Miracle-dz/UniFAR-FTUs huggingface dataset:
Peipei Jiao1,2 · Zhihua Wu3 · Xu Wang1 · Zhenbo Jiang1 · Yanqin Wang1,2 · Hong Liu3 · Rui Qin3 · Zhijun Li1,2

term ‘response to organic substance’ and KEGG pathway ‘nitrogen metabolism’ were enriched for drought-stressed leaves, while GO term ‘cell wall organization or biogenesis’ and KEGG pathway ‘zeatin biosynthesis’ were enriched for drought-stressed roots. The enrichment of the GO term ‘enzyme linked receptor protein signalling pathway’ in both leaf and root drought responses suggests that these tissues may also have similar mechanisms. However, roots under drought stress for four hs responded by activating programed cell death. The KEGG pathway ‘plant hormone signal transduction’ was detected for 4and 12-h droughtstressed leaves and 12-h drought-stressed roots, suggesting that plant hormone signal transduction plays an important role in both roots and leaves. GO enrichment of upregulated and downregulated genes for leaves and roots reflect differentially regulatory mechanisms of response to drought stress via different biological processes such as the regulation of photosynthesis and auxin signalling pathway in leaves, and the regulation of defence response and water homeostasis in roots. Fifteen candidate genes, including transcription factors, protein kinase, transporter, late embryogenesis abundant protein and mannitol dehydrogenase, were further selected to determine their response to drought using qRTPCR. The results show that the expression patterns of 13 of the 15 genes correspond to the RNA-seq results. This study provides new insights into the drought response mechanism of P. euphratica and suggests new candidate gene resources. Abstract Populus euphratica Oliv. , the Euphrates poplar, is the tallest tree species in the arid desert areas of Northwest China. Investigation of its drought-resistance genes is valuable to increase understanding of drought resistance mechanisms. RNA-seq of leaves and roots under drought simulation by 25% polyethylene glycol-6000 (PEG 6000) were performed at 0, 4, and 12 h. Leaves and roots responded differently to drought via differentially upregulated and downregulated genes; more genes were downregulated than upregulated in both leaves and roots. Additionally, these differentially expressed genes were enriched in different GO terms and KEGG pathways. For example, GO
Robustness of the Generalized Inverse Regression Estimator against an Outlier

要 旨 目的変 数yと 説 明変 数xと の間 に線形 回帰 モ デル が成 り立 つ場 か ら,n組 のデ ー タが独立 に収 集 され,さ らにxが 未 知 の水 準X0の も とでyに 関す る新 た な デー タがm個 収 集 された とす る. こ の とき,こ れ らの デー タか らX0を 推定 す る こ とは,回 帰 の逆推 定 問題 と呼 ばれ る. X0の 推 定量 と しては,一 般 化逆 回帰推 定量等 が あ る. 本 研 究 で は,新 た なm個 の デー タの 中 に外れ値 が1つ 存 在 す る場 合 をと り あげ,外 れ値 が この推定 量 に与 え る影 響 につ いて の考 察 を行 う. ま ず,外 れ値 が一 般化逆 回帰 推定 量 の期待 値,MSEに 与 え る影 響 に つい て漸 近 的 に考 察 を行 う. そ し て,外 れ値 の存在 に よるMSEの 増加 を避 ける ため に,実 際 的 な観 点 か らは平均 の外 れの影 響 を考慮 す る必要 が あ る ことを示す. こ の 結果 を踏 まえ,従 来 提 案 されて い る推 定量 に,メ ジア ン を代 入 した一般 化逆 回 帰推 定量,偏 りを修 正 した一 般 化逆 回 帰推 定量 を加 えて数値 的 に検 討 を行 う. 以 上 の結 果 を もとに考察 を行 い,mが3以 上 の場合 には,メ ジ アン を代 入 した一 般化 逆 回帰推 定 量 を用 いれ ば よい こ とを示 す.
Evaluation of nutritional risk in adult inpatients in the Hospital Regional Norte of

