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

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

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

Do We Fully Understand Students' Knowledge States? Identifying and Mitigating Answer Bias in Knowledge Tracing

Knowledge tracing (KT) aims to monitor students' evolving knowledge states through their learning interactions with concept-related questions, and can be indirectly evaluated by predicting how students will perform on future questions. In this paper, we observe that there is a common phenomenon of answer bias, i.e., a highly unbalanced distribution of correct and incorrect answers for each question. Existing models tend to memorize the answer bias as a shortcut for achieving high prediction performance in KT, thereby failing to fully understand students' knowledge states. To address this issue, we approach the KT task from a causality perspective. A causal graph of KT is first established, from which we identify that the impact of answer bias lies in the direct causal effect of questions on students' responses. A novel COunterfactual REasoning (CORE) framework for KT is further proposed, which separately captures the total causal effect and direct causal effect during training, and mitigates answer bias by subtracting the latter from the former in testing. The CORE framework is applicable to various existing KT models, and we implement it based on the prevailing DKT, DKVMN, and AKT models, respectively. Extensive experiments on three benchmark datasets demonstrate the effectiveness of CORE in making the debiased inference for KT.Comment: 13 page