Miracle-dz/UniFAR-FTUs huggingface dataset:
Nocturnal stomatal conductance in wheat is growth stage specific and shows genotypic variation.

Nocturnal stomatal conductance (gsn ) represents a significant source of water-loss, with implications for metabolism, thermal regulation and water-use efficiency. With increasing nocturnal temperatures due to climate change, it is vital to identify and understand variation in the magnitude and responses of gsn in major crops. We assessed interspecific variation in gsn and daytime stomatal conductance (gs ) in a wild relative and modern spring wheat genotype. To investigate intraspecific variation, we grew six modern wheat genotypes and two landraces under well-watered, simulated field conditions. For the diurnal data, higher gsn in the wild relative associated with significantly lower nocturnal respiration and higher daytime CO2 assimilation while both species exhibited declines in gsn post-dusk and pre-dawn. Lifetime gsn achieved rates of 5.7-18.9% of gs . Magnitude of gsn was genotype-specific and positively correlated with gs gsn and gs was significantly higher on the adaxial surface. No relationship was determined between harvest characteristics, stomatal morphology and gsn , while cuticular conductance was genotype-specific. Finally, in the majority of genotypes, gsn declined with age. Here we present the discovery that variation in gsn occurs across developmental, morphological and temporal scales in non-stressed wheat presenting opportunities for exploiting intrinsic variation under heat or water stressed conditions.
The Mediating Roles of Impulsiveness and Maladjustment in the Association of Psychological Resilience with Drug Addiction during Isolated Detoxification

Abstract Background: Studies have shown that psychological resilience is a key factor in drug rehabilitation. To explore the feasibility of developing psychological resilience as an addiction treatment intervention, it is essential to explore the role that it plays in drug addiction. Objectives: This study aimed to investigate the relationship between psychological resilience and drug addiction, as well as to examine the underlying mediational roles of maladjustment and impulsiveness in this association. Methods: We used a cross-sectional design that included a sample of 140 male drug addicts in compulsory isolation centers and used questionnaires and scales to ascertain their level of drug addiction, psychological resilience, maladjustment, impulsiveness, social support, and loneliness. Correlation and mediation effect analyses were performed to determine the roles of impulsiveness and maladjustment in the association of psychological resilience with drug addiction. Results: Psychological resilience was an inverse predictor of drug addiction. The results of the mediation effect analysis showed that maladjustment acted as a mediator between resilience and drug addiction and between impulsiveness and drug addiction. Furthermore, impulsiveness and maladjustment jointly mediated the relationship between psychological resilience and drug addiction. Conclusion: These findings highlight the importance of psychological resilience in maladjustment and impulsiveness for drug addicts and suggest that the role of psychological resilience in drug addiction needs to be further explored.
Investigation of structural, electronic, magnetic, elastic and thermodynamic properties of Mn3GaN antiperovskite by first principle and Monte Carlo simulations

ABSTRACT Structural, electronic, magnetic, elastic and thermodynamic properties of anti-perovskite have been studied using the first-principles calculations based on density functional theory (DFT) and Monte Carlo simulations within the Ising model. The structural investigation exposes the antiferromagnetic configuration stability of the compound. Also, the density of states (DOS) and band structure calculations show a metallic behaviour of . The computed elastic constants, phonon dispersion and density phonon states calculations demonstrate that the compound is mechanically and dynamically stable. The calculated results of Poisson's ratio, Pugh's ratio and Cauchy pressure show that the is ductile material with a metallic bond. The thermodynamic parameters like heat capacity, Debye and melting temperature have also been calculated. The exchange coupling of has been calculated to obtain the critical temperature ( ) from Monte Carlo calculations. The obtained value of (288 K) is in good agreement with other experimental and theoretical works.
A new form of pseudopotential for the study of lattice mechanical properties of transition metals