: The objective of this research was to evaluate the nutritional risk of adults at the Hospital Regional Norte in Sobral, Ceará, Brazil. The descriptive study was conducted from May to June 2018, in the emergency unit of the Hospital Regional. The sample was composed of 80 adult patients of both genders, aged between 18 and 59 years old, and admitted in the prior 48 hours. Two nutritional screening instruments were applied with
“Don’t Vax Up” The Real-Time Failure of Public STEM Education in the COVID-19 Era

Although many other factors come to bear on present issues in the contemporary world, vaccine hesitancy and refusal are also the direct result of poor STEM education. In this article we employ a sociological thought experiment methodology to articulate the shortcomings of STEM education and suggest pathways for much needed changes in solving future pandemics and other 21st century challenges. The problems we expose in STEM education include unequal access to high quality education via inter and intra school tracking and curricular issues where STEM does not integrate with other disciplines
Model-informed LIME Extension for Business Process Explainability

Our focus in this work is on the adaptation of eXplainable AI techniques for the interpretation of business process execution results. Such adaptation is required since conventional employment of such techniques involves a surrogate machine learning model that is trained on historical process execution logs. However, being a data-driven surrogate, its representation faithfulness of the real business process model affects the adequacy of the explanations derived from it. Hence, native use of such techniques is not ensured to be adhering to the target business process explained. We present a business-process-model-driven approach that extends LIME, a conventional machine-learning-model-agnostic eXplainable AI tool, to cope with business processes constraints that is replicable and reproducible. Our results show that our extended LIME approach produces correct and significantly more adequate explanations than the ones given by LIME as-is.
Privacy-Preserving Process Mining with PM4Py (Extended Abstract)

Process Mining allows for the data-driven analysis of business processes based on logs that contain fine-granular data from the process’ execution. However, such logs can potentially be exploited to extract sensitive information about process participants. To mitigate this risk, techniques that anonymize event logs to guarantee the privacy of process participants have recently been proposed. In this paper, we report on the integration of anonymization techniques for event logs into PM4Py , one of the leading process mining tools. Specifically, we incorporated several state-of-the-art solutions for differential privacy-based protection. By presenting the first integration of anonymization techniques into a general process mining toolkit, we make the respective algorithms accessible to the wider community of process mining experts and data scientists.
Minds, models and mechanisms: a new perspective on intentional psychology

In this article, I argue that intentional psychology (i.e. the interpretation of human behaviour in terms of intentional states and propositional attitudes) plays an essential role in the sciences of the mind. However, this role is not one of identifying scientifically respectable states of the world. Rather, I argue that intentional psychology acts as a type of phenomenological model, as opposed to a mechanistic one. I demonstrate that, like other phenomenological models in science, intentional psychology is a methodological tool with its own benefits and insights that complements our mechanistic understanding of systems. As a result, intentional psychology's distinctive scientific benefit is its ability to model systems in unique, non-mechanistic, ways. This allows us to generate predictions that we cannot otherwise generate using the mechanistic models of neuroscience and cognitive psychology necessary for various scientific tasks.
Dynamics of CO2 laser heating in the processing of silicon

The optical transmission of commerical Si wafers was measured at the wavelength of 10.6 μm as a function of temperature in the interval from about 78 to 850 K. We show that the absorptivity of Si increases with the sample temperature. This temperature dependence is explained by free‐carrier absorption. A transmission measurement at 10.6 μm wavelength has been used to monitor the temperature of a Si sample as a function of time during cw laser heating.
Aeroelastic Modeling Using Geometrically Nonlinear Solid-Shell Elements

