Tunable hysteresis effect for perovskite solar cells

Perovskite solar cells (PSCs) usually suffer from a hysteresis effect in current–voltage measurements, which leads to an inaccurate estimation of the device e fficiency. Although ion migration, charge trapping/ detrapping, and accumulation have been proposed as a b asis for the hysteresis, the origin of the hysteresis has not been apparently unraveled. Herein we reporte d a tunable hysteresis effect based uniquely on open- circuit voltage variations in printable mesos copic PSCs with a simplified triple-layer TiO 2 /ZrO 2 /carbon architecture. The electrons are collected by the compact TiO 2 /mesoporous TiO 2 (c-TiO 2 /mp-TiO 2 )bilayer, and the holes are collected by the carbon layer. By adj usting the spray deposition cycles for the c-TiO 2 layer andUV-ozonetreatment,weachievedhysteresis-norm al, hysteresis-free, and hysteresis-inverted PSCs. Such unique trends of tunable hysteresis are anal yzed by considering the polarization of the TiO 2 /perovskite interface, which can accumulate positive charges reversibly. Successfully tuning of the hysteresis effect clarifies the critical importance of the c-TiO 2 /perovskite interface in controlling the hysteretic trends observed, providing important insights towards the understanding of this rapidly developing photovoltaic technology

Positivstellens\"atze and Moment problems with Universal Quantifiers

This paper studies Positivstellens\"atze and moment problems for sets that are given by universal quantifiers. Let $Q$ be a closed set and let $g = (g_1,...,g_s)$ be a tuple of polynomials in two vector variables $x$ and $y$. Then $K$ is described as the set of all points $x$ such that each $g_j(x, y) \ge 0$ for all $y \in Q$. Fix a measure $\nu$ with $supp(\nu) = Q$, and assume it satisfies the Carleman condition. The first main result of the paper is a Positivstellensatz with universal quantifiers: if a polynomial $f(x)$ is positive on $K$, then it belongs to the quadratic module $QM(g,\nu)$ associated to $(g,\nu)$, under the archimedeanness assumption on $QM(g,\nu)$. Here, $QM(g,\nu)$ denotes the quadratic module of polynomials in $x$ that can be represented as $$\tau_0(x) + \int \tau_1(x,y)g_1(x, y)\, d\nu(y) + \cdots + \int \tau_s(x,y) g_s(x, y)\, d\nu(y),$$ where each $\tau_j$ is a sum of squares polynomial. Second, necessary and sufficient conditions for a full (or truncated) multisequence to admit a representing measure supported in $K$ are given. In particular, the classical flat extension theorem of Curto and Fialkow is generalized to truncated moment problems on such a set $K$. Finally, applications of these results for solving semi-infinite optimization problems are presented

Moment-SOS Relaxation Methods for Generalized Semi-Infinite Programs

In today’s world, Artificial Intelligence is developing quickly and is closely related to our daily lives. Optimization plays an important role in this field. Machine learning, a key area of AI, relies on optimization to enhance model efficiency and precision by minimizing loss functions and updating parameters. However, many real-world learning problems, such as robust training, distributionally robust optimization, and reinforcement learning, naturally involve infinitely many constraints. One example is adversarial training, where a model is trained to resist worst-case perturbations from infinitely many possibilities within a given norm ball. Polynomial optimization considers optimization problems defined by polynomials. In contrast to classical nonlinear optimization, polynomial optimization aims to find global optimizers. In polynomial optimization, we are mostly interested in two major kinds of optimization with infinitely many constraints: semi-infinite programs (SIPs) and generalized semi-infinite programs (GSIPs). SIPs are optimization problems that involve infinitely many constraints, each defined by a parameter within a given parameter set. Besides, the decision variables belong to another constraining set. GSIPs are the generalization form of SIPs, where the parameter set depends on all variables, including both the decision variables and the parameters. SIPs and GSIPs can be formulated as polynomial optimization problems when all the defining functions are polynomials. We propose a hierarchy of polynomial optimization problems based on Moment–Sum of Squares (SOS) relaxations to solve the GSIPs. Lagrange multiplier expressions (LMEs) and polynomial extensions are used to construct this hierarchy. In particular, the classical SIPs can also be solved as a special case of GSIPs. The finite convergence as well as asymptotic convergence of this hierarchy is shown under certain conditions. We also study GSIPs that have infinitely many convex constraints and show that they can be solved exactly by a single polynomial optimization relaxation. The computational efficiency of this algorithm based on Moment-SOS relaxations is demonstrated by extensive numerical results. Specifically, we also study GSIPs with polyhedral parameter sets, where the parameter set is given by a parametrized polyhedron. We propose a new approach to solve them as a disjunctive program. This disjunctive relaxation is based on the Kurash-Kuhn-Tucker (KKT) conditions of the robust constraint and a technique called partial Lagrange multiplier expressions (PLMEs). We summarize a semidefinite programming (SDP)-based algorithm and study its convergence properties. Numerical experiments show the efficiency of this approach in solving GSIPs with polyhedral parameter sets, particularly in applications such as gemstone cutting problems and robust control. For SIPs with non-semialgebraic parameter sets, we tackle their solutions by analyzing the algebraic properties of these constraining sets. Positivstellens\"atze, also known as positivity certificates, requires us to find the algebraic conditions under which a given polynomial is nonnegative over a constraining set. The moment problem requires us to determine whether a given sequence of moments corresponds to a measure supported on a given set. In polynomial optimization, these tools provide fundamental techniques for certifying feasibility and deriving semidefinite relaxations. We study Positivstellens\"atze and moment problems for sets defined by constraining functions and constraining sets from SIPs. We fix a measure whose support equals the parameter set and assume it satisfies the Carleman condition. First, we prove a Positivstellensatz for this set. Furthermore, we derive necessary and sufficient conditions for a full (or truncated) multisequence to admit a representing measure supported in this set. Finally, we show its application in SIPs by reformulating them as equivalent optimization problems over moment sequences. Under certain assumptions, this reformulation can be approximated by a finite hierarchy of SDP relaxations

