Miracle-dz/UniFAR-FTUs huggingface dataset:
Electronic and structural properties of crystalline and amorphous (TaNbHfTiZr)C from first principles

High entropy materials (HEMs) are of great interest for their mechanical, chemical and electronic properties. In this paper we analyse (TaNbHfTiZr)C, a carbide type of HEM, both in crystalline and amorphous phases, using density functional theory (DFT). We find that the relaxed lattice volume of the amorphous phase is larger, while its bulk modulus is lower, than that of its crystalline counterpart. Both phases are metallic with all the transition metals contributing similarly to the density of states close to the Fermi level, with Ti and Nb giving the proportionally largest contribution of states. We confirm that despite its great structural complexity, 2×2×2 supercells are large enough for reliable simulation of the presented mechanical and electronic properties by DFT.
Standardizing and Centralizing Datasets for Efficient Training of Agricultural Deep Learning Models

In recent years, deep learning models have become the standard for agricultural computer vision. Such models are typically fine-tuned to agricultural tasks using model weights that were originally fit to more general, non-agricultural datasets. This lack of agriculture-specific fine-tuning potentially increases training time and resource use, and decreases model performance, leading to an overall decrease in data efficiency. To overcome this limitation, we collect a wide range of existing public datasets for 3 distinct tasks, standardize them, and construct standard training and evaluation pipelines, providing us with a set of benchmarks and pretrained models. We then conduct a number of experiments using methods that are commonly used in deep learning tasks but unexplored in their domain-specific applications for agriculture. Our experiments guide us in developing a number of approaches to improve data efficiency when training agricultural deep learning models, without large-scale modifications to existing pipelines. Our results demonstrate that even slight training modifications, such as using agricultural pretrained model weights, or adopting specific spatial augmentations into data processing pipelines, can considerably boost model performance and result in shorter convergence time, saving training resources. Furthermore, we find that even models trained on low-quality annotations can produce comparable levels of performance to their high-quality equivalents, suggesting that datasets with poor annotations can still be used for training, expanding the pool of currently available datasets. Our methods are broadly applicable throughout agricultural deep learning and present high potential for substantial data efficiency improvements.
Almost Consistent Systems of Linear Equations

Checking whether a system of linear equations is consistent is a basic computational problem with ubiquitous applications. When dealing with inconsistent systems, one may seek an assignment that minimizes the number of unsatisfied equations. This problem is NP-hard and UGC-hard to approximate within any constant even for two-variable equations over the two-element field. We study this problem from the point of view of parameterized complexity, with the parameter being the number of unsatisfied equations. We consider equations defined over Euclidean domains - a family of commutative rings that generalize finite and infinite fields including the rationals, the ring of integers, and many other structures. We show that if every equation contains at most two variables, the problem is fixed-parameter tractable. This generalizes many eminent graph separation problems such as Bipartization, Multiway Cut and Multicut parameterized by the size of the cutset. To complement this, we show that the problem is W[1]-hard when three or more variables are allowed in an equation, as well as for many commutative rings that are not Euclidean domains. On the technical side, we introduce the notion of important balanced subgraphs, generalizing important separators of Marx [Theor. Comput. Sci. 2006] to the setting of biased graphs. Furthermore, we use recent results on parameterized MinCSP [Kim et al., SODA 2021] to efficiently solve a generalization of Multicut with disjunctive cut requests.
More Effort Towards Multiagent Knapsack

In this paper, we study some multiagent variants of the knapsack problem. Fluschnik et al. [AAAI 2019] considered the model in which every agent assigns some utility to every item. They studied three preference aggregation rules for finding a subset (knapsack) of items: individually best, diverse, and Nash-welfare-based. Informally, diversity is achieved by satisfying as many voters as possible. Motivated by the application of aggregation operators in multiwinner elections, we extend the study from diverse aggregation rule to Median and Best scoring functions. We study the computational and parameterized complexity of the problem with respect to some natural parameters, namely, the number of voters, the number of items, and the distance from an easy instance. We also study the complexity of the problem under domain restrictions. Furthermore, we present significantly faster parameterized algorithms with respect to the number of voters for the diverse aggregation rule.
Privacy Safe Representation Learning via Frequency Filtering Encoder

Deep learning models are increasingly deployed in real-world applications. These models are often deployed on the server-side and receive user data in an information-rich representation to solve a specific task, such as image classification. Since images can contain sensitive information, which users might not be willing to share, privacy protection becomes increasingly important. Adversarial Representation Learning (ARL) is a common approach to train an encoder that runs on the client-side and obfuscates an image. It is assumed, that the obfuscated image can safely be transmitted and used for the task on the server without privacy concerns. However, in this work, we find that training a reconstruction attacker can successfully recover the original image of existing ARL methods. To this end, we introduce a novel ARL method enhanced through low-pass filtering, limiting the available information amount to be encoded in the frequency domain. Our experimental results reveal that our approach withstands reconstruction attacks while outperforming previous state-of-the-art methods regarding the privacy-utility trade-off. We further conduct a user study to qualitatively assess our defense of the reconstruction attack.
Information Flow Control-by-Construction for an Object-Oriented Language Using Type Modifiers