ABSTRACT The study of lattice mechanical properties of transition metals using pseudopotential is burdensome as the presence of d-band in these metals complicates their electronic structure. The use of pseudopotential for the study of physical properties can be justified if it accounts for the effect of d-band. The local form of the pseudopotentials is widely used to study the physical properties of materials due to their computational simplicity, physical transparency, and transferability to other environments. It is a well-established fact that non-local pseudopotentials should be preferred to the local ones. But, non-local pseudopotentials are proven to be at a higher computational cost. Inspired by such requirements, we, in the present communication, propose a pseudopotential comprising of one non-local (energy-dependent) and another local term in q-space. The non-local (energy-dependent) term accounts for s-p interaction, while the other term is used to account for the effect of s-d hybridisation. The Generalized Pseudopotential Theory (GPT) has been used to formulate the pseudopotential. Further, the pseudopotential has been used to study static binding energy, lattice dynamics, lattice mechanical properties and second-order elastic constants in the long-wavelength limit (q→0) for transition metals γ-Fe (fcc), Co and α-Fe (bcc). Computed results are found to be in good agreement with experimental and other theoretical results, which proves that the proposed pseudopotential itself accounts for the effect of d-band without using additional short-ranged Born–Mayer type potential and effective valency.
Cycles of Conflict, a Century of Continuity: The Impact of Persistent Place-Based Political Logics on Social Movement Strategy1

The women’s liberation movement hotly debated both the cause of women’s oppression and the best approach to changing it. When treated as a moment within 1960s political polarization, these debates can seem esoteric and arbitrary. When examined across the longue durée, the debates prove to reflect complex and stable differences in interpretation that were tied to place more than to the political moment. Using a combination of network analysis, computational text analysis, and qualitative interpretation, the author examines women’s movement discourses across the first and second waves of activism and between two sites, New York City and Chicago. Place, she finds, serves to capture differences in political logics that generate durable differences within movement discourse.
Activation mechanism of Drosophila cryptochrome through an allosteric switch

Photoexcitation of FAD triggers ion-pair breakage and allosteric conformational change, leading to cryptochrome activation. Cryptochromes are signaling proteins activated by photoexcitation of the flavin adenine dinucleotide (FAD) cofactor. Although extensive research has been performed, the mechanism for this allosteric process is still unknown. We constructed three computational models, corresponding to different redox states of the FAD cofactor in Drosophila cryptochrome (dCRY). Analyses of the dynamics trajectories reveal that the activation process occurs in the semiquinone state FAD−●, resulting from excited-state electron transfer. The Arg381-Asp410 salt bridge acts as an allosteric switch, regulated by the change in the redox state of FAD. In turn, Asp410 forms new hydrogen bonds, connecting allosteric networks of the amino-terminal and carboxyl-terminal domains initially separated in the resting state. The expansion to a global dynamic network leads to enhanced protein fluctuations, an increase in the radius of gyration, and the expulsion of the carboxyl-terminal tail. These structural features are in accord with mutations and spectroscopic experiments.
Control of intestinal inflammation by glycosylation-dependent lectin-driven immunoregulatory circuits

Glycosylation-dependent programs control intestinal inflammation. Diverse immunoregulatory circuits operate to preserve intestinal homeostasis and prevent inflammation. Galectin-1 (Gal1), a β-galactoside–binding protein, promotes homeostasis by reprogramming innate and adaptive immunity. Here, we identify a glycosylation-dependent “on-off” circuit driven by Gal1 and its glycosylated ligands that controls intestinal immunopathology by targeting activated CD8+ T cells and shaping the cytokine profile. In patients with inflammatory bowel disease (IBD), augmented Gal1 was associated with dysregulated expression of core 2 β6-N-acetylglucosaminyltransferase 1 (C2GNT1) and α(2,6)-sialyltransferase 1 (ST6GAL1), glycosyltransferases responsible for creating or masking Gal1 ligands. Mice lacking Gal1 exhibited exacerbated colitis and augmented mucosal CD8+ T cell activation in response to 2,4,6-trinitrobenzenesulfonic acid; this phenotype was partially ameliorated by treatment with recombinant Gal1. While C2gnt1−/− mice exhibited aggravated colitis, St6gal1−/− mice showed attenuated inflammation. These effects were associated with intrinsic T cell glycosylation. Thus, Gal1 and its glycosylated ligands act to preserve intestinal homeostasis by recalibrating T cell immunity.
Examining discrimination and familism values as longitudinal predictors of prosocial behaviors among recent immigrant adolescents

