Experimental study on water evaporation from sand using environmental chamber

International audienceLarge-scale evaporation experiments were conducted on bare sand using an environmental chamber. Four different atmospheric conditions and various drying durations were imposed to soil sample. Both the atmospheric parameters (air flow rate, relative humidity and temperature) and the response of soil (volumetric water content, temperature and soil suction) were monitored simultaneously. Notably, the temperature and matric suction at soil surface were monitored using infrared thermometer and high-capacity tensiometer, respectively. The results show that the air and soil temperatures depend on the evaporation process and atmospheric conditions. In addition, volumetric water content in the near-surface zone is strongly affected by the evaporation process and changes linearly over depth. The evaporation rate is strongly dependent on the air conditions

Deficiency in Silicon Transporter Lsi1 Compromises Inducibility of Anti-herbivore Defense in Rice Plants

Silicon (Si) application can significantly enhance rice resistance against herbivorous insects. However, the underlying mechanism is elusive. In this study, silicon transporter mutant OsLsi1 and corresponding wild-type rice (WT) were treated with and without Si to determine Si effects on rice resistance to leaffolder (LF), Cnaphalocrocis medinalis (Guenée) (Lepidoptera: Pyralidae). Si application on WT plants significantly promoted rice plant growth, upregulated expression level of OsLsi1 and increased Si accumulation in the leaves and roots, as well as effectively reduced LF weight gain, while it showed only marginal or no effect on the mutant plants. Furthermore, upon LF infestation, transcript levels of OsLOX, OsAOS2, OsCOI1a, OsCOI1b, and OsBBPI, and activity of catalase, superoxide dismutase, peroxidase, and polyphenol oxidase were significantly higher in Si-treated than untreated WT plants. However, OsLsi1 mutant plants displayed higher susceptibility to LF, and minimal response of defense-related enzymes and jasmonate dependent genes to Si application. These results suggest that induced defense plays a vital role in Si-enhanced resistance and deficiency in silicon transporter Lsi1 compromises inducibility of anti-herbivore defense in rice plants

Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-Verification

Recent progress in large language models (LLMs) like GPT-4 and PaLM-2 has brought significant advancements in addressing math reasoning problems. In particular, OpenAI's latest version of GPT-4, known as GPT-4 Code Interpreter, shows remarkable performance on challenging math datasets. In this paper, we explore the effect of code on enhancing LLMs' reasoning capability by introducing different constraints on the \textit{Code Usage Frequency} of GPT-4 Code Interpreter. We found that its success can be largely attributed to its powerful skills in generating and executing code, evaluating the output of code execution, and rectifying its solution when receiving unreasonable outputs. Based on this insight, we propose a novel and effective prompting method, explicit \uline{c}ode-based \uline{s}elf-\uline{v}erification~(CSV), to further boost the mathematical reasoning potential of GPT-4 Code Interpreter. This method employs a zero-shot prompt on GPT-4 Code Interpreter to encourage it to use code to self-verify its answers. In instances where the verification state registers as ``False'', the model shall automatically amend its solution, analogous to our approach of rectifying errors during a mathematics examination. Furthermore, we recognize that the states of the verification result indicate the confidence of a solution, which can improve the effectiveness of majority voting. With GPT-4 Code Interpreter and CSV, we achieve an impressive zero-shot accuracy on MATH dataset \textbf{(53.9\% $\to$ 84.3\%)}.Comment: Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-Verificatio

Deep quantum neural networks equipped with backpropagation on a superconducting processor