An aeroelastic model is developed by using multivariable solid-shell elements. Geometrical nonlinearity and the elimination of corresponding locking phenomena are considered accurately in the modeling. Unsteady aerodynamic forces are addressed by solving Euler flow dynamic equations. The computational-structural-dynamics solver is loosely coupled to the computational-fluid-dynamics solver, and the full match of the computational structural dynamics and computational fluid dynamics interfaces is achieved by three-dimensional modeling. An interpolation method using finite-element shape functions is developed to transform forces from aerodynamic grids to structural grids. The aerodynamic mesh deformation is obtained by using radial basis functions combined with the transfinite interpolation method. The aeroelastic model is validated through the study of the flutter and the limit cycle oscillation of a cropped delta wing in transonic flow. Moreover, the establishment of the aeroelastic model using multivariab.
Two color far infrared laser interferometer

Two color interferometer using a short wavelength far infrared laser has been developed for high performance plasmas on large helical device and for future fusion devices such as ITER. High power laser lines simultaneously oscillating at 57.2 and 47. 6μm were achieved in a CO2-laser-pumped CH3OD laser. By introducing Ge:Ga photoconductive detectors operating at liquid He temperature, we have successfully detected two color beat signals (0.55 and 1.2MHz) with excellent signal-to-noise ratio (∼40dB). These beat signals were fed into phase comparators for phase measurement after passing through intermediate frequency bandpass filters. Two color far infraned laser interferometer work was successful in the demonstration of mechanical vibration compensation.
Improving transit detection with collective light curves information

The search for planetary transits in star light-curves can be improved in an non standard way applying appropriate filtering of the systematic effects just after the detection step. The procedure has been tested using a set of light curves simulated in the context of the CoRoT space mission. The level of the continuum in the detection curves is significantly lowered when compared to other standard approaches, a property we use to reduce false alarm. Ambiguities may originate in unexpected effects that combine instrumental and environmental factors. In a large set of synchronous light curves collective behaviours permit to identify systematic effects against which the detected events are compared. We estimate a significance of our detections and show that with our procedure the number of true detections is increased by more than 80% (22 events detected over the 36 injected ones). In spite of its simplicity, our method scores quite well (average results) when compared to the other methods used for the CoRoT "blind test" exercice by Moutou et al.1
The circumstellar environment of TMR-1 - A young, low-mass star in the Taurus molecular ring

Near-infrared camera and Owens Valley millimeter interferometer data are presented that reveal the circumstellar environment of TMR-1 on scales of roughly 1000 AU. TMR-1 is a deeply embedded, young, low-mass star located in a rotating ring of material in the TMC-1/Heiles Cloud 2 region of the Taurus molecular cloud. The stellar wind cavity, as outlined by the extended infrared continuum emission at 2.2 microns, overlaps with the redshifted outflow detected in the (C-12)O data. High-density circumstellar material is seen in (C-13)O in emission and at 2.2 microns in absorption. The morphology suggests the high-density gas may either be part of a 1000 AU disk or may represent infalling material from the molecular cloud core. TMR-1 appears to be in an early wind-clearing phase of protostar formation. 25 refs.
A Self-Organizing Network for Principal-Component Analysis

We present a two-layered network of linear neurons that organizes itself in response to a set of presented patterns. After completion of the learning process, the net transforms the complete information contained in a pattern into mutually independent features. The synaptic weights between layers obey a Hebbian learning rule. We propose a local anti-Hebbian rule for lateral, hierarchically organized weights within the output layer. For a proper choice of the learning parameters, the rule forces the activities of the output units to becomes uncorrelated and the lateral weights to vanish. The weights between the two layers converge to the eigenvectors of the covariance matrix of input patterns, i.e. the network performs a principal-component analysis of the input information.
Barium Circular Atoms Prepared by the Crossed-Field Method

Circular two-electron atoms, i.e. Rydberg atoms where the angular momentum of one of the valence electrons is maximum, have been prepared in barium (n = 21) by using the adiabatic crossed-field method already tested in alkali atoms. This simple and versatile method proves to be very efficient for heavy atoms with large quantum defects. Circular alkaline-earth atoms represent a decisive step for the preparation of doubly-excited Rydberg states with long autoionization lifetimes.
Dispersive shocks in diffusive-dispersive approximations of elasticity and quantum-hydrodynamics