The current study was designed to address gaps in the existing literature by examining the role of discrimination and familism values as predictors of multiple forms of prosocial behaviors across time in a sample of recent immigrant Latino/a adolescents. Participants were 302 recent immigrant Latino/a adolescents (53.3% male; average age 14.51 years, range = 13–17). Data were collected from adolescents in two U.S. cities: Los Angeles (n = 150) and Miami (n = 152). Adolescents completed measures of their own discrimination experiences, familism values, and tendency to engage in six forms of prosocial behaviors. Results indicated generally positive links between familism values and prosocial behaviors. Discrimination also positively predicted public prosocial behaviors and negatively predicted altruistic prosocial behaviors. We discuss the development of cultural processes and perceptions of discrimination experiences, and how these factors predict helping behaviors among immigrant adolescents.
The Case of the Hungry Hippos: Supporting Students’ Development of Problem-Finding Strategies

While problem identification is a key stage in creative problem solving, many gifted students may rush to begin developing solutions before they have spent sufficient time accurately identifying the underlying problem or opportunity. As a result, they may develop irrelevant or hackneyed solutions. Despite the importance of problem finding, teachers may struggle to provide opportunities for students to practice authentic problem finding for a variety of reasons, including ambiguity surrounding the problem-finding process and concerns regarding external expectations emphasizing standardized assessment performance. Thus, the purpose of this article is to address these concerns by (a) operationalizing the process of problem finding and (b) delineating an instructional design method to support teachers as they construct problem-finding opportunities for students. Our research team identified four primary cognitive strategies involved in problem finding, including: Resource Management, Elaboration, Analysis, and Manipulation, which we integrate into the “Facilitating Problem-Finding Model” to provide clarity for the problem identification process. Then, we illustrate how teachers may use these specific strategic approaches to create targeted learning objectives. These objectives serve to anchor assessments and classroom experiences, providing students with important opportunities to develop problem-finding skills.
MT-DLA: An Efficient Multi-Task Deep Learning Accelerator Design

Multi-task learning systems are commonly adopted in many real-world AI applications such as intelligent robots and self-driving vehicles. Instead of improving single-network performance, this work proposes a specialized Multi-Task Deep Learning Accelerator architecture, MT-DLA, to improve the performance of concurrent networks by exploiting the shared feature and parameters across these models. It is shown in our evaluation with realistic multi-task workloads, MT-DLA dramatically eliminates the memory and computation overhead caused by the shared parameters, activations and computation result. In the experiments with real-world multi-task learning workloads, MT-DLA brings about 1.4x-7.0x energy efficiency boost when compared to the baseline neural network accelerator without multi-task support.
MEDASec: Logic Encryption Scheme for Micro-electrode-dot-array Biochips IP Protection

As the next generation of digital microfluidic biochips (DMFBs) platform, Micro-electrode-dot-array (MEDA) has sea-of-micro-electrodes that support fine-grained control of the droplet and constant real-time monitoring of droplet paths. As MEDA biochips are expected to be had a booming market in the near future, their security, especially the protection of intellectual property (IP), has become the focus of the academic community. In this paper, we first propose a logic encryption-based IP protection scheme for MEDA biochips, called MEDASec, which is without modifying the structure. Our scheme aims to hide the droplet mix-split operation in bioassay, making the IP akin to a black-box in the manufacturing process. Besides, increasing computing power has facilitated the threat of brute force attacks on the secret key. In response to the brute force attack, we propose security metrics for the first time, called RRA, to evaluate the ratio of reagents that result from biochemical reactions. Experimental results on multiple bioassays demonstrate the effectiveness of the proposed logical encryption strategy against the brute force attack.
PACE: Learning Effective Task Decomposition for Human-in-the-loop Healthcare Delivery

