Miracle-dz/UniFAR-FTUs huggingface dataset:
Photosynthesis and photoinhibition at low temperatures: physiological responses of Antarctic rhodophytes

The environment of Antarctica represents one of the most challenging and harshest ecosystems, characterized by very low temperatures and a strong seasonality in light availability. Due to the abiotic conditions, inhabiting organisms are highly adapted to their habitat and possess the ability to cope flexibly with changing environmental factors. In the context of global climate change, the Antarctic and especially the Antarctic Peninsula undergo the most rapid and significantly changing regions worldwide. The combination of low temperatures and high light intensities are challenging conditions for photosynthetic organisms, as low temperatures reduce for instance enzymatic processes and the turn-over of the D1 centre protein of photosystem II. This protein plays a crucial role in photosynthetic function.Low water temperatures also decrease membrane fluidity, resulting in an impairment of transfer processes for instance through the thylakoid membrane of chloroplasts. In addition, photosynthetic activity is particularly sensitive to low temperatures, as enzymatic secondary reactions are strictly temperature-dependent, while primary reactions are not. The present thesis investigated the physiological performance, the acclimation potential and tolerance limits of the endemic Antarctic rhodophyte Palmaria decipiens. In various experiments conducted under laboratory and field conditions, the alga was exposed to changing light and temperature levels. It was hypothesized that in particular the combination of photosynthetically active radiation (PAR, 400-700 nm) and low temperatures lead to stress responses in the organism. To estimate the responses of P. decipiens, the alga was exposed over different periods of time to natural and artificial radiation. The macroalgae investigated in the present study revealed a relatively high acclimation potential within their species-specific tolerance ranges. Obviously, this tolerance is limited and particularly rapid and pronounced changes can impact seaweeds negatively. To obtain realistic predictions in a more ecological context, investigations should involve interactive effects of the abiotic and biotic conditions. In summary, this study demonstrates that Antarctic rhodophytes are very well adapted to their environment and are, in spite of this, still able to acclimate and cope with certain changes in the abiotic conditions. The results obtained during the present study also indicate that the combined effects of environmental conditions is an important issue that has to be considered when adressing ecological questions.
Mechanical Terahertz Modulation Based on Single‐Layered Graphene

The 2D terahertz (THz) modulator, enabling efficient manipulation of such versatile band in nanoscale, is crucial for THz microdevices and systems, but its implementation is difficult and remains challenging in practice. Here, a novel 2D THz modulator based on single‐layered graphene under mechanical strain is demonstrated. Bidirectional, i.e., both positive and negative, THz modulation effect is realized by utilizing unconventionally distributed strains on graphene. Such mechanical modulation is found to be stable and reversible, and its modulation depth can exceed 26% at 1 THz under 10–2 GPa strain. Observations of both the strain and frequency dependent modulation behavior evidence the mechanical strain‐induced change of the Dirac‐like energy dispersion in graphene, which is distinctive from that of the electrical and optical approaches. Due to the reliability and wide applicability of mechanical forces, these results provide an alternative route for chip‐scale THz modulation devices based on 2D materials.
A comparison between single layer and multilayer artificial neural networks in predicting diesel fuel properties using near infrared spectrum

ABSTRACT The implementation of near infrared spectroscopy in monitoring diesel fuel properties is highly dependent on the capability of its predictive model. This study investigates the feasibility of a single layer artificial neural networks among various predictive models in predicting the diesel fuel properties using near infrared spectrum. Results were compared and discussed with that reported in previous studies that used the same data in predicting the diesel fuel properties. Findings show that the proposed single layer outperforms popular models, and is comparable with a recent advanced models in predicting the diesel fuel properties using near Infrared spectrum without data reduction.
Phase space evolution of cold atoms in the presence of a weakly disordered potential

We study the phase space evolution of cold atoms oscillating in a harmonic trap in the presence of a weak potential disorder. We demonstrate a full tomographic reconstruction of the atomic phase space distribution from a series of real-space images of the trapped atoms. For oscillation times on the order of the mean time between collisions we observe squeezing of the phase space distribution and deformations determined by the specific shape of the potential corrugations. This evolution is interpreted as angular velocity dispersion of iso-energetic trajectories in phase space. We discuss the effects of collisions and coupling between different dimensions, as well as possible applications of the method for probing classical or quantum dynamics of cold atoms.
Reimagining the Water Snowline