Hypoimmune induced pluripotent stem cells survive long term in fully immunocompetent, allogeneic rhesus macaques

Genetic engineering of allogeneic cell therapeutics that fully prevents rejection by a recipient\u27s immune system would abolish the requirement for immunosuppressive drugs or encapsulation and support large-scale manufacturing of off-the-shelf cell products. Previously, we generated mouse and human hypoimmune pluripotent (HIP) stem cells by depleting HLA class I and II molecules and overexpressing CD47 (B2

RADAR: Robust AI-Text Detection via Adversarial Learning

Recent advances in large language models (LLMs) and the intensifying popularity of ChatGPT-like applications have blurred the boundary of high-quality text generation between humans and machines. However, in addition to the anticipated revolutionary changes to our technology and society, the difficulty of distinguishing LLM-generated texts (AI-text) from human-generated texts poses new challenges of misuse and fairness, such as fake content generation, plagiarism, and false accusation of innocent writers. While existing works show that current AI-text detectors are not robust to LLM-based paraphrasing, this paper aims to bridge this gap by proposing a new framework called RADAR, which jointly trains a Robust AI-text Detector via Adversarial leaRning. RADAR is based on adversarial training of a paraphraser and a detector. The paraphraser's goal is to generate realistic contents to evade AI-text detection. RADAR uses the feedback from the detector to update the paraphraser, and vice versa. Evaluated with 8 different LLMs (Pythia, Dolly 2.0, Palmyra, Camel, GPT-J, Dolly 1.0, LLaMA, and Vicuna) across 4 datasets, experimental results show that RADAR significantly outperforms existing AI-text detection methods, especially when paraphrasing is in place. We also identify the strong transferability of RADAR from instruction-tuned LLMs to other LLMs, and evaluate the improved capability of RADAR via GPT-3.5.Comment: Preprint. Project page and demos: https://radar.vizhub.a

A gene catalogue for post-diapause development of an anhydrobiotic arthropod Artemia franciscana

<p>Abstract</p> <p>Background</p> <p>Diapause is a reversible state of developmental suspension and found among diverse taxa, from plants to animals, including marsupials and some other mammals. Although previous work has accumulated ample data, the molecular mechanism underlying diapause and reactivation from it remain elusive.</p> <p>Results</p> <p>Using <it>Artemia franciscana</it>, a model organism to study the development of post-diapause embryos in Arthropod, we sequenced random clones up to a total of 28,039 ESTs from four cDNA libraries made from dehydrated cysts and three time points after rehydration/reactivation, which were assembled into 8,018 unigene clusters. We identified 324 differentially-expressed genes (DEGs, <it>P </it>< 0.05) based on pairwise comparisons of the four cDNA libraries. We identified a group of genes that are involved in an anti-water-deficit system, including proteases, protease inhibitors, heat shock proteins, and several novel members of the late embryogenesis abundant (LEA) protein family. In addition, we classified most of the up-regulated genes after cyst reactivation into metabolism, biosynthesis, transcription, and translation, and this result is consistent with the rapid development of the embryo. Some of the specific expressions of DEGs were confirmed experimentally based on quantitative real-time PCR.</p> <p>Conclusion</p> <p>We found that the first 5-hour period after rehydration is most important for embryonic reactivation of <it>Artemia</it>. As the total number of expressed genes increases significantly, the majority of DEGs were also identified in this period, including a group of water-deficient-induced genes. A group of genes with similar functions have been described in plant seeds; for instance, one of the novel LEA members shares ~70% amino-acid identity with an <it>Arabidopsis </it>EM (embryonic abundant) protein, the closest animal relative to plant LEA families identified thus far. Our findings also suggested that not only nutrition, but also mRNAs are produced and stored during cyst formation to support rapid development after reactivation.</p

