Translingual Discrimination, by Sender Dovchin. Cambridge University Press, 2022, 92 pp., USD 22.00 (pbk), ISBN 978-1-009-20973-1

Book Revie

Super‐Resolution Confocal Microscopy Through Pixel Reassignment

Confocal microscopy has gained great popularity in the observation of biological microstructures and dynamic processes. Its resolution enhancement comes from shrinking the pinhole size, which, however, degrades imaging signal‐to‐noise ratio (SNR) severely. Recently developed super‐resolution method based on the pixel reassignment technique is capable of achieving a factor of 2 resolution improvement and further reaching twofold improvement by deconvolution, compared with the optical diffraction limit. More importantly, the approach allows better imaging SNR when its lateral resolution is similar to the standard confocal microscopy. Pixel reassignment can be realized both computationally and optically, but the optical realization demonstrates much faster acquisition of super‐resolution imaging. In this chapter, the development and advancement of super‐resolution confocal microscopy through the pixel realignment method are summarized, and its capabilities of imaging biological structures and interactions are represented

Biomethane production in an innovative two-phase pressurized anaerobic digestion system

Generation of biogas from biomass through anaerobic digestion is receiving increasing attention. Over the past decade, the biogas industry has been developing rapidly in Germany, as well as the rest of the world. In Germany, biogas is generally used in a heat and power plant (CHP) for electricity and heat production. However, most biogas plants are located in a rural area, where heating demands are quite low. Except for biogas plant thermal control, a huge amount of cogenerated heat is often wasted. In order to increase the overall energy utilization efficiency, biogas can be alternatively converted to biomethane of natural gas quality and injected into existing gas grids. By making use of the mature gas transportation and storage systems, biogas production and end utilization can be temporally and spatially separated. Therefore, it is regarded as an efficient and flexible solution to energy issues. Nevertheless, in terms of this application, raw biogas requires, above all, gas purification and upgrading. Carbon dioxide content, in particular, must be reduced from 4050% in the raw biogas to approximately 4% in the purified gas. Conventional technologies are generally expensive in investment and/or operation. Therefore, an economical option is desired. Within this research project, a two-phase pressurized anaerobic digestion system was developed. The innovative concept aimed to reduce the cost involved in biomethane conversion and injection into the natural gas grids by integration of biogas production, purification and compression in one system. It was expected that a great amount of carbon dioxide could be directly removed from the pressurized digester due to its high solubility. In addition, the methane-rich biogas could be produced at an elevated pressure which could meet the injection standard, and therefore could reduce or even avoid the expenses for further compression. In order to gain better understanding of two-phase pressurized anaerobic digestion, three major studies were conducted: - The pressure effects on two-phase anaerobic digestion - Effects of organic loading rate (OLR) on the performance of a pressurized anaerobic filter in two-phase anaerobic digestion - Effects of liquid circulation on two-phase pressurized anaerobic digestion By this means, the system performance could be examined and the technical feasibility and potential of the new concept could be explored. Moreover, an optimization of the process in a two-phase pressurized anaerobic digestion system could be realized. From both economic and ecological perspective, two-phase pressurized anaerobic digestion offers an interesting process option for biomethane production, making a great contribution to sustainable energy supply.Die Erzeugung von Biogas aus Biomasse durch anaerobe Vergärung erfährt vor dem Hintergrund einer nachhaltigen Energieversorgung eine immer größere Aufmerksamkeit. Im letzten Jahrzehnt hat sich, nicht nur in Deutschland, sondern auch weltweit, eine wachsende Biogasindustrie entwickelt. Traditionell wird in Deutschland bisher Biogas in Blockheizkraftwerken in Wärme und Strom umgewandelt. Die meisten Biogasanlagen sind jedoch in ländlichen Gebieten angesiedelt, wo der Wärmebedarf eher gering ist. Abgesehen von der Wärmenutzung für die Biogasanlagen wird eine große Menge der erzeugten Wärme ungenutzt an die Umwelt abgegeben. Um die Effizienz der gesamten Energieverwendung zu steigern, kann Biogas alternativ auch in Biomethan umgewandelt werden, welches in das vorhandene Gasnetz eingespeist wird. Auf diesem Weg kann die Biogasproduktion von der Nutzung räumlich und zeitlich entkoppelt werden, da mit dem Erdgasnetz ein leistungsfähiges Transport- und Speichersystem zur Verfügung steht. Vor der Einspeisung muss Rohbiogas jedoch einem aufwendigen Reinigungs- und Aufbereitungsverfahren unterzogen werden. Insbesondere ist der Kohlenstoffdioxidgehalt des Biogases von 4050 % im Rohgas auf ca. 4 % im Reingas zu reduzieren. Die dazu verwendeten konventionellen Technologien sind häufig technisch sehr aufwändig und nur für Großanlagen geeignet. Daher ist eine ökonomische Lösung auch für kleinere Biogasanlagen wünschenswert. Im Rahmen des Forschungsprojektes wurde ein zweiphasiges Druckfermentationssystem entwickelt. Das innovative Konzept strebt eine Kostenreduzierung der Biomethanerzeugung und einspeisung an, indem die Biogasproduktion, -reinigung und -verdichtung in ein Verfahren integriert werden. Es wurde erwartet, dass, aufgrund der höheren Wasserlöslichkeit im Vergleich zu Methan, eine große Menge Kohlenstoffdioxid mit der Prozessflüssigkeit aus dem unter Druck stehenden Fermenter entfernt werden kann. Darüber hinaus sollte das methanreiche Biogas unter erhöhtem Druck produziert werden, welcher dem Einspeisungsstandard entspricht und somit die Kosten einer weiteren Verdichtung reduzieren oder sogar vermeiden könnte. Um ein besseres Verständnis der zweiphasigen anaeroben Druckfermentation zu gewinnen, wurden drei umfassende Studien in folgenden Bereichen durchgeführt: - Der Einfluss des Drucks auf die zweiphasige anaerobe Vergärung - Der Einfluss der Raumbelastung (BR) auf die Leistung eines unter Druck gesetzten Methanreaktors bei der zweiphasigen anaeroben Vergärung - Der Einfluss der Flüssigkeitszirkulation auf die Qualität der produzierten biogenen Gase Auf diese Weise konnten die Leistung des Systems untersucht und die technische Durchführbarkeit sowie das Potenzial des neuen Konzepts erforscht werden. Des Weiteren konnte eine Optimierung des Prozesses im zweiphasigen Druckfermentationssystem realisiert werden. Zusammenfassend ist festzuhalten, dass die zweiphasige Druckfermentation sowohl unter ökonomischen als auch unter ökologischen Gesichtspunkten ein interessantes Verfahren der Biomethanerzeugung darstellt und somit zu einer nachhaltigen Energieversorgung beitragen kann