Water is a molecule that is tightly related to many facets of star and planet formation. Water’s abundance and distribution, especially the location of its snowline has thus been the subject of much study. While water is seen to be abundant in the inner region of protoplanetary disks in infrared spectroscopy, detections of water in the disk in the submillimeter are rare, with only one detection toward AS 205. Here we put the multitude of nondetections and the single detection into context of recent physicochemical models. We find that the 321.2257 GHz (102,9–93,6) line detection toward AS 205 is inconsistent with a normal inner disk temperature structure and that the observed line must be masing. Furthermore, the emitting area derived from the line width, together with published analyses on water in disks around T-Tauri stars implies that the water snowline in the disk surface is at the same location as the snowline in the midplane. We propose that this is caused by vertical mixing continuously sequestering water from the warm surface layers into the cold disk midplane.
A Dimension Reduction Technique for Large-Scale Structured Sparse Optimization Problems with Application to Convex Clustering

In this paper, we propose a novel adaptive sieving (AS) technique and an enhanced AS (EAS) technique, which are solver independent and could accelerate optimization algorithms for solving large scale convex optimization problems with intrinsic structured sparsity. We establish the finite convergence property of the AS technique and the EAS technique with inexact solutions of the reduced subproblems. As an important application, we apply the AS technique and the EAS technique on the convex clustering model, which could accelerate the state-of-the-art algorithm SSNAL by more than 7 times and the algorithm ADMM by more than 14 times.
Learning to Compute Approximate Nash Equilibrium for Normal-form Games

In this paper, we propose a general meta learning approach to computing approximate Nash equilibrium for ﬁnite n -player normal-form games. Unlike existing solutions that approximate or learn a Nash equilibrium from scratch for each of the games, our meta solver directly constructs a mapping from a game utility matrix to a joint strategy proﬁle. The mapping is parameterized and learned in a self-supervised fashion by a proposed Nash equilibrium approximation metric without ground truth data informing any Nash equilibrium. As such, it can immediately predict the joint strategy proﬁle that approximates a Nash equilibrium for any unseen new game under the same game distribution. Moreover, the meta-solver can be further ﬁne-tuned and adaptive to a new game if iteration updates are allowed. We theoretically prove that our meta-solver is not affected by the non-smoothness of exact Nash equilibrium solutions, and derive a sample complexity bound to demonstrate its generalization ability across normal-form games. Experimental results demonstrate its substantial approximation power against other strong baselines in both adaptive and non-adaptive cases.
Task-Oriented Multi-User Semantic Communications for Multimodal Data

—Semantic communications focus on the successful transmission of information relevant to the transmission task. In this paper, we investigate multi-users transmission for multimodal data in a task semantic communication system. We take the vision-answering as the semantic transmission task, in which part of the users transmit images and the other users transmit text to inquiry the information about the images. The receiver will provide answers based on the image and text from multiple users in the considered system. To exploit the correlation between the multimodal data from multiple users, we proposed a deep neural network enabled multi-user semantic communication system, named MU-DeepSC, for the visual question answering (VQA) task, in which the answer is highly dependent on the related image and text from the multiple users. Particularly, based on the memory, attention, and composition (MAC) neural network, we jointly design the transceiver and merge the MAC network to capture the features from the correlated multimodal data for serving the transmission task. The MU-DeepSC extracts the semantic information of image and text from different users and then generates the corresponding answers. Simulation results validate the feasibility of the proposed MU-DeepSC, which is more robust to various channel conditions than the traditional communication systems, especially in the low signal-to-noise (SNR) regime.
Graph Capsule Aggregation for Unaligned Multimodal Sequences

Humans express their opinions and emotions through multiple modalities which mainly consist of textual, acoustic and visual modalities. Prior works on multimodal sentiment analysis mostly apply Recurrent Neural Network (RNN) to model aligned multimodal sequences. However, it is unpractical to align multimodal sequences due to different sample rates for different modalities. Moreover, RNN is prone to the issues of gradient vanishing or exploding and it has limited capacity of learning long-range dependency which is the major obstacle to model unaligned multimodal sequences. In this paper, we introduce Graph Capsule Aggregation (GraphCAGE) to model unaligned multimodal sequences with graph-based neural model and Capsule Network. By converting sequence data into graph, the previously mentioned problems of RNN are avoided. In addition, the aggregation capability of Capsule Network and the graph-based structure enable our model to be interpretable and better solve the problem of long-range dependency. Experimental results suggest that GraphCAGE achieves state-of-the-art performance on two benchmark datasets with representations refined by Capsule Network and interpretation provided.
First-Order Theory of Probabilistic Independence and Single-Letter Characterizations of Capacity Regions