Human-in-the-loop data analysis involves both machine learning models and humans in analytic tasks. In healthcare applications, human-in-the-loop data analysis is crucial in that the model can handle "easy" tasks and hand over "hard" ones to medical experts for assistance and medical judgment, where easy tasks are the ones for which the model can provide high accuracy and hard tasks vice versa. In this process, how to decompose tasks in an effective manner is an important stage. To achieve task decomposition, classification with a reject option is a solution. However, existing studies either directly implement a reject option or dive into the theoretical details of the rejection mechanism. Different from such studies, we aim to optimize general classifiers with a reject option and hence, optimize task decomposition for healthcare applications. To this end, we first introduce task decomposition for healthcare applications, which is a crucial stage in human-in-the-loop healthcare delivery. We then devise a framework PACE to learn effective task decomposition concentrating on delivering high performance on the easy tasks. PACE is two-level: on the macro level, PACE employs the Self-Paced Learning method to select easy tasks for each training iteration; on the micro level, PACE adapts the weights of selected tasks through its weighted loss revision strategy. Experimental results in two real-world healthcare datasets show that PACE outperforms baselines in terms of their performance on the easy tasks which are expected to be solved by the learning model.
Benchmarking Approximate Consistent Query Answering

Consistent query answering (CQA) aims to deliver meaningful answers when queries are evaluated over inconsistent databases. Such answers must be certainly true in all repairs, which are consistent databases whose difference from the inconsistent one is somehow minimal. Although CQA provides a clean framework for querying inconsistent databases, it is arguably more informative to compute the percentage of repairs in which a candidate answer is true, instead of simply saying that is true in all repairs, or is false in at least one repair. It should not be surprising, though, that computing this percentage is computationally hard. On the other hand, for practically relevant settings such as conjunctive queries and primary keys, there are data-efficient randomized approximation schemes for approximating this percentage. Our goal is to perform a thorough experimental evaluation and comparison of those approximation schemes. Our analysis provides new insights on which technique is indicated depending on key characteristics of the input, and it further provides evidence that making approximate CQA as described above feasible in practice is not an unrealistic goal.
Floating Utopia, in 2 Acts

Our project is a total installation, consisting of space, objects, video and audio. It comes in 2 acts: an already exhibited one ”Gradient Descent Into the Wetlands”, and work in progress. The project deals with the idea of reconstructing the natural skin of critical zones by algorithms and neural networks. The term critical zones was introduced by Peter Weibel and Bruno Latour [4]. It is the area of the Earth’s surface, thin but vital skin of the planet, which is now influenced by the human impact. Biodiversity in this kind of zones is in danger.
Assessing Programming Skills and Knowledge During the COVID-19 Pandemic: An Experience Report

The current COVID-19 pandemic has resulted in disruption to the delivery of higher education. The government-mandated workplace closures that lasted for two months from April 2020 resulted in the closing of all university campuses in our city. This happened while our first-year introductory Python programming course was still in progress. We were thus unable to administer our final exam on campus. In this paper, we describe how our final exam, usually conducted on campus, was replaced with a performance-based assessment. This assessment tasked students to design and program their own game individually. After submitting their code, each student was then required to attend an oral exam that was administered online. We reflect on our experience, drawing from both instructors' and students' perspectives of the programming task and the assessment format. We conclude with a description of how the lessons learnt were applied to a subsequent run of the course.
CoRM: Compactable Remote Memory over RDMA

