running Python in Docker, or any programming language in a containerized environment, brings numerous benefits including efficiency, scalability, and portability. But mastering debugging in such an environment can be challenging. In the realm of python, the task often becomes complex, requiring understanding and efficient application of debugging techniques. This article seeks to highlight the core aspects of debugging in a containerized environment and the potential future developments around it.
Long-term Implications
The use of a containerized environment in programming is altering the technological landscape. In terms of debugging, mastering the techniques could yield the following benefits:
- Enhanced scalability: Debugging techniques in a containerized environment enables programmers to quickly scale their applications without significant hindrance.
- Improved efficiency: Understanding debugging in Python running in Docker can lead to faster code execution and less wasted resources, thus improving efficiency markedly.
- Greater portability: Ability to debug in a containerized environment adds to the portability of the software, enabling developers to move applications easily across different systems.
Potential Future Developments
The future of debugging Python in Docker points towards greater technological advancements. We can expect even more sophisticated debugging tools and techniques, improved containerization processes, and more accurate problem-solving mechanisms. The aim will be to make the debugging process as efficient and hassle-free as possible.
Actionable Advice
Embrace the containerized environment and learn the necessary debugging techniques. Think of them as long-term skills that will continue to yield benefits as technology advances. Always stay updated on the latest developments and trends.
For developers already using Python in Docker, challenge yourself to employ advanced debugging techniques. Also, remember the core aspects of portability, scalability, and effectiveness at all times. Continually working on these skills will enable you to scale greater heights in your programming career.
Key Takeaways:
- Running Python in a containerized environment requires understanding and mastering debugging techniques.
- Mastering these skills enhances scalability, efficiency, and portability of applications.
- The future of debugging in Docker suggests significant technological progress leading to more advanced debugging tools and techniques.
- Developers should constantly stay abreast with these changes to enhance their debugging skills, thereby improving their programming career prospects.
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