Deep learning and quantum computing have achieved dramatic progresses in recent years. The interplay between these two fast-growing fields gives rise to a new research frontier of quantum machine learning. In this work, we report the first experimental demonstration of training deep quantum neural networks via the backpropagation algorithm with a six-qubit programmable superconducting processor. In particular, we show that three-layer deep quantum neural networks can be trained efficiently to learn two-qubit quantum channels with a mean fidelity up to 96.0% and the ground state energy of molecular hydrogen with an accuracy up to 93.3% compared to the theoretical value. In addition, six-layer deep quantum neural networks can be trained in a similar fashion to achieve a mean fidelity up to 94.8% for learning single-qubit quantum channels. Our experimental results explicitly showcase the advantages of deep quantum neural networks, including quantum analogue of the backpropagation algorithm and less stringent coherence-time requirement for their constituting physical qubits, thus providing a valuable guide for quantum machine learning applications with both near-term and future quantum devices.Comment: 7 pages (main text) + 11 pages (Supplementary Information), 10 figure

Experimental quantum adversarial learning with programmable superconducting qubits

Quantum computing promises to enhance machine learning and artificial intelligence. Different quantum algorithms have been proposed to improve a wide spectrum of machine learning tasks. Yet, recent theoretical works show that, similar to traditional classifiers based on deep classical neural networks, quantum classifiers would suffer from the vulnerability problem: adding tiny carefully-crafted perturbations to the legitimate original data samples would facilitate incorrect predictions at a notably high confidence level. This will pose serious problems for future quantum machine learning applications in safety and security-critical scenarios. Here, we report the first experimental demonstration of quantum adversarial learning with programmable superconducting qubits. We train quantum classifiers, which are built upon variational quantum circuits consisting of ten transmon qubits featuring average lifetimes of 150 $\mu$s, and average fidelities of simultaneous single- and two-qubit gates above 99.94% and 99.4% respectively, with both real-life images (e.g., medical magnetic resonance imaging scans) and quantum data. We demonstrate that these well-trained classifiers (with testing accuracy up to 99%) can be practically deceived by small adversarial perturbations, whereas an adversarial training process would significantly enhance their robustness to such perturbations. Our results reveal experimentally a crucial vulnerability aspect of quantum learning systems under adversarial scenarios and demonstrate an effective defense strategy against adversarial attacks, which provide a valuable guide for quantum artificial intelligence applications with both near-term and future quantum devices.Comment: 26 pages, 17 figures, 8 algorithm

A comparative study on the three calculation methods for reproduction numbers of COVID-19

ObjectiveThis study uses four COVID-19 outbreaks as examples to calculate and compare merits and demerits, as well as applicational scenarios, of three methods for calculating reproduction numbers.MethodThe epidemiological characteristics of the COVID-19 outbreaks are described. Through the definition method, the next-generation matrix-based method, and the epidemic curve and serial interval (SI)-based method, corresponding reproduction numbers were obtained and compared.ResultsReproduction numbers (Reff), obtained by the definition method of the four regions, are 1.20, 1.14, 1.66, and 1.12. Through the next generation matrix method, in region H Reff = 4.30, 0.44; region P Reff = 6.5, 1.39, 0; region X Reff = 6.82, 1.39, 0; and region Z Reff = 2.99, 0.65. Time-varying reproduction numbers (Rt), which are attained by SI of onset dates, are decreasing with time. Region H reached its highest Rt = 2.8 on July 29 and decreased to Rt < 1 after August 4; region P reached its highest Rt = 5.8 on September 9 and dropped to Rt < 1 by September 14; region X had a fluctuation in the Rt and Rt < 1 after September 22; Rt in region Z reached a maximum of 1.8 on September 15 and decreased continuously to Rt < 1 on September 19.ConclusionThe reproduction number obtained by the definition method is optimal in the early stage of epidemics with a small number of cases that have clear transmission chains to predict the trend of epidemics accurately. The effective reproduction number Reff, calculated by the next generation matrix, could assess the scale of the epidemic and be used to evaluate the effectiveness of prevention and control measures used in epidemics with a large number of cases. Time-varying reproduction number Rt, obtained via epidemic curve and SI, can give a clear picture of the change in transmissibility over time, but the conditions of use are more rigorous, requiring a greater sample size and clear transmission chains to perform the calculation. The rational use of the three methods for reproduction numbers plays a role in the further study of the transmissibility of COVID-19