Distributed memory systems are becoming increasingly important since they provide a system-scale abstraction where physically separated memories can be addressed as a single logical one. This abstraction enables memory disaggregation, allowing systems as in-memory databases, caching services, and ephemeral storage to be naturally deployed at large scales. While this abstraction effectively increases the memory capacity of these systems, it faces additional overheads for remote memory accesses. To narrow the difference between local and remote accesses, low latency RDMA networks are a key element for efficient memory disaggregation. However, RDMA acceleration poses new obstacles to efficient memory management and particularly to memory compaction: network controllers and CPUs can concurrently access memory, potentially leading to inconsistencies if memory management operations are not synchronized. To ensure consistency, most distributed memory systems do not provide memory compaction and are exposed to memory fragmentation. We introduce CoRM, an RDMA-accelerated shared memory system that supports memory compaction and ensures strict consistency while providing one-sided RDMA accesses. We show that CoRM sustains high read throughput during normal operations, comparable to similar systems not providing memory compaction while experiencing minimal overheads during compaction. CoRM never disrupts RDMA connections and can reduce applications' active memory up to 6x by performing memory compaction.
An Energy-Efficient Stream Join for the Internet of Things

The Internet of Things (IoT) combines large data centers with (mobile, networked) edge devices that are constrained both in compute power and energy budget. Modern edge devices contribute to query processing by leveraging accelerated processing units with multicore CPUs or GPUs. Therefore, data processing in the IoT presents the challenges of 1) minimizing the energy consumed while sustaining a given query throughput, and 2) processing increasingly complex queries within a given energy budget. In this paper, we investigate how modern edge devices can reduce the energy requirements of stream joins as a common data processing operation. We explore three dimensions to save energy: workload characteristics, computational efficiency, and heterogeneous hardware. Based on our findings, we propose the ecoJoin that 1) reduces energy consumption by 81% at a given join throughput, and 2) enables scaling the throughput by two orders-of-magnitude within a given energy budget.
Modernizing Legacy Systems with Microservices: A Roadmap

Legacy systems are long-lived applications, with obsolete technology and degraded architecture. These systems hamper digital transformation and innovation, and require a great amount of resources for maintenance. The modernization of monolithic legacy systems is a strategy to promote better evolution and maintenance, taking advantage of new technologies such as microservices. Microservice architectural style is a paradigm to develop systems as a suite of small and autonomous services, communicating through a lightweight protocol. However, the migration of legacy systems to microservices is complex. Although we can find several studies on this topic, they usually focus on specific activities, e.g., the identification of the microservice boundaries in the legacy code. Also, existing pieces of work do not cover real-world scenarios, since they do not take into account organizational, operational, and technical aspects. To overcome this limitation, in this paper we present a roadmap for modernizing monolithic legacy systems with microservices. The roadmap is distilled from the existing body of knowledge, describing common activities and input/output information. The proposed roadmap is composed of eight activities, grouped in four phases, namely initiation, planning, execution, and monitoring. The main contributions are: (i) serve as a basis for practitioners to plan, execute, and monitor the modernization process; (ii) be a reference for researchers to design new studies; and (iii) motivate tool builders to deal with existing needs.
Cross-Boundary Inductive Timing Optimization for 2.5D Chiplet-Package Co-Design

With the popularity of 2.5D integration, an increasing number of chiplets are integrated into advanced system-in-package designs. In such systems, redistribution layer (RDL) wires become longer and denser, with a growing impact on system performance. However, RDL inductive impacts in timing analysis are ignored by the traditional CAD tools. This paper presents our chiplet-package co-optimization flow, which can capture the RDL inductance impact on system performance and automatically adjust the IO drivers to compensate for the inductance overhead. We develop our extraction and timing analysis tool that models RDL wire inductive timing impact on 2.5D system performance within +/-1% error. Our study shows 35% signal paths through RDL violate the timing requirement because of the inductive impact, and remain undetected through only RC-based STA.