Predicting pharmaceutical particle size distributions using kernel mean embedding

In the pharmaceutical industry, the transition to continuous manufacturing of solid dosage forms is adopted by more and more companies. For these continuous processes, high-quality process models are needed. In pharmaceutical wet granulation, a unit operation in the ConsiGmaTM-25 continuous powder-to-tablet system (GEA Pharma systems, Collette, Wommelgem, Belgium), the product under study presents itself as a collection of particles that differ in shape and size. The measurement of this collection results in a particle size distribution. However, the theoretical basis to describe the physical phenomena leading to changes in this particle size distribution is lacking. It is essential to understand how the particle size distribution changes as a function of the unit operation's process settings, as it has a profound effect on the behavior of the fluid bed dryer. Therefore, we suggest a data-driven modeling framework that links the machine settings of the wet granulation unit operation and the output distribution of granules. We do this without making any assumptions on the nature of the distributions under study. A simulation of the granule size distribution could act as a soft sensor when in-line measurements are challenging to perform. The method of this work is a two-step procedure: first, the measured distributions are transformed into a high-dimensional feature space, where the relation between the machine settings and the distributions can be learnt. Second, the inverse transformation is performed, allowing an interpretation of the results in the original measurement space. Further, a comparison is made with previous work, which employs a more mechanistic framework for describing the granules. A reliable prediction of the granule size is vital in the assurance of quality in the production line, and is needed in the assessment of upstream (feeding) and downstream (drying, milling, and tableting) issues. Now that a validated data-driven framework for predicting pharmaceutical particle size distributions is available, it can be applied in settings such as model-based experimental design and, due to its fast computation, there is potential in real-time model predictive control

Új módszerek az adattömörítésben = New methods in data compression

Univerzális, kis késleltetésű kódokat terveztünk individuális sorozatok veszteséges tömörítésére, melyek ugyanolyan jó teljesítményt nyújtanak, mint a sorozathoz illesztett legjobb időben változó kód egy referenciaosztályból, mely az alkalmazott kódolási eljárást időről időre változtathatja. Hatékony, kis komplexitású implementációt készítettünk arra az esetre, amikor az alap-referenciaosztály a hagyományos vagy bizonyos hálózati skalárkvantálók osztálya. Új útvonalválasztási módszereket dolgoztunk ki kommunikációs hálózatokra, melyek aszimptotikusan ugyanolyan jó QoS (csomagvesztési arány, késleltetés) eredményt adnak, mint a változó hálózati környezethez (utólag) illesztett legjobb út. Kiemelendő, hogy a módszer teljesítménye és komplexitása időben optimális konvergenciasebesség mellett a hálózat méretével (és nem az utak számával) skálázik. Kísérletek szerint az elterjedt standard bájt-alapú tömörítő algoritmusok rosszul teljesítenek, ha a forrás nem bájt-alapú, ugyanakkor a bit-alapú módszerek jól működnek bájt-alapú forrásokra is (továbbá komplexitásuk - az alkalmazott kisebb ábécé miatt - gyakran lényegesen kisebb). Ezt a megfigyelést elméletileg is igazoltuk, megvizsgálva, hogy hogyan közelíthetőek blokk-Markov-források magasabb rendű szimbólum-alapú Markov-modellek segítségével. Megoldottuk a ládapakolási probléma egy szekvenciális, on-line változatát, mely alkalmazható bizonyos, kevés erőforrással rendelkező szenzorok hatékony adásütemezésére. | We designed limited-delay data compression methods that perform asymptotically as well as the best time-varying code from a reference family (matched to the source sequence in hindsight) that can change the employed base code several times. We provided efficient, low-complexity solutions for the cases when the base reference class is the set of traditional or certain network scalar quantizers. We developed routing algorithms for communication networks that can provide asymptotically as good QoS parameters (such as packet loss ratio or delay) as the best fixed path in the network matched to the varying conditions in hindsight. The performance and complexity of the developed methods scale with the size of the network (instead of with the number of paths) even when the rate of convergence (in time) is optimal. Experiments indicate that data for which bytes are not the natural choice of symbols compress poorly using standard byte-based implementations of lossless data compression algorithms, while algorithms working on a bit level perform reasonably on byte-based data (in addition to having computational advantages resulting from operating on a small alphabet). We explained this phenomenon by analyzing how block Markov sources can be approximated with symbol-based higher order Markov sources. We provided a solution to a sequential on-line version of the bin packing problem, which can be applied to schedule transmissions for certain sensors with limited resources

Tight Lower Bounds for Multiplicative Weights Algorithmic Families

We study the fundamental problem of prediction with expert advice and develop regret lower bounds for a large family of algorithms for this problem. We develop simple adversarial primitives, that lend themselves to various combinations leading to sharp lower bounds for many algorithmic families. We use these primitives to show that the classic Multiplicative Weights Algorithm (MWA) has a regret of $\sqrt{\frac{T \ln k}{2}}$, there by completely closing the gap between upper and lower bounds. We further show a regret lower bound of $\frac{2}{3}\sqrt{\frac{T\ln k}{2}}$ for a much more general family of algorithms than MWA, where the learning rate can be arbitrarily varied over time, or even picked from arbitrary distributions over time. We also use our primitives to construct adversaries in the geometric horizon setting for MWA to precisely characterize the regret at $\frac{0.391}{\sqrt{\delta}}$ for the case of $2$ experts and a lower bound of $\frac{1}{2}\sqrt{\frac{\ln k}{2\delta}}$ for the case of arbitrary number of experts $k$