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This paper studies generalized inverse reinforcement learning (GIRL) in Markov decision processes (MDPs), that is, the problem of learning the basic components of an MDP given observed behavior (policy) that might not be optimal. These components include not only the reward function and transition probability matrices, but also the action space and state space that are not exactly known but are known to belong to given uncertainty sets. We address two key challenges in GIRL: first, the need to quantify the discrepancy between the observed policy and the underlying optimal policy; second, the difficulty of mathematically characterizing the underlying optimal policy when the basic components of an MDP are unobservable or partially observable. Then, we propose the mathematical formulation for GIRL and develop a fast heuristic algorithm. Numerical results on both finite and infinite state problems show the merit of our formulation and algorithm.

Dark patterns and deceptive designs (DPs) are user interface elements that trick people into taking actions that benefit the purveyor. Such designs are widely deployed, with special varieties found in certain nations like Japan that can be traced to global power hierarchies and the local socio-linguistic context of use. In this breaking work, we report on the first user study involving Japanese people (n=30) experiencing a mock shopping website injected with simulated DPs. We found that Alphabet Soup and Misleading Reference Pricing were the most deceptive and least noticeable. Social Proofs, Sneaking in Items, and Untranslation were the least deceptive but Untranslation prevented most from cancelling their account. Mood significantly worsened after experiencing the website. We contribute the first empirical findings on a Japanese consumer base alongside a scalable approach to evaluating user attitudes, perceptions, and behaviours towards DPs in an interactive context. We urge for more human participant research and ideally collaborations with industry to assess real designs in the wild.

Kawaii computing is a new term for a steadily growing body of work on the Japanese notion of "cute" in human-computer interaction (HCI) research and practice. Kawaii is distinguished from general notions of cute by its experiential and culturally-sensitive nature. While it can be designed into the appearance and behaviour of interactive agents, interfaces, and systems, kawaii also refers to certain affective and cultural dimensions experienced by culturally Japanese users, i.e., kawaii user experiences (UX) and mental models of kawaii elicited by the socio-cultural context of Japan. In this scoping review, we map out the ways in which kawaii has been explored within HCI research and related fields as a factor of design and experience. We illuminate theoretical and methodological gaps and opportunities for future work on kawaii computing.

Many cancer drugs fail at treating solid epithelial tumors with hypoxia and insufficient drug penetration thought to be contributing factors to the observed chemoresistance. Owing to this, it is imperative to evaluate potential cancer drugs in conditions as close to in vivo as possible, which is not always done. To address this, we developed a mass spectrometry-based protein footprinting method for exploring the impact of hypoxia on protein in 3D colorectal cancer cells. Our group has previously extended the protein footprinting method fast photochemical oxidation of proteins (FPOP) for live cell analysis (IC-FPOP); however, this is the first application of IC-FPOP in a 3D cancer model. In this study, we perform IC-FPOP on intact spheroids (Spheroid-FPOP) using a modified version of the static platform incubator with an XY movable stage (PIXY) FPOP platform. We detected modification in each of three spheroid layers, even the hypoxic core. Pathway analysis revealed protein modifications in over 10 distinct protein pathways, including some involved in protein ubiquitination; a process modulated in cancer pathologies. These results demonstrate the feasibility of Spheroid-FPOP to be utilized as a tool to interrogate protein interactions within a native tumor microenvironment.

A novel metal-organic framework (MOF) Zn-PBC (H2PBC = pyridine-3,5-bis(phenyl-4-carboxylic acid)) was designed and synthesized via a solvothermal reaction with the H2PBC ligand, and produced a strong fluorescence. The material exhibited good stability and an ideal luminescent property in water. In addition, it was found that Zn-PBC displayed a different fluorescent response to different types of amino acids, and the mechanism was investigated. This research might give insight to the interaction between MOFs and amino acids, which would provide a strategy to fabricate MOF-based sensors for biomolecules in future. Zn-PBC, a metal-organic framework with ideal fluorescent property was prepared. It exhibited differential fluorescent response to different kinds of amino acids, which might provide a strategy to design MOF-based sensors for biomolecules in future.

It is very difficult to forecast the production rate of oil wells as the output of a single well is sensitive to various uncertain factors, which implicitly or explicitly show the influence of the static, temporal and spatial properties on the oil well production. In this study, a novel machine learning model is constructed to fuse the static geological information, dynamic well production history, and spatial information of the adjacent water injection wells. There are 3 basic modules in this stacking model, which are regarded as the encoders to extract the features from different types of data. One is Multi-Layer Perceptron, which is to analyze the static geological properties of the reservoir that might influence the well production rate. The other two are both LSTMs, which have the input in the form of two matrices rather than vectors, standing for the temporal and the spatial information of the target well. The difference of the two modules is that in the spatial information processing module we take into consideration the time delay of water flooding response, from the injection well to the target well. In addition, we use Symbolic Transfer Entropy to prove the superiorities of the stacking model from the perspective of Causality Discovery. It is proved theoretically and practically that the presented model can make full use of the model structure to integrate the characteristics of the data and the experts' knowledge into the process of machine learning, greatly improving the accuracy and generalization ability of prediction.

We consider infinite horizon Markov decision processes (MDPs) with fast-slow structure, meaning that certain parts of the state space move "fast" (and in a sense, are more influential) while other parts transition more "slowly." Such structure is common in real-world problems where sequential decisions need to be made at high frequencies, yet information that varies at a slower timescale also influences the optimal policy. Examples include: (1) service allocation for a multi-class queue with (slowly varying) stochastic costs, (2) a restless multi-armed bandit with an environmental state, and (3) energy demand response, where both day-ahead and real-time prices play a role in the firm's revenue. Models that fully capture these problems often result in MDPs with large state spaces and large effective time horizons (due to frequent decisions), rendering them computationally intractable. We propose an approximate dynamic programming algorithmic framework based on the idea of "freezing" the slow states, solving a set of simpler finite-horizon MDPs (the lower-level MDPs), and applying value iteration (VI) to an auxiliary MDP that transitions on a slower timescale (the upper-level MDP). We also extend the technique to a function approximation setting, where a feature-based linear architecture is used. On the theoretical side, we analyze the regret incurred by each variant of our frozen-state approach. Finally, we give empirical evidence that the frozen-state approach generates effective policies using just a fraction of the computational cost, while illustrating that simply omitting slow states from the decision modeling is often not a viable heuristic., Comment: 69 pages, 9 figures

Large language models (LLMs) show promise for natural language tasks but struggle when applied directly to complex domains like finance. LLMs have difficulty reasoning about and integrating all relevant information. We propose a data-centric approach to enable LLMs to better handle financial tasks. Our key insight is that rather than overloading the LLM with everything at once, it is more effective to preprocess and pre-understand the data. We create a financial LLM (FLLM) using multitask prompt-based finetuning to achieve data pre-processing and pre-understanding. However, labeled data is scarce for each task. To overcome manual annotation costs, we employ abductive augmentation reasoning (AAR) to automatically generate training data by modifying the pseudo labels from FLLM's own outputs. Experiments show our data-centric FLLM with AAR substantially outperforms baseline financial LLMs designed for raw text, achieving state-of-the-art on financial analysis and interpretation tasks. We also open source a new benchmark for financial analysis and interpretation. Our methodology provides a promising path to unlock LLMs' potential for complex real-world domains.

We propose a general framework for decoding quantum error-correcting codes with generative modeling. The model utilizes autoregressive neural networks, specifically Transformers, to learn the joint probability of logical operators and syndromes. This training is in an unsupervised way, without the need for labeled training data, and is thus referred to as pre-training. After the pre-training, the model can efficiently compute the likelihood of logical operators for any given syndrome, using maximum likelihood decoding. It can directly generate the most-likely logical operators with computational complexity $\mathcal O(2k)$ in the number of logical qubits $k$, which is significantly better than the conventional maximum likelihood decoding algorithms that require $\mathcal O(4^k)$ computation. Based on the pre-trained model, we further propose refinement to achieve more accurately the likelihood of logical operators for a given syndrome by directly sampling the stabilizer operators. We perform numerical experiments on stabilizer codes with small code distances, using both depolarizing error models and error models with correlated noise. The results show that our approach provides significantly better decoding accuracy than the minimum weight perfect matching and belief-propagation-based algorithms. Our framework is general and can be applied to any error model and quantum codes with different topologies such as surface codes and quantum LDPC codes. Furthermore, it leverages the parallelization capabilities of GPUs, enabling simultaneous decoding of a large number of syndromes. Our approach sheds light on the efficient and accurate decoding of quantum error-correcting codes using generative artificial intelligence and modern computational power., Comment: Comments are welcome

Alkali metals are regarded as the most promising candidates for advanced anode for the next-generation batteries due to their high specific capacity, low electrochemical potential, and lightweight. However, critical problems of the alkali metal anodes, especially dendrite formation and interface stabilization, remain challenging to overcome. The solid electrolyte interphase (SEI) is a key factor affecting Li and Na deposition behavior and electrochemical performances. Herein, a facile and universal approach is successfully developed to fabricate ionic conductive interfaces for Li and Na metal anodes by modified atomic layer deposition (ALD). In this process, the Li metal (or Na metal) plays the role of Li (or Na) source without any additional Li (or Na) precursor during ALD. Moreover, the key questions about the influence of ALD deposition temperature on the compositions and structure of the coatings are addressed. The optimized ionic conductive coatings have significantly improved the electrochemical performances. In addition, the electrochemical phase-field model is performed to prove that the ionic conductive coating is very effective in promoting uniform electrodeposition. This approach is universal and can be potentially applied to other different metal anodes. At the same time, it can be extended to other types of coatings or other deposition techniques.

Solving Markov decision processes (MDPs) efficiently is challenging in many cases, for example, when the state space or action space is large, when the reward function is sparse and delayed, and when there is a distribution of MDPs. Structures in the policy, value function, reward function, or state space can be useful in accelerating the learning process. In this thesis, we exploit structures in MDPs to solve them effectively and efficiently. First, we study problems with concave value function and basestock policy and leverage these two structures to propose an approximate dynamic programming (ADP) algorithm. Next, we study the exploration problem in unknown MDPs, introduce structured intrinsic reward to the problem, and propose a Bayes-optimal algorithm for learning the intrinsic reward. Finally, we move to problems with structured state space (slow and fast state), build a hierarchical model which exploits the structure, and propose ADP algorithms for the hierarchical model.

Solving Markov decision processes (MDPs) efficiently is challenging in many cases, for example, when the state space or action space is large, when the reward function is sparse and delayed, and when there is a distribution of MDPs. Structures in the policy, value function, reward function, or state space can be useful in accelerating the learning process. In this thesis, we exploit structures in MDPs to solve them effectively and efficiently. First, we study problems with concave value function and basestock policy and leverage these two structures to propose an approximate dynamic programming (ADP) algorithm. Next, we study the exploration problem in unknown MDPs, introduce structured intrinsic reward to the problem, and propose a Bayes-optimal algorithm for learning the intrinsic reward. Finally, we move to problems with structured state space (slow and fast state), build a hierarchical model which exploits the structure, and propose ADP algorithms for the hierarchical model.