This paper shows that many notions in information theory (e.g. entropy, capacity regions) can be defined using probabilistic independence alone. We consider the first-order theory of random variables with the probabilistic independence relation, which concerns statements consisting only of random variables, the probabilistic independence symbol, logical operators, and existential and universal quantifiers. Although probabilistic independence is the only non-logical relation included, this theory is surprisingly expressive, and is able to express notions such as entropy and cardinality, and interpret the true first-order arithmetic over natural numbers (and hence is undecidable). We also characterize the capacity region for a general class of multiuser coding settings (including broadcast channel, interference channel and relay channel) using a first-order formula, which can be regarded as a "single-letter characterization" of the capacity region of the aforementioned settings (conventional single-letter characterizations are existential formulae, whereas our formula contains both existential and universal quantifiers).
Identifying Biased Subgroups in Ranking and Classification

When analyzing the behavior of machine learning algorithms, it is important to identify specific data subgroups for which the considered algorithm shows different performance with respect to the entire dataset. The intervention of domain experts is normally required to identify relevant attributes that define these subgroups. We introduce the notion of divergence to measure this performance difference and we exploit it in the context of (i) classification models and (ii) ranking applications to automatically detect data subgroups showing a significant deviation in their behavior. Furthermore, we quantify the contribution of all attributes in the data subgroup to the divergent behavior by means of Shapley values, thus allowing the identification of the most impacting attributes.
Mass-loading and non-spherical divergence in hot galactic winds: implications for X-ray observations

Cool clouds are expected to be destroyed and incorporated into hot supernova-driven galactic winds. The mass-loading of a wind by the cool medium modifies the bulk velocity, temperature, density, entropy, and abundance profiles of the hot phase relative to an un-mass-loaded outflow. We provide general equations and limits for this physics that can be used to infer the rate of cool gas entrainment from X-ray observations, accounting for non-spherical expansion. In general, mass-loading flattens the density and temperature profiles, decreases the velocity and increases the entropy if the Mach number is above a critical value. We first apply this model to a recent high-resolution galactic outflow simulation where the mass-loading can be directly inferred. We show that the temperature, entropy, and composition profiles are well-matched, providing evidence that this physics sets the bulk hot gas profiles. We then model the diffuse X-ray emission from the local starburst M82. The non-spherical (more cylindrical) outflow geometry is directly taken from the observed X-ray surface brightness profile. These models imply a total mass-loading rate that is about equal to that injected in the starburst, ≃ 10 M⊙ yr−1, and they predict an asymptotic hot wind velocity of ∼1000 km s−1 that is ∼1.5 − 2 times smaller than previous predictions. We also show how the observed entropy profile can be used to constrain the outflow velocity, making predictions for future missions like XRISM. We argue that the observed X-ray limb-brightening may be explained by mass-loading at the outflow’s edges.
An Iterative Improvement Method for HHL algorithm for Solving Linear System of Equations

We propose an iterative improvement method for the Harrow-Hassidim-Lloyd (HHL) algorithm to solve a linear system of equations. This is a quantum-classical hybrid algorithm. The accuracy is essential to solve the linear system of equations. However, the accuracy of the HHL algorithm is limited by the number of quantum bits used to express the eigenvalues of the matrix. Our iterative method improves the accuracy of the HHL solutions, and gives higher accuracy which surpasses the accuracy limited by the number of quantum bits. In practical HHL algorithm, a huge number of measurements is required to obtain good accuracy, even if we provide a sufficient number of quantum bits for the eigenvalue expression, since the solution is statistically processed from the measurements. Our improved iterative method can reduce the number of measurements. Moreover, the sign information for each eigenstate of the solution is lost once the measurement is made, although the sign is significant. Therefore, the na\"ive iterative method of the HHL algorithm may slow down, especially, when the solution includes wrong signs. In this paper, we propose and evaluate an improved iterative method for the HHL algorithm that is robust against the sign information loss, in terms of the number of iterations and the computational accuracy.
Asymmetric transport in long-range interacting chiral spin chains

Harnessing power-law interactions (1/r^\alpha1/rα) in a large variety of physical systems are increasing. We study the dynamics of chiral spin chains as a possible multi-directional quantum channel. This arises from the nonlinear character of the dispersion with complex quantum interference effects. Using complementary numerical and analytical techniques, we propose a model to guide quantum states to a desired direction. We illustrate our approach using the long-range XXZ model modulated by Dzyaloshinskii-Moriya (DM) interaction. By exploring non-equilibrium dynamics after a local quantum quench, we identify the interplay of interaction range \alphaα and Dzyaloshinskii-Moriya coupling giving rise to an appreciable asymmetric spin excitations transport. This could be interesting for quantum information protocols to transfer quantum states, and it may be testable with current trapped-ion experiments. We further explore the growth of block entanglement entropy in these systems, and an order of magnitude reduction is distinguished.
Computing and Maintaining Provenance of Query Result Probabilities in Uncertain Knowledge Graphs