Bridging Convex and Nonconvex Optimization in Robust PCA: Noise, Outliers, and Missing Data

This paper delivers improved theoretical guarantees for the convex programming approach in low-rank matrix estimation, in the presence of (1) random noise, (2) gross sparse outliers, and (3) missing data. This problem, often dubbed as robust principal component analysis (robust PCA), finds applications in various domains. Despite the wide applicability of convex relaxation, the available statistical support (particularly the stability analysis vis-a-vis random noise) remains highly suboptimal, which we strengthen in this paper. When the unknown matrix is well-conditioned, incoherent, and of constant rank, we demonstrate that a principled convex program achieves near-optimal statistical accuracy, in terms of both the Euclidean loss and the $\ell_{\infty}$ loss. All of this happens even when nearly a constant fraction of observations are corrupted by outliers with arbitrary magnitudes. The key analysis idea lies in bridging the convex program in use and an auxiliary nonconvex optimization algorithm, and hence the title of this paper

Gut microbiota, inflammatory proteins and COVID-19: a Mendelian randomisation study

BackgroundThe human gut microbiota has been identified as a potentially important factor influencing the development of COVID-19. It is believed that the disease primarily affects the organism through inflammatory pathways. With the aim of improving early diagnosis and targeted therapy, it is crucial to identify the specific gut microbiota associated with COVID-19 and to gain a deeper understanding of the underlying processes. The present study sought to investigate the potential causal relationship between the gut microbiota and COVID-19, and to determine the extent to which inflammatory proteins act as mediators in this relationship.MethodsBidirectional mendelian randomization (MR) and Two-step mediated MR analyses were applied to examine causative associations among 196 gut microbiota, 91 inflammatory proteins and COVID-19. The main analytical method used in the MR was the random effects inverse variance weighted (IVW) method. This was complemented by the Bayesian weighted Mendelian randomization (BWMR) method, which was utilized to test the hypothesis of MR. In order for the results to be deemed reliable, statistical significance was required for both methods. Validation was then carried out using an external dataset, and further meta-analyses were conducted to authenticate that the association was reliable.ResultsResults of our research indicated that seven gut microbiota were actively associated to the COVID-19 risk. Five inflammatory proteins were associated with COVID-19 risk, of which three were positively and two were negatively identified with COVID-19. Further validation was carried out using sensitivity analyses. Mediated MR results revealed that CCL2 was a possible mediator of causality of family Bifidobacteriaceae and order Bifidobacteriales with COVID-19, mediating at a ratio of 12.73%.ConclusionSuggesting a genetic causation between specific gut microbiota and COVID-19, our present research emphasizes the underlying mediating role of CCL2, an inflammatory factor, and contributes to a deeper understanding of the mechanism of action underlying COVID-19