Jiankai Mao,1,* Chaoyue Wang,2,3,* Chao Teng,4,* Mengpu Wang,2,3 Siyao Zhou,2 Ke Zhao,2 Xiangming Ye,5 Yijia Wang6 1Tongde Hospital of Zhejiang Province, Hangzhou, People’s Republic of China; 2Department of Psychiatry, School of Mental Health, Wenzhou Medical University, Wenzhou, People’s Republic of China; 3The Affiliated Kangning Hospital of Wenzhou Medical University, Wenzhou, People’s Republic of China; 4The 4th Clinical College of Zhejiang Chinese Medicine University, Hangzhou, People’s Republic of China; 5Zhejiang Provincial People’s Hospital, Hangzhou, People’s Republic of China; 6Wenzhou Seventh People’s Hospital, Wenzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiangming Ye; Yijia Wang, Email yexmdr@126.com; wyj202188@126.comIntroduction: The COVID-19 is a highly contagious disease belonging to the family of coronaviruses which can affect a great deal of people shortly. As a devastating event in the world, many people suffer the PTSD from this severe disease. The aim of study is to explore the prevalence and severity of post-traumatic stress disorder (PTSD) symptoms, and discuss the possible factors among the general public in China after the COVID-19 epidemic outbreak.Methods: This is a cross-sectional study. We used the self-designed demographic questionnaire and the Posttraumatic Stress Checklist-Civilian Version (PCL-C) of Chinese version as our screening tools to investigate 4872 subjects living in the communities in China from Nov. 8th, 2020 to Dec. 8th, 2020.Results: Finally, we received 3705 effective respondents. The response rates of the questionnaire can reach 76.97 percentage. Peculiarly, the prevalence of PTSD mild and severe symptoms after the COVID-19 epidemic outbreak was 53.2%. Mild PTSD symptoms accounted for 24.9%, and severe PTSD symptoms (scores above 38) accounted for 28.3%. PCL-C mean scores were 41.4± 14.7. The females accounted for 67.7

Luminogens with aggregation-induced emission (AIEgens) have been widely applied in the field of photodynamic therapy. Among them, aggregation-induced emission photosensitizers (AIE-PSs) are demonstrated with high capability in fluorescence and photoacoustic bimodal imaging, as well as in fluorescence imaging-guided photodynamic therapy. They not only improve diagnosis accuracy but also provide an efficient theranostic platform to accelerate preclinical translation as well. In this short review, we divide AIE-PSs into three categories. Through the analysis of such classification and construction methods, it will be helpful for scientists to further develop various types of AIE-PSs with superior performance.

Luminogens with aggregation-induced emission (AIEgens) have been widely applied in the field of photodynamic therapy. Among them, aggregation-induced emission photosensitizers (AIE-PSs) are demonstrated with high capability in fluorescence and photoacoustic bimodal imaging, as well as in fluorescence imaging-guided photodynamic therapy. They not only improve diagnosis accuracy but also provide an efficient theranostic platform to accelerate preclinical translation as well. In this short review, we divide AIE-PSs into three categories. Through the analysis of such classification and construction methods, it will be helpful for scientists to further develop various types of AIE-PSs with superior performance.

Luminogens with aggregation-induced emission (AIEgens) have been widely applied in the field of photodynamic therapy. Among them, aggregation-induced emission photosensitizers (AIE-PSs) are demonstrated with high capability in fluorescence and photoacoustic bimodal imaging, as well as in fluorescence imaging-guided photodynamic therapy. They not only improve diagnosis accuracy but also provide an efficient theranostic platform to accelerate preclinical translation as well. In this short review, we divide AIE-PSs into three categories. Through the analysis of such classification and construction methods, it will be helpful for scientists to further develop various types of AIE-PSs with superior performance.

Deep learning models in large-scale machine learning systems are often continuously trained with enormous data from production environments. The sheer volume of streaming training data poses a significant challenge to real-time training subsystems and ad-hoc sampling is the standard practice. Our key insight is that these deployed ML systems continuously perform forward passes on data instances during inference, but ad-hoc sampling does not take advantage of this substantial computational effort. Therefore, we propose to record a constant amount of information per instance from these forward passes. The extra information measurably improves the selection of which data instances should participate in forward and backward passes. A novel optimization framework is proposed to analyze this problem and we provide an efficient approximation algorithm under the framework of Mini-batch gradient descent as a practical solution. We also demonstrate the effectiveness of our framework and algorithm on several large-scale classification and regression tasks, when compared with competitive baselines widely used in industry., Comment: 13 pages