In security-critical software applications, confidential information must be prevented from leaking to unauthorized sinks. Static analysis techniques are widespread to enforce a secure information flow by checking a program after construction. A drawback of these systems is that incomplete programs during construction cannot be checked properly. The user is not guided to a secure program by most systems. We introduce IFbCOO, an approach that guides users incrementally to a secure implementation by using refinement rules. In each refinement step, confidentiality or integrity (or both) is guaranteed alongside the functional correctness of the program, such that insecure programs are declined by construction. In this work, we formalize IFbCOO and prove soundness of the refinement rules. We implement IFbCOO in the tool CorC and conduct a feasibility study by successfully implementing case studies.
Twitter Attribute Classification With Q-Learning on Bitcoin Price Prediction

Bitcoin price prediction based on people’s opinions on Twitter usually requires millions of tweets, using different text mining techniques, and developing a machine learning model to perform the prediction. These attempts lead to the employment of a significant amount of computer power, central processing unit (CPU) utilization, random-access memory (RAM) usage, and time. To address this issue, in this paper, we consider a classification of tweet attributes that effects on price changes and computer resource usage levels while obtaining an accurate price prediction. To classify tweet attributes having a high effect on price movement, we collect all Bitcoin-related tweets posted in a certain period and divide them into four categories based on the following tweet attributes: <inline-formula> <tex-math notation="LaTeX">$(i)$ </tex-math></inline-formula> the number of followers of the tweet poster, <inline-formula> <tex-math notation="LaTeX">$(ii)$ </tex-math></inline-formula> the number of comments on the tweet, <inline-formula> <tex-math notation="LaTeX">$(iii)$ </tex-math></inline-formula> the number of likes, and <inline-formula> <tex-math notation="LaTeX">$(iv)$ </tex-math></inline-formula> the number of retweets. We separately train and test by using the Q-learning model with the above four categorized sets of tweets and find the best accurate prediction among them. We compare our approach with a classic approach where all Bitcoin-related tweets are used as input data for the model, by analyzing the CPU workloads, RAM usage, memory, time, and prediction accuracy. The results show that tweets posted by users with the most followers have the most influence on a future price, and their utilization leads to spending 80% less time, 88.8% less CPU consumption, and 12.5% more accurate predictions compared with the classic approach.
Energy Transport during 3D Small-scale Reconnection Driven by Anisotropic Plasma Turbulence

Energy dissipation in collisionless plasmas is a long-standing fundamental physics problem. Although it is well known that magnetic reconnection and turbulence are coupled and transport energy from system-size scales to subproton scales, the details of the energy distribution and energy dissipation channels remain poorly understood. Especially, the energy transfer and transport associated with 3D small-scale reconnection that occurs as a consequence of a turbulent cascade is unknown. We use an explicit fully kinetic particle-in-cell code to simulate 3D small-scale magnetic reconnection events forming in anisotropic and decaying Alfvénic turbulence. We identify a highly dynamic and asymmetric reconnection event that involves two reconnecting flux ropes. We use a two-fluid approach based on the Boltzmann equation to study the spatial energy transfer associated with the reconnection event and compare the power density terms in the two-fluid energy equations with standard energy-based damping, heating, and dissipation proxies. Our findings suggest that the electron bulk flow transports thermal energy density more efficiently than kinetic energy density. Moreover, in our turbulent reconnection event, the energy density transfer is dominated by plasma compression. This is consistent with turbulent current sheets and turbulent reconnection events, but not with laminar reconnection.
Use of the 6-Min Walk Distance to IdentifyVariations in Treatment Benefits FromImplantable Cardioverter-Defibrillator andAmiodarone

Use of the 6-Min Walk Distance to Identify Variations in Treatment Benefits From Implantable Cardioverter-Defibrillator and Amiodarone Results From the SCD-HeFT (Sudden Cardiac Death in Heart Failure Trial) Daniel P. Fishbein, MD,* Anne S. Hellkamp, MS,y Daniel B. Mark, MD, MPH,z Mary Norine Walsh, MD,x Jeanne E. Poole, MD,* Jill Anderson, BSN, RN,k George Johnson, BSEE,k Kerry L. Lee, PHD,y Gust H. Bardy, MD,*k for the SCD-HeFT Investigators
The temporal logic of programs