An Information Minimization Based Contrastive Learning Model for Unsupervised Sentence Embeddings Learning

Unsupervised sentence embeddings learning has been recently dominated by contrastive learning methods (e.g., SimCSE), which keep positive pairs similar and push negative pairs apart. The contrast operation aims to keep as much information as possible by maximizing the mutual information between positive instances, which leads to redundant information in sentence embedding. To address this problem, we present an information minimization based contrastive learning (InforMin-CL) model to retain the useful information and discard the redundant information by maximizing the mutual information and minimizing the information entropy between positive instances meanwhile for unsupervised sentence representation learning. Specifically, we find that information minimization can be achieved by simple contrast and reconstruction objectives. The reconstruction operation reconstitutes the positive instance via the other positive instance to minimize the information entropy between positive instances. We evaluate our model on fourteen downstream tasks, including both supervised and unsupervised (semantic textual similarity) tasks. Extensive experimental results show that our InforMin-CL obtains a state-of-the-art performance.Comment: 11 pages, 3 figures, published to COLING 202

Model-Based Reinforcement Learning for Offline Zero-Sum Markov Games

This paper makes progress towards learning Nash equilibria in two-player zero-sum Markov games from offline data. Specifically, consider a $\gamma$-discounted infinite-horizon Markov game with $S$ states, where the max-player has $A$ actions and the min-player has $B$ actions. We propose a pessimistic model-based algorithm with Bernstein-style lower confidence bounds -- called VI-LCB-Game -- that provably finds an $\varepsilon$-approximate Nash equilibrium with a sample complexity no larger than $\frac{C_{\mathsf{clipped}}^{\star}S(A+B)}{(1-\gamma)^{3}\varepsilon^{2}}$ (up to some log factor). Here, $C_{\mathsf{clipped}}^{\star}$ is some unilateral clipped concentrability coefficient that reflects the coverage and distribution shift of the available data (vis-\`a-vis the target data), and the target accuracy $\varepsilon$ can be any value within $\big(0,\frac{1}{1-\gamma}\big]$. Our sample complexity bound strengthens prior art by a factor of $\min\{A,B\}$, achieving minimax optimality for the entire $\varepsilon$-range. An appealing feature of our result lies in algorithmic simplicity, which reveals the unnecessity of variance reduction and sample splitting in achieving sample optimality.Comment: accepted to Operations Researc

Inference and Uncertainty Quantification for Noisy Matrix Completion

Noisy matrix completion aims at estimating a low-rank matrix given only partial and corrupted entries. Despite substantial progress in designing efficient estimation algorithms, it remains largely unclear how to assess the uncertainty of the obtained estimates and how to perform statistical inference on the unknown matrix (e.g.~constructing a valid and short confidence interval for an unseen entry). This paper takes a step towards inference and uncertainty quantification for noisy matrix completion. We develop a simple procedure to compensate for the bias of the widely used convex and nonconvex estimators. The resulting de-biased estimators admit nearly precise non-asymptotic distributional characterizations, which in turn enable optimal construction of confidence intervals\,/\,regions for, say, the missing entries and the low-rank factors. Our inferential procedures do not rely on sample splitting, thus avoiding unnecessary loss of data efficiency. As a byproduct, we obtain a sharp characterization of the estimation accuracy of our de-biased estimators, which, to the best of our knowledge, are the first tractable algorithms that provably achieve full statistical efficiency (including the preconstant). The analysis herein is built upon the intimate link between convex and nonconvex optimization --- an appealing feature recently discovered by \cite{chen2019noisy}.Comment: published at Proceedings of the National Academy of Sciences Nov 2019, 116 (46) 22931-2293

Traceable and authenticated key negotiations via blockchain for vehicular communications

While key negotiation schemes, such as those based on Diffie–Hellman, have been the subject of ongoing research, designing an efficient and security scheme remains challenging. In this paper, we propose a novel key negotiation scheme based on blockchain, which can be deployed in blockchain-enabled contexts such as data sharing or facilitating electric transactions between vehicles (e.g., unmanned vehicles). We propose three candidates for flexible selection, namely, key exchanges via transaction currency values through value channels (such as the amount in transactions), automated key exchanges through static scripts,and dynamic scripts, which can not only guarantee key availability with timeliness but also defend against MITM (man-in-the-middle) attacks, packet-dropping attacks, and decryption failure attacks