Sugars are abundant natural sources existing in biological systems, and bioactive saccharides have attracted much more attention in the field of biochemistry and biomaterials. For better understanding of the sugar-based biomaterials and biological sciences, aggregation-induced emission luminogens (AIE-gens) have been widely employed for detection, tracing, and imaging. This review covers the applications of AIE molecules on sugar-based biomaterials by three parts, polysaccharide, oligosaccharide, and monosaccharide, mainly focusing on saccharide detection, stimuli response materials preparation, bioimaging, and study of the AIE mechanism. These excellent works suggest the promising future of the sugar-based AIE bioconjugates, considering that the naturally designed and elaborately functionalized saccharides play discriminate roles in biological processes and AIE-tagged species may work as an indicator in each case. However, there are a lot of sugar-based biological species that have not been touched, such as mucopolysaccharides and glycoproteins on the cell surface and in the cell plasma. Based on these features, we enthusiastically look forward to more glorious developments in this bright research area. © 2020 American Chemical Society.

Sugars are abundant natural sources existing in biological systems, and bioactive saccharides have attracted much more attention in the field of biochemistry and biomaterials. For better understanding of the sugar-based biomaterials and biological sciences, aggregation-induced emission luminogens (AIE-gens) have been widely employed for detection, tracing, and imaging. This review covers the applications of AIE molecules on sugar-based biomaterials by three parts, polysaccharide, oligosaccharide, and monosaccharide, mainly focusing on saccharide detection, stimuli response materials preparation, bioimaging, and study of the AIE mechanism. These excellent works suggest the promising future of the sugar-based AIE bioconjugates, considering that the naturally designed and elaborately functionalized saccharides play discriminate roles in biological processes and AIE-tagged species may work as an indicator in each case. However, there are a lot of sugar-based biological species that have not been touched, such as mucopolysaccharides and glycoproteins on the cell surface and in the cell plasma. Based on these features, we enthusiastically look forward to more glorious developments in this bright research area. © 2020 American Chemical Society.

Sugars are abundant natural sources existing in biological systems, and bioactive saccharides have attracted much more attention in the field of biochemistry and biomaterials. For better understanding of the sugar-based biomaterials and biological sciences, aggregation-induced emission luminogens (AIE-gens) have been widely employed for detection, tracing, and imaging. This review covers the applications of AIE molecules on sugar-based biomaterials by three parts, polysaccharide, oligosaccharide, and monosaccharide, mainly focusing on saccharide detection, stimuli response materials preparation, bioimaging, and study of the AIE mechanism. These excellent works suggest the promising future of the sugar-based AIE bioconjugates, considering that the naturally designed and elaborately functionalized saccharides play discriminate roles in biological processes and AIE-tagged species may work as an indicator in each case. However, there are a lot of sugar-based biological species that have not been touched, such as mucopolysaccharides and glycoproteins on the cell surface and in the cell plasma. Based on these features, we enthusiastically look forward to more glorious developments in this bright research area. © 2020 American Chemical Society.

We study the problem of learning the objective functions or constraints of a multiobjective decision making model, based on a set of sequentially arrived decisions. In particular, these decisions might not be exact and possibly carry measurement noise or are generated with the bounded rationality of decision makers. In this paper, we propose a general online learning framework to deal with this learning problem using inverse multiobjective optimization. More precisely, we develop two online learning algorithms with implicit update rules which can handle noisy data. Numerical results show that both algorithms can learn the parameters with great accuracy and are robust to noise., Comment: 17 pages

Reinforcement learning in sparse-reward navigation environments with expensive and limited interactions is challenging and poses a need for effective exploration. Motivated by complex navigation tasks that require real-world training (when cheap simulators are not available), we consider an agent that faces an unknown distribution of environments and must decide on an exploration strategy. It may leverage a series of training environments to improve its policy before it is evaluated in a test environment drawn from the same environment distribution. Most existing approaches focus on fixed exploration strategies, while the few that view exploration as a meta-optimization problem tend to ignore the need for cost-efficient exploration. We propose a cost-aware Bayesian optimization approach that efficiently searches over a class of dynamic subgoal-based exploration strategies. The algorithm adjusts a variety of levers -- the locations of the subgoals, the length of each episode, and the number of replications per trial -- in order to overcome the challenges of sparse rewards, expensive interactions, and noise. An experimental evaluation demonstrates that the new approach outperforms existing baselines across a number of problem domains. We also provide a theoretical foundation and prove that the method asymptotically identifies a near-optimal subgoal design.