Advances and Open Problems in Federated Learning

Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges

Advances and Open Problems in Federated Learning

Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges.Comment: Published in Foundations and Trends in Machine Learning Vol 4 Issue 1. See: https://www.nowpublishers.com/article/Details/MAL-08

Etude d’évaporation d’eau d’un sable et d’une argile à l’aide d’une chambre environnementale

As a well-known phenomenon, soil water evaporation plays an important role in the interaction between soil and atmosphere. Water evaporates during this process resulting in changes of soil thermo-hydro-mechanical behavior and in turn causing problems in different domains such as agronomy, hydrology, soil science, geotechnical engineering, etc. Therefore, it is essential to investigate the soil water evaporation mechanisms in depth.This study deals with the soil water evaporation mechanisms under controlled atmospheric conditions. The Fontainebleau sand and the Hércourt clay used for the construction of the experimental embankment with the ANR project TerDOUEST (Terrassements Durables - Ouvrages en Sols Traités, 2008 - 2012) were used in this investigation. A large-scale environmental chamber system (900 mm high, 800 mm large and 1000 mm long) equipped with various sensors was firstly developed, allowing a full monitoring of both atmospheric and soil parameters during the evaporation process. Four experimental tests were carried out on the Fontainebleau sand compacted at 1.70 Mg/m3 dry density with a steady water table at soil bottom under different atmospheric conditions (different values of air relative humidity, temperature and air flow rate). The performance of the environmental chamber system in investigating soil water evaporation was evidenced by the quality and the relevance of results. The air temperature inside the chamber was found to be affected by the heating tube temperature, the air flow rate and the soil water evaporation process; the soil temperature was strongly affected by the air conditions and the evaporation progress; the relative humidity in the chamber was decreasing during the evaporation progress and its evolution could be considered as an indicator of the evaporation progress; the volumetric water content in the near-surface zone was strongly affected by the evaporation process and exhibited a linear relationship with depth; the soil suction was decreasing over depth and increasing over time; the evaporation rate was strongly affected by the air conditions especially at the initial constant evaporation rate stage. After the tests on the Fontainebleau sand, the Hércourt clay sample compacted at 1.40 Mg/m3 dry density was subjected to an infiltration experiment for investigating its hydraulic properties. To get a better insight into the water evaporation mechanism for clay, two compacted Hércourt clay evaporation tests with a steady water table at bottom were carried out under controlled atmospheric conditions. The results allow understanding the evaporation mechanisms in case of desiccation cracks. Furthermore, in order to investigate the potential evaporation mechanisms, tests with a free water layer was also conducted with varying wind speed and air temperature. The initiation and propagation of desiccation cracking during the evaporation process and its effect on water evaporation were also investigated by the digital image processing technique. In terms of modeling, the potential evaporation rate was first modeled through evaluation of the existing models and the combined models. It reveals that the model developed by Ta (2009) is the most appropriate one. The actual evaporation rate for sand was then analyzed. It appears important to consider the progress of the dry front during the evaporation process for sandy soils. For the Héricourt clay, good simulation was also obtained using a model that accounts for the effect of desiccations cracksIl est bien connu que l'évaporation d'eau joue un rôle essentiel dans l'interaction entre le sol et l'atmosphère. Pendant le processus d'évaporation, le comportement thermo-hydro-mécanique des sols change, engendrant ainsi des problèmes préoccupants. Ceci peut concerner différents domaines comme l'agronomie, l'hydrologie, la science des sols, la géotechnique, etc. Par conséquent, il est essentiel d'étudier les mécanismes d'évaporation de façon approfondie. Cette étude porte sur les mécanismes d'évaporation dans des conditions atmosphériques contrôlées. Le sable de Fontainebleau et l'argile d'Hércourt utilisée pour la construction du remblai expérimental dans le cadre du projet ANR TerDOUEST (Terrassements Durables - Ouvrages en Sols Traités, 2008-2012) ont été étudiés à cet effet. Une chambre environnementale (900 mm de haut, 800 mm de large et 1000 mm de long) équipée de différents capteurs a d'abord été développée, permettant un suivi complet des paramètres concernant l'atmosphère et le sol au cours d'évaporation. Quatre essais expérimentaux ont été réalisés sur le sable de Fontainebleau compacté à une densité sèche de 1,70 Mg/m3, avec une nappe phréatique constante au fond de l'échantillon, et sous différentes conditions atmosphériques (différentes valeurs de l'humidité relative de l'air, de la température et du débit d'air). La pertinence du système a été mise en évidence par la bonne qualité des résultats. La température de l'air à l'intérieur de la chambre a été trouvée affectée par la température du tube de chauffage, le débit d'air et l'évaporation d'eau; la température du sol est fortement affectée par les conditions atmosphériques et l'état d'avancement de l'évaporation ; l'humidité relative dans la chambre diminue au cours du temps et son évolution peut être considérée comme un indicateur du processus d' évaporation ; la teneur en eau volumique dans la zone proche de la surface est fortement influencée par le processus d'évaporation et présente une relation linéaire avec la profondeur ; la succion du sol diminue avec la profondeur et augmente au fil du temps ; le taux d'évaporation est fortement affecté par les conditions de l'air en particulier dans la phase initiale de vitesse d'évaporation constante. Après les essais sur le sable de Fontainebleau, l'échantillon de l'argile d'Hércourt compactée à une densité sèche de 1,40 Mg/m3 a été soumis à une infiltration d'eau afin d'étudier ses propriétés hydrauliques. Pour obtenir un meilleur aperçu du mécanisme d'évaporation pour l'argile, deux essais d'évaporation sur l'argile d'Hércourt compactée avec une nappe phréatique constante au fond de l'échantillon ont été effectuées sous des conditions atmosphériques contrôlées. Les résultats permettent de comprendre les mécanismes d'évaporation en cas de fissuration due à la dessiccation. En outre, afin d'étudier les mécanismes d'évaporation potentiels, des essais avec une couche d'eau libre ont été également réalisés en faisant varier la vitesse du vent et la température de l'air. L'initiation et la propagation de fissures de dessiccation pendant le processus d'évaporation et son effet sur l'évaporation ont également été étudiés par la technique de traitement d'image. En termes de modélisation, le taux d'évaporation potentiel a été modélisé à travers l'évaluation des modèles existants et des modèles combinés. Il apparait que le modèle développé par Ta (2009) est le plus approprié. Le taux d'évaporation réelle depuis le sable a été ensuite analysé. Il semble important de considérer l'avancement du front sec pendant le processus d'évaporation pour les sols sableux. Pour l'argile d'Héricourt, une bonne prévision a été également obtenue en utilisant un modèle qui tient compte de l'effet des fissures de dessiccatio

Modification of Lime-Fly Ash-Crushed Stone with Phosphogypsum for Road Base

In order to increase the recycling of phosphogypsum waste, this study explored the feasibility of using phosphogypsum to replace some of the lime and aggregate in the lime-fly ash-crushed stone mixture which is a widely used road base material in China. For this purpose, compaction, compressive strength, composition structures, wetting-drying cycle tests, and shrinkage tests were carried out on the lime-fly ash-phosphogypsum-crushed stone composite to investigate its performance. The results indicate that lime-fly ash-crushed stone modified with phosphogypsum has the required strength of the road base material and favourable performances in environment (wetting-drying cycle) stability. The image processing analysis and shrinkage tests demonstrated that phosphogypsum can significantly improve the compactness and shrinkage performance of lime-fly ash-crushed stone mixture. A suitable content of phosphogypsum and a reasonable content of fine aggregate are conducive to improving the roadway engineering properties (i.e., decreasing shrinkage cracks and increasing compressive strength) of lime-fly ash-phosphogypsum-crushed stone composites