The aim is to assess the combined effect of diffusion and dispersion on shocks in the moderate dispersion regime. For a diffusive dispersive approximation of the equations of one-dimensional elasticity (or p-system), we study convergence of traveling waves to shocks. The problem is recast as a Hamiltonian system with small friction, and an analysis of the length of oscillations yields convergence in the moderate dispersion regime ε , δ → 0 \varepsilon , \delta \to 0 with δ = o ( ε ) \delta = o(\varepsilon ) , under hypotheses that the limiting shock is admissible according to the Liu E-condition and is not a contact discontinuity at either end state. A similar convergence result is proved for traveling waves of the quantum hydrodynamic system with artificial viscosity as well as for a viscous Peregrine-Boussinesq system where traveling waves model undular bores, in all cases in the moderate dispersion regime.
The role of author identities in peer review

There is widespread debate on whether to anonymize author identities in peer review. The key argument for anonymization is to mitigate bias, whereas arguments against anonymization posit various uses of author identities in the review process. The Innovations in Theoretical Computer Science (ITCS) 2023 conference adopted a middle ground by initially anonymizing the author identities from reviewers, revealing them after the reviewer had submitted their initial reviews, and allowing the reviewer to change their review subsequently. We present an analysis of the reviews pertaining to the identification and use of author identities. Our key findings are: (I) A majority of reviewers self-report not knowing and being unable to guess the authors’ identities for the papers they were reviewing. (II) After the initial submission of reviews, 7.1% of reviews changed their overall merit score and 3.8% changed their self-reported reviewer expertise. (III) There is a very weak and statistically insignificant correlation of the rank of authors’ affiliations with the change in overall merit; there is a weak but statistically significant correlation with respect to change in reviewer expertise. We also conducted an anonymous survey to obtain opinions from reviewers and authors. The main findings from the 200 survey responses are: (i) A vast majority of participants favor anonymizing author identities in some form. (ii) The “middle-ground” initiative of ITCS 2023 was appreciated. (iii) Detecting conflicts of interest is a challenge that needs to be addressed if author identities are anonymized. Overall, these findings support anonymization of author identities in some form (e.g., as was done in ITCS 2023), as long as there is a robust and efficient way to check conflicts of interest.
Autonomous Driving Simulator based on Neurorobotics Platform

— There are many artiﬁcial intelligence algorithms for autonomous driving in the present market, but directly installing these algorithms on vehicles is unrealistic and expensive. At the same time, many of these algorithms need an environment to train and optimize. Simulation is a valuable and meaningful solution with training and testing functions, and it can say that simulation is a critical link in the autonomous driving world. There are also many diﬀerent applications or systems of simulation from companies or academies such as SVL and Carla. These simulators ﬂaunt that they have the closest real-world simulation, but their environment objects, such as pedestrians and other vehicles around the agent-vehicle, are already ﬁxed programmed. They can only move along the pre-setting trajectory, or random numbers determine their movements. What is the situation when all environmental objects are also installed by Artiﬁcial Intelligence, or their behaviors are like real people or natural reactions of other drivers? This problem is a blind spot for most of the simulation applications, or these applications cannot be easy to solve this problem. The Neurorobotics Platform from the TUM team of Prof. Alois Knoll has the idea about "Engines" and "Transceiver Functions" to solve the multi-agents problem. This report will start with a little research on the Neurorobotics Platform and analyze the potential and possibility of developing a new simulator to achieve the true real-world simulation goal. Then based on the NRP-Core Platform, this initial development aims to construct an initial demo experiment. The consist of this report starts with the basic knowledge of NRP-Core and its installation, then focus on the explanation of the necessary components for a simulation experiment, at last, about the details of constructions for the autonomous driving system, which is integrated object detection function and autonomous driving control function. At the end will discuss the existing disadvantages and improvements of this autonomous driving system
An Integrated Visual System for Unmanned Aerial Vehicles Tracking and Landing on the Ground Vehicles