Knowing the reflection of game theory and ethics, we develop a mathematical representation to bridge the gap between the concepts in moral philosophy (e.g., Kantian and Utilitarian) and AI ethics industry technology standard (e.g., IEEE P7000 standard series for Ethical AI). As an application, we demonstrate how human value can be obtained from the experimental game theory (e.g., trust game experiment) so as to build an ethical AI. Moreover, an approach to test the ethics (rightness or wrongness) of a given AI algorithm by using an iterated Prisoner's Dilemma Game experiment is discussed as an example. Compared with existing mathematical frameworks and testing method on AI ethics technology, the advantages of the proposed approach are analyzed., Comment: 6 pages, 8 figures

A box-like macrocycle based on 1,4-bis(4-pyridylethynyl)benzene was derived in high yield. The macrocyclic fluorogen shows unique aggregation-induced emission properties. © The Royal Society of Chemistry 2016.

A box-like macrocycle based on 1,4-bis(4-pyridylethynyl)benzene was derived in high yield. The macrocyclic fluorogen shows unique aggregation-induced emission properties. © The Royal Society of Chemistry 2016.

A box-like macrocycle based on 1,4-bis(4-pyridylethynyl)benzene was derived in high yield. The macrocyclic fluorogen shows unique aggregation-induced emission properties.

The photophysical properties of a diphenylacetylene derivative (FOEB) were investigated. The fluorescence quantum efficiency (φF, %) of FOEB changes from moderate (49%) to low (9%) and to evidently enhanced (60%) in dilute solution, amorphous solid, and crystal state, respectively. This is a typical phenomenon of crystallization-induced emission enhancement (CIEE). The diphenylacetylene luminogen can be viewed as a propeller-like molecule with two blades (phenyl groups). In solution, the intramolecular rotations of the two blades around the ethynyl unit are activated and partially dissipate the energy in the photoexcited state, thereby leading to emission quenching. Consequently, FOEB exhibits moderate φF in solution. In amorphous solid, the intramolecular rotations are restricted, and a higher φF could be expected. But in fact, the strong π-π interactions between the luminogens cause heavier emission quenching, and the φF of the molecular aggregates is as low as 9%. In the single crystal, as the crystallographic data revealed, the two phenyl groups in the same diphenylacetylene unit are highly coplanar and the conjugation planes of the two adjacent diphenylacetylene units are orthogonal to each other. Such molecular conformation and chromophore packing eliminate the intermolecular π-π interaction thereby greatly reducing the π-π interaction that caused fluorescence quenching, and meanwhile, the orthogonal packing allows the formation of C-H···π bonding between the adjacent phenyl groups to lock the intramolecular rotations and retain fluorescence. As a result, the FOEB single crystal shows evidently enhanced emission. Besides the CIEE property, FOEB demonstrates good liquid crystal property due to the diphenylacetylene mesogen and solvatochromic and mechnochromic effects due to the modification of diphenylacetylene with introduction of electron donor and acceptor functionalities. © 2015 American Chemical Society.