A unified approach to program verification is suggested, which applies to both sequential and parallel programs. The main proof method suggested is that of temporal reasoning in which the time dependence of events is the basic concept. Two formal systems are presented for providing a basis for temporal reasoning. One forms a formalization of the method of intermittent assertions, while the other is an adaptation of the tense logic system Kb, and is particularly suitable for reasoning about concurrent programs.
CLINICAL CASE SEMINAR Hormonal Manipulation of Benign Metastasizing Leiomyomas: Report of Two Cases and Review of the Literature

Benign metastasizing leiomyomas (BMLs) occur predomi- nantly in women during reproductive years. The condition is characterized by uterine leiomyomas associated with the de- velopment, typically years later, of slow-growing metastatic lesions. The most commonly affected organs are the lungs, but BMLs have been reported in lymph nodes, deep soft tissues, mesentery, bones, the central nervous system, and the heart. In many cases, these lesions have an indolent course and are discovered rather incidentally. However, occasionally they can present with debilitating symptoms or even life-threat-ening complications. The presence of estrogen and progesterone receptors in these tumors supports their origin from uter- ine smooth muscle and, more importantly, makes them amenable to hormonal manipulation. Radical interventions, such as extensive tumor debulking and oophorectomy for hormonal control, although effective in many cases, are not al- ways possible or desirable and carry significant morbidity. Here we present two cases of BMLs to illustrate the role of newer therapeutic agents, the estrogen receptor modulators and the aromatase inhibitors, in the hormonal manipulation of these tumors. ( J Clin Endocrinol Metab 89: 3183–3188, 2004)
de-Green Light for Traffic in the Early Secretory Pathway

The endomembrane system, which includes the endoplasmic reticulum (ER), the Golgi apparatus, vacuoles, and associated vesicles, functions in the biosynthesis and transport of lipids, proteins, and cell wall polysaccharides destined for various locations within the cell, the maintenance of cell integrity, and the detoxification of foreign molecules that gain entry into the cell. In fact, this pathway plays a major role in feeding the world through the synthesis of seed storage proteins (Vitale and Denecke, 1999). Anterograde traffic in the pathway begins with protein synthesis: nascent proteins destined for secretion are delivered to the ER and then transported to the Golgi apparatus, where they are further sorted for translocation to the cell exterior, or to cellular compartments such as protein storage vacuoles, lytic vacuoles, the plasma membrane, or the cell wall. Retrograde traffic, which runs in the opposite direction, allows for endocytosis of extracellular molecules and for recycling of membranes and proteins to maintain the integrity of the various cellular compartments (Vitale and Denecke, 1999). The ER is the largest membrane system in eukaryotes, consisting of highly invaginated tubules and flattened cisternae connected to ribosomes (rough ER) and a network of numerous extensions and vesicles that connect to the nuclear envelope and the Golgi apparatus, and extend through plasmodesmata to neighboring cells. The Golgi apparatus consists of stacks of individual cisternae, flattened organelles with bulbous ends that are connected by a network of tubules. Both the number of cisternae within a stack and the number of stacks per cell can vary considerably between cell types and genera, and changes also may be triggered by deGreen Light for Traffic in the Early Secretory Pathway
Running head: rough sheath1 enhances knotted1 phenotypes

The knotted1 ( kn1 ) homeobox ( knox ) gene rough sheath1 plays a critical, dosage-dependent role in shoot apical meristem establishment and maintenance, stem elongation and inflorescence development that is only uncovered in a knotted1 mutant background. ABSTRACT The knotted1 ( kn1 ) homeobox ( knox ) gene family was first identified through gain-of-function dominant mutants in maize. Class I knox members are expressed in meristems but excluded from leaves. In maize, a loss-of-function phenotype has only been characterized for kn1 . To assess the function of another knox member, we characterized a loss-of-function mutation of rough sheath1 ( rs1 ). rs1-mum1 has no phenotype alone but exacerbates several aspects of the kn1 phenotype. In permissive backgrounds in which kn1 mutants grow to maturity, loss of a single copy of rs1 enhances the tassel branch reduction phenotype while loss of both copies results in limited shoots. In less introgressed lines, double mutants can grow to maturity but are shorter. Using a KNOX antibody, we demonstrate that RS1 binds in vivo to some of the KN1 target genes, which could partially explain why KN1 binds many genes, but modulates few. Our results demonstrate an unequal redundancy between knox genes, with a role for rs1 only revealed in the complete absence of kn1 .
Relaxin Stimulates Protein Kinase C (cid:1) Translocation: Requirement for Cyclic Adenosine 3 (cid:1) ,5 (cid:1) Monophosphate Production