GlycoPep MassList: Software to Generate Massive Inclusion Lists for Glycopeptide Analyses

Protein glycosylation drives many biological processes and serves as markers for disease; therefore, the development of tools to study glycosylation is an essential and growing area of research. Mass spectrometry can be used to identify both the glycans of interest and the glycosylation sites to which those glycans are attached, when proteins are proteolytically digested and their glycopeptides are analyzed by a combination of high-resolution mass spectrometry (MS) and tandem mass spectrometry (MS/MS) methods. One major challenge in these experiments is collecting the requisite MS/MS data. The digested glycopeptides are often present in complex mixtures and in low abundance, and the most commonly used approach to collect MS/MS data on these species is data-dependent acquisition (DDA), where only the most intense precursor ions trigger MS/MS. DDA results in limited glycopeptide coverage. Semi-targeted data acquisition is an alternative experimental approach that can alleviate this difficulty. However, due to the massive heterogeneity of glycopeptides, it is not obvious how to expediently generate inclusion lists for these types of analyses. To solve this problem, we developed the software tool GlycoPep MassList, which can be used to generate inclusion lists for liquid chromatography tandem-mass spectrometry (LC-MS/MS) experiments. The utility of the software was tested by conducting comparisons between semi-targeted and untargeted data-dependent analysis experiments on a variety of proteins, including IgG, a protein whose glycosylation must be characterized during its production as a biotherapeutic. When the GlycoPep MassList software was used to generate inclusion lists for LC-MS/MS experiments, more unique glycopeptides were selected for fragmentation. Generally, ∼30 % more unique glycopeptides can be analyzed per protein, in the simplest cases, with low background. In cases where background ions from proteins or other interferents are high, usage of an inclusion list is even more advantageous. The software is freely publically accessible

The high dimensional psychological profile and cultural bias of ChatGPT

Given the rapid advancement of large-scale language models, artificial intelligence (AI) models, like ChatGPT, are playing an increasingly prominent role in human society. However, to ensure that artificial intelligence models benefit human society, we must first fully understand the similarities and differences between the human-like characteristics exhibited by artificial intelligence models and real humans, as well as the cultural stereotypes and biases that artificial intelligence models may exhibit in the process of interacting with humans. This study first measured ChatGPT in 84 dimensions of psychological characteristics, revealing differences between ChatGPT and human norms in most dimensions as well as in high-dimensional psychological representations. Additionally, through the measurement of ChatGPT in 13 dimensions of cultural values, it was revealed that ChatGPT's cultural value patterns are dissimilar to those of various countries/regions worldwide. Finally, an analysis of ChatGPT's performance in eight decision-making tasks involving interactions with humans from different countries/regions revealed that ChatGPT exhibits clear cultural stereotypes in most decision-making tasks and shows significant cultural bias in third-party punishment and ultimatum games. The findings indicate that, compared to humans, ChatGPT exhibits a distinct psychological profile and cultural value orientation, and it also shows cultural biases and stereotypes in interpersonal decision-making. Future research endeavors should emphasize enhanced technical oversight and augmented transparency in the database and algorithmic training procedures to foster more efficient cross-cultural communication and mitigate social disparities

Transcriptomic profiling of mature embryo from an elite super-hybrid rice LYP9 and its parental lines

<p>Abstract</p> <p>Background</p> <p>The mature embryo of rice (<it>Oryza sativa, L</it>.) is a synchronized and integrated tissue mass laying the foundation at molecular level for its growth, development, and differentiation toward a developing and ultimately a mature plant. We carried out an EST (expressed-sequence-tags)-based transcriptomic study, aiming at gaining molecular insights into embryonic development of a rice hybrid triad–an elite hybrid rice <it>LYP</it>9 and its parental lines (<it>93-11 </it>and <it>PA64s</it>)–and possible relatedness to heterosis.</p> <p>Results</p> <p>We generated 27,566 high-quality ESTs from cDNA libraries made from mature rice embryos. We classified these ESTs into 7,557 unigenes (2,511 contigs and 5,046 singletons) and 7,250 (95.9%) of them were annotated. We noticed that the high-abundance genes in mature rice embryos belong to two major functional categories, stress-tolerance and preparation-for-development, and we also identified 191 differentially-expressed genes (General Chi-squared test, <it>P</it>-value <= 0.05) between <it>LYP9 </it>and its parental lines, representing typical expression patterns including over-dominance, high- and low-parent dominance, additivity, and under-dominance. In <it>LYP9</it>, the majority of embryo-associated genes were found not only abundantly and specifically enriched but also significantly up-regulated.</p> <p>Conclusion</p> <p>Our results suggested that massively strengthening tissue-(or stage-) characteristic functions may contribute to heterosis rather than a few simple mechanistic explanations at the individual gene level. In addition, the large collection of rice embryonic ESTs provides significant amount of data for future comparative analyses on plant development, especially for the important crops of the grass family.</p