A tetraphenylethene (TPE) derivative substituted with the electron-acceptor 1,3-indandione (IND) group was designed and prepared. The targeted IND-TPE reserves the intrinsic aggregation-induced emission (AIE) property of the TPE moiety. Meanwhile, owing to the decorated IND moiety, IND-TPE demonstrates intramolecular charge-transfer process and pronounced solvatochromic behavior. When the solvent is changed from apolar toluene to highly polar acetonitrile, the emission peak redshifts from 543 to 597nm. IND-TPE solid samples show an evident mechanochromic process. Grinding of the as-prepared powder sample induces a redshift of emission from green (peak at 515nm) to orange (peak at 570nm). The mechanochromic process is reversible in multiple grinding-thermal annealing and grinding-solvent-fuming cycles, and the emission of the solid sample switches between orange (ground) and yellow (thermal/solvent-fuming-treated) colors. The mechanochromism is ascribed to the phase transition between amorphous and crystalline states. IND-TPE undergoes a hydrolysis reaction in basic aqueous solution, thus the red-orange emission can be quenched by OH- or other species that can induce the generation of sufficient OH-. Accordingly, IND-TPE has been used to discriminatively detect arginine and lysine from other amino acids, due to their basic nature. The experimental data are satisfactory. Moreover, the hydrolyzation product of IND-TPE is weakly emissive in the resultant mixture but becomes highly blue-emissive after the illumination for a period by UV light. Thus IND-TPE can be used as a dual-responsive fluorescent probe, which may extend the application of TPE-based molecular probes in chemical and biological categories.

A tetraphenylethene (TPE) derivative substituted with the electron-acceptor 1,3-indandione (IND) group was designed and prepared. The targeted IND-TPE reserves the intrinsic aggregation-induced emission (AIE) property of the TPE moiety. Meanwhile, owing to the decorated IND moiety, IND-TPE demonstrates intramolecular charge-transfer process and pronounced solvatochromic behavior. When the solvent is changed from apolar toluene to highly polar acetonitrile, the emission peak redshifts from 543 to 597nm. IND-TPE solid samples show an evident mechanochromic process. Grinding of the as-prepared powder sample induces a redshift of emission from green (peak at 515nm) to orange (peak at 570nm). The mechanochromic process is reversible in multiple grinding-thermal annealing and grinding-solvent-fuming cycles, and the emission of the solid sample switches between orange (ground) and yellow (thermal/solvent-fuming-treated) colors. The mechanochromism is ascribed to the phase transition between amorphous and crystalline states. IND-TPE undergoes a hydrolysis reaction in basic aqueous solution, thus the red-orange emission can be quenched by OH- or other species that can induce the generation of sufficient OH-. Accordingly, IND-TPE has been used to discriminatively detect arginine and lysine from other amino acids, due to their basic nature. The experimental data are satisfactory. Moreover, the hydrolyzation product of IND-TPE is weakly emissive in the resultant mixture but becomes highly blue-emissive after the illumination for a period by UV light. Thus IND-TPE can be used as a dual-responsive fluorescent probe, which may extend the application of TPE-based molecular probes in chemical and biological categories.

A tetraphenylethene (TPE) derivative substituted with the electron-acceptor 1,3-indandione (IND) group was designed and prepared. The targeted IND-TPE reserves the intrinsic aggregation-induced emission (AIE) property of the TPE moiety. Meanwhile, owing to the decorated IND moiety, IND-TPE demonstrates intramolecular charge-transfer process and pronounced solvatochromic behavior. When the solvent is changed from apolar toluene to highly polar acetonitrile, the emission peak redshifts from 543 to 597nm. IND-TPE solid samples show an evident mechanochromic process. Grinding of the as-prepared powder sample induces a redshift of emission from green (peak at 515nm) to orange (peak at 570nm). The mechanochromic process is reversible in multiple grinding-thermal annealing and grinding-solvent-fuming cycles, and the emission of the solid sample switches between orange (ground) and yellow (thermal/solvent-fuming-treated) colors. The mechanochromism is ascribed to the phase transition between amorphous and crystalline states. IND-TPE undergoes a hydrolysis reaction in basic aqueous solution, thus the red-orange emission can be quenched by OH- or other species that can induce the generation of sufficient OH-. Accordingly, IND-TPE has been used to discriminatively detect arginine and lysine from other amino acids, due to their basic nature. The experimental data are satisfactory. Moreover, the hydrolyzation product of IND-TPE is weakly emissive in the resultant mixture but becomes highly blue-emissive after the illumination for a period by UV light. Thus IND-TPE can be used as a dual-responsive fluorescent probe, which may extend the application of TPE-based molecular probes in chemical and biological categories.

We demonstrate a concept-proof work of using fluorescence (FL) "turn-on" probes for the discriminatory detection of cysteine (Cys) over homocysteine (Hcy). The fluorogens are provided with aggregation-induced emission (AIE) characteristic and functionalized with two aldehyde-groups (DMTPS-ALD and TPE-ALD). All the detections were carried out in a biocompatible medium (10 mm HEPES buffer and DMSO, pH 7.4). In principle, the formation of thiazinane/thiazolidine through the chemical reaction of aldehydes on the probe molecules and the residue of Cys/Hcy determines the selective recognition of Cys and Hcy over other amino acids and glucose. The FL responses originate from the AIE property of thiazinane/thiazolidine resultants, which have low solubility and precipitate (aggregate) in the detection medium. The discrimination between Cys and Hcy comes from the difference in reaction kinetics of TPE-ALD/DMTPS-ALD with Cys and Hcy, thereby the FL responses show different time courses and intensity enhancement. It is worth noting that TPE-ALD outshined the other two probes in performance with fast response, a high FL enhancement up to 16-fold, high sensitivity, and good specificity and selectivity. Moreover, its FL response threshold at 250 mu m is very close to the lower limit of the normal level of Cys in human plasma, which implies that TPE-ALD could be applied as a potential indicator of Cys deficiency.

We demonstrate a concept-proof work of using fluorescence (FL) "turn-on" probes for the discriminatory detection of cysteine (Cys) over homocysteine (Hcy). The fluorogens are provided with aggregation-induced emission (AIE) characteristic and functionalized with two aldehyde-groups (DMTPS-ALD and TPE-ALD). All the detections were carried out in a biocompatible medium (10 mm HEPES buffer and DMSO, pH 7.4). In principle, the formation of thiazinane/thiazolidine through the chemical reaction of aldehydes on the probe molecules and the residue of Cys/Hcy determines the selective recognition of Cys and Hcy over other amino acids and glucose. The FL responses originate from the AIE property of thiazinane/thiazolidine resultants, which have low solubility and precipitate (aggregate) in the detection medium. The discrimination between Cys and Hcy comes from the difference in reaction kinetics of TPE-ALD/DMTPS-ALD with Cys and Hcy, thereby the FL responses show different time courses and intensity enhancement. It is worth noting that TPE-ALD outshined the other two probes in performance with fast response, a high FL enhancement up to 16-fold, high sensitivity, and good specificity and selectivity. Moreover, its FL response threshold at 250 mu m is very close to the lower limit of the normal level of Cys in human plasma, which implies that TPE-ALD could be applied as a potential indicator of Cys deficiency.

We demonstrate a concept-proof work of using fluorescence (FL) "turn-on" probes for the discriminatory detection of cysteine (Cys) over homocysteine (Hcy). The fluorogens are provided with aggregation-induced emission (AIE) characteristic and functionalized with two aldehyde-groups (DMTPS-ALD and TPE-ALD). All the detections were carried out in a biocompatible medium (10 mm HEPES buffer and DMSO, pH 7.4). In principle, the formation of thiazinane/thiazolidine through the chemical reaction of aldehydes on the probe molecules and the residue of Cys/Hcy determines the selective recognition of Cys and Hcy over other amino acids and glucose. The FL responses originate from the AIE property of thiazinane/thiazolidine resultants, which have low solubility and precipitate (aggregate) in the detection medium. The discrimination between Cys and Hcy comes from the difference in reaction kinetics of TPE-ALD/DMTPS-ALD with Cys and Hcy, thereby the FL responses show different time courses and intensity enhancement. It is worth noting that TPE-ALD outshined the other two probes in performance with fast response, a high FL enhancement up to 16-fold, high sensitivity, and good specificity and selectivity. Moreover, its FL response threshold at 250 mu m is very close to the lower limit of the normal level of Cys in human plasma, which implies that TPE-ALD could be applied as a potential indicator of Cys deficiency.