The vision of unmanned aerial vehicles is very significant for UAV-related applications such as search and rescue, landing on a moving platform, etc. In this work, we have developed an integrated system for the UAV landing on the moving platform, and the UAV object detection with tracking in the complicated environment. Firstly, we have proposed a robust LoG-based deep neural network for object detection and tracking, which has great advantages in robustness to object scale and illuminations compared with typical deep network-based approaches. Then, we have also improved based on the original Kalman filter and designed an iterative multi-model-based filter to tackle the problem of unknown dynamics in real circumstances of motion estimations. Next, we implemented the whole system and do ROS Gazebo-based testing in two complicated circumstances to verify the effectiveness of our design. Finally, we have deployed the proposed detection, tracking, and motion estimation strategies into real applications to do UAV tracking of a pillar and obstacle avoidance. It is demonstrated that our system shows great accuracy and robustness in real applications.
Using affine policies to reformulate two-stage Wasserstein distributionally robust linear programs to be independent of sample size

Intensively studied in theory as a promising data-driven tool for decision-making under ambiguity, two-stage distributionally robust optimization (DRO) problems over Wasserstein balls are not necessarily easy to solve in practice. This is partly due to large sample size. In this article, we study a generic two-stage distributionally robust linear program (2-DRLP) over a 1-Wasserstein ball using an affine policy. The 2-DRLP has right-hand-side uncertainty with a rectangular support. Our main contribution is to show that the 2-DRLP problem has a tractable reformulation with a scale independent of sample size. The reformulated problem can be solved within a pre-defined optimality tolerance using robust optimization techniques. To reduce the inevitable conservativeness of the affine policy while preserving independence of sample size, we further develop a method for constructing an uncertainty set with a probabilistic guarantee over which the Wasserstein ball is re-defined. As an application, we present a novel unit commitment model for power systems under uncertainty of renewable energy generation to examine the effectiveness of the proposed 2-DRLP technique. Extensive numerical experiments demonstrate that our model leads to better out-of-sample performance on average than other state-of-the-art distributionally robust unit commitment models while staying computationally competent.
Federated Multi-Agent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multi-Microgrid Energy Management

The utilization of large-scale distributed renewable energy (RE) promotes the development of the multimicrogrid (MMG), which raises the need of developing an effective energy management method to minimize economic costs and keep self energy sufficiency. The multiagent deep reinforcement learning (MADRL) has been widely used for the energy management problem because of its real-time scheduling ability. However, its training requires massive energy operation data of microgrids (MGs), while gathering these data from different MGs would threaten their privacy and data security. Therefore, this article tackles this practical yet challenging issue by proposing a federated MADRL (F-MADRL) algorithm via the physics-informed reward. In this algorithm, the federated learning (FL) mechanism is introduced to train the F-MADRL algorithm, thus ensures the privacy and the security of data. In addition, a decentralized MMG model is built, and the energy of each participated MG is managed by an agent, which aims to minimize economic costs and keep self energy sufficiency according to the physics-informed reward. At first, MGs individually execute the self-training based on local energy operation data to train their local agent models. Then, these local models are periodically uploaded to a server and their parameters are aggregated to build a global agent, which will be broadcasted to MGs and replace their local agents. In this way, the experience of each MG agent can be shared and the energy operation data are not explicitly transmitted, thus protecting the privacy and ensuring data security. Finally, experiments are conducted on Oak Ridge National Laboratory distributed energy control communication laboratory MG (ORNL-MG) test system, and the comparisons are carried out to verify the effectiveness of introducing the FL mechanism and the outperformance of our proposed F-MADRL.