Knowledge graphs (KG) model relationships between entities as labeled edges (or facts). They are mostly constructed using a suite of automated extractors, thereby inherently leading to uncertainty in the extracted facts. Modeling the uncertainty as probabilistic confidence scores results in a probabilistic knowledge graph. Graph queries over such probabilistic KGs require answer computation along with the computation of result probabilities, i.e., probabilistic inference. We propose a system, HAPPI (How Provenance of Probabilistic Inference), to handle such query processing and inference. Complying with the standard provenance semiring model, we propose a novel commutative semiring to symbolically compute the probability of the result of a query. These provenance-polynomial-like symbolic expressions encode fine-grained information about the probability computation process. We leverage this encoding to efficiently compute as well as maintain probabilities of results even as the underlying KG changes. Focusing on conjunctive basic graph pattern queries, we observe that HAPPI is more efficient than knowledge compilation for answering commonly occurring queries with lower range of probability derivation complexity. We propose an adaptive system that leverages the strengths of both HAPPI and compilation based techniques, for not only to perform efficient probabilistic inference and compute their provenance, but also to incrementally maintain them.
On the complexity of two-party differential privacy

In distributed differential privacy, the parties perform analysis over their joint data while preserving the privacy for both datasets. Interestingly, for a few fundamental two-party functions such as inner product and Hamming distance, the accuracy of the distributed solution lags way behind what is achievable in the client-server setting. McGregor, Mironov, Pitassi, Reingold, Talwar, and Vadhan [FOCS ’10] proved that this gap is inherent, showing upper bounds on the accuracy of (any) distributed solution for these functions. These limitations can be bypassed when settling for computational differential privacy, where the data is differentially private only in the eyes of a computationally bounded observer, using oblivious transfer. We prove that the use of public-key cryptography is necessary for bypassing the limitation of McGregor et al., showing that a non-trivial solution for the inner product, or the Hamming distance, implies the existence of a key-agreement protocol. Our bound implies a combinatorial proof for the fact that non-Boolean inner product of independent (strong) Santha-Vazirani sources is a good condenser. We obtain our main result by showing that the inner-product of a (single, strong) SV source with a uniformly random seed is a good condenser, even when the seed and source are dependent.
Distant Representatives for Rectangles in the Plane

The input to the distant representatives problem is a set of n objects in the plane and the goal is to find a representative point from each object while maximizing the distance between the closest pair of points. When the objects are axis-aligned rectangles, we give polynomial time constant-factor approximation algorithms for the L₁, L₂, and L_∞ distance measures. We also prove lower bounds on the approximation factors that can be achieved in polynomial time (unless P = NP).
Monitor++?: Multiple versus Single Laboratory Monitors in Early Programming Education

CONTRIBUTION: This paper presents an empirical study of an introductory-level programming course with students using multiple monitors and compares their performance and self-reported experiences versus students using a single monitor. BACKGROUND: Professional-level programming in many technological fields often employs multiple-monitors stations, however, some education laboratories employ single-monitor stations. This is unrepresentative of what students will encounter in practice and experiential learning. RESEARCH QUESTIONS: This study aims to answer three research questions. The questions include discovering the experiential observations of the students, contrasting the performance of the students using one monitor versus those using two monitors, and an investigation of the ways in which multiple monitors were employed by the students. METHODOLOGY: Half of the students in the study had access to multiple monitors. This was the only difference between the two study groups. This study contrasts grade medians and conducts median-test evaluation. Additionally, an experience survey facilitated likert-scale values and open-ended feedback questions facilitated textual analysis. Limitations of the study include the small sample size (86 students) and lack of control of participant composition. FINDINGS: Students reacted very favorably in rating their experience using the intervention. Overall, the multiple-monitor group had a slight performance improvement. Most improvement was in software-design and graphics assignments. Performance increased statistically significantly on the interfaces-and-hierarchies labs. Students used multiple-monitors in different ways including reference guides, assignment specifications, and more.
Modeling Protein Using Large-scale Pretrain Language Model

Protein is linked to almost every life process. Therefore, analyzing the biological structure and property of protein sequences is critical to the exploration of life, as well as disease detection and drug discovery. Traditional protein analysis methods tend to be labor-intensive and time-consuming. The emergence of deep learning models makes modeling data patterns in large quantities of data possible. Interdisciplinary researchers have begun to leverage deep learning methods to model large biological datasets, e.g. using long short-term memory and convolutional neural network for protein sequence classification. After millions of years of evolution, evolutionary information is encoded in protein sequences. Inspired by the similarity between natural language and protein sequences, we use large-scale language models to model evolutionary-scale protein sequences, encoding protein biology information in representation. Significant improvements are observed in both token-level and sequence-level tasks, demonstrating that our large-scale model can accurately capture evolution information from pretraining on evolutionary-scale individual sequences. Our code and model are available at https://github.com/THUDM/ProteinLM.