Negative differential resistance (NDR) refers to a unique electrical property where current decreases with increasing voltage. Herein, we report experimental evidence showing that the NDR effect can be observed in mesopores that feature charged pore walls and are subjected to a KCl concentration gradient. NDR in our system originates from the solution and ion flows driven by the synergistic effects of electroosmosis [electroosmotic flow (EOF)] and diffusioosmosis, the so-called electrodiffusioosmosis. Experiments reveal that in addition to the ion current rectification, the mesopores considered here exhibit the NDR phenomenon that is dependent on the magnitude and direction of the salinity gradient and on pH. The NDR behavior can be observed only at conditions at which the EOF and diffusioosmosis occur in the opposite directions: diffusioosmosis fills the tip opening with a high concentration solution, while EOF brings a low concentration solution to the pore. All experimental findings are supported by our numerical model, which takes into account the interfacial site reactions of acidic and basic functional groups on the entire pore membrane surfaces. Our results provide an important insight into how liquid pH, salinity gradients, interfacial site reactions, and pore geometries can influence the current−voltage characteristics of mesopores, enriching transport modes that can be induced by voltage.

Learning with a primary objective, such as softmax cross entropy for classification and sequence generation, has been the norm for training deep neural networks for years. Although being a widely-adopted approach, using cross entropy as the primary objective exploits mostly the information from the ground-truth class for maximizing data likelihood, and largely ignores information from the complement (incorrect) classes. We argue that, in addition to the primary objective, training also using a complement objective that leverages information from the complement classes can be effective in improving model performance. This motivates us to study a new training paradigm that maximizes the likelihood of the groundtruth class while neutralizing the probabilities of the complement classes. We conduct extensive experiments on multiple tasks ranging from computer vision to natural language understanding. The experimental results confirm that, compared to the conventional training with just one primary objective, training also with the complement objective further improves the performance of the state-of-the-art models across all tasks. In addition to the accuracy improvement, we also show that models trained with both primary and complement objectives are more robust to single-step adversarial attacks.

Despite recent success in neural task-oriented dialogue systems, developing such a realworld system involves accessing large-scale knowledge bases (KBs), which cannot be simply encoded by neural approaches, such as memory network mechanisms. To alleviate the above problem, we propose AirConcierge, an end-to-end trainable text-to-SQL guided framework to learn a neural agent that interacts with KBs using the generated SQL queries. Specifically, the neural agent first learns to ask and confirm the customer's intent during the multi-turn interactions, then dynamically determining when to ground the user constraints into executable SQL queries so as to fetch relevant information from KBs. With the help of our method, the agent can use less but more accurate fetched results to generate useful responses efficiently, instead of incorporating the entire KBs. We evaluate the proposed method on the AirDialogue dataset, a large corpus released by Google, containing the conversations of customers booking flight tickets from the agent. The experimental results show that AirConcierge significantly improves over previous work in terms of accuracy and the BLEU score, which demonstrates not only the ability to achieve the given task but also the good quality of the generated dialogues.

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