Relaxin is a polypeptide hormone that activates the leucine-rich repeat containing G protein-coupled receptors, LGR7 and LGR8. In an earlier study, we reported that relaxin produces a biphasic time course and the second wave of cAMP is highly sensitive to phosphoinositide-3 kinase inhibitors (LY294002 and wortmannin). LY294002 inhibits relaxin-mediated increases in cAMP production by 40–50% across a large range of relaxin concentrations. Here we show that protein kinase C (cid:1) (PKC (cid:1) ) is a component of relaxin signaling in THP-1 cells. Sphingomyelinase increases cAMP production due to the release of ceramide, a direct activator of PKC (cid:1) . Chelerythrine chloride (a general PKC inhibitor) inhibits relaxin induced cAMP production to the same degree ( (cid:1) 40%) as LY294002. Relaxin stimulates PKC (cid:1) translocation to the plasma membrane in THP-1, MCF-7, pregnant human myometrial 1–31, and mouse mesangial cells, as shown by immunocytochemistry. PKC (cid:1) translocation is phosphoinositide-3 kinase dependent and independent of cAMP production. Antisense PKC (cid:1) oligodeoxynucleotides (PKC (cid:1) -ODNs) deplete both PKC (cid:1) transcript and protein levels in THP-1 cells. PKC (cid:1) -ODNs abolish relaxin-mediated PKC (cid:1) translocation and inhibit relaxin stimulation of cAMP by 40%, as compared with mock and random ODN controls. Treatment with LY294002 in the presence of PKC (cid:1) -ODNs results in little further inhibition. In summary, we present a novel role for PKC (cid:1) in relaxin-mediated stimulation of cAMP. ( Molecular Endocrinology 19: 1012–1023, 2005)
Research Online Research Online Data Fusion for MaaS: Opportunities and Challenges Data Fusion for MaaS: Opportunities and Challenges

— Computer Supported Cooperative Work (CSCW) in design is an essential facilitator for the development and implementation of smart cities, where modern cooperative transportation and integrated mobility are highly demanded. Owing to greater availability of different data sources, data fusion problem in intelligent transportation systems (ITS) has been very challenging, where machine learning modelling and approaches are promising to offer an important yet comprehensive solution. In this paper, we provide an overview of the recent advances in data fusion for Mobility as a Service (MaaS), including the basics of data fusion theory and the related machine learning methods. We also highlight the opportunities and challenges on MaaS, and discuss potential future directions of research on the integrated mobility modelling.
Title: Co-Infections of Two Strains of NDM-1 and OXA-232 Co-producing Klebsiella 1 pneumoniae in a Kidney Transplant Patient 2

: We report here a fatal case of carbapenem-resistant Klebsiella pneumoniae (CRKP) infections 17 in a renal transplant patient without travel history in the past year, from whom 2 genetically different 18 CRKP (ST14 and ST2497) strains carrying the same plasmids and anti-microbial resistance genes 19 including bla NDM-1 , bla OXA-232 , bla CTX-M-15 , armA and tet (D) were isolated from blood and abdominal cavity. 20 The isolates were susceptible to colistin, tigecycline, eravacycline and cefiderocol, which was used to 21 treat the CRKP in combination with ceftazidime/avibactam and polymyxin B and resulted in bacterial 22 clearance. Despite the aggressive treatment, the patient died of ischemic colitis and multi-organ failure.
Degradome Products of the Matricellular Protein CCN1 as Modulators of Pathological Angiogenesis in the Retina*

From SUNY Eye Institute and Department of Cell Biology, Department of Ophthalmology, Downstate Medical Center, Brooklyn, New York 11203 Department of Biochemistry and Molecular Genetics, University of Illinois at Chicago College of Medicine, Chicago, IL 60607 Depts of Pharmacology and Therapeutics, Physiology and Functional Genomics and Ophthalmology, University of Florida, Gainesville, Florida 32610
Current Perception Threshold Testing in Pharyngeal Paresthesia Patients with Depression or Anxiety

Purpose: Satisfactory quantitative diagnostic approaches to pharyngeal paresthesia patients with depression or anxiety remain to be explored. This study investigated the plausibility of current perception threshold (CPT) testing in diagnosing pharyngeal paresthesia in patients with depression or anxiety. Patients and Methods: A total of 41 patients with pharyngeal paresthesia with depression or anxiety were recruited as the study group. Additionally, 60 healthy volunteers constituted the control group. The CPT values associated with 5-, 250-, and 2000-Hz electrical stimulation frequencies were measured at the palatal lingual arch and posterior third of the lingual body (two sensory nerve distribution sites in the pharynx). The normal range of CPT values of the above three frequencies was analyzed. The differences in the CPT values for sensory nerves were compared. Results: There were no signi ﬁ cant differences in age and sex between the study and control groups. The CPT values of the pharynx at the two tested sites were not signi ﬁ cantly correlated with age and gender. The CPT value of the study group was signi ﬁ cantly lower than that of the control group in the palatal lingual arch and posterior third of the lingual body at an electrical stimulation of 5 Hz (p<0.05). No signi ﬁ cant differences in the CPT values at other frequencies were found between the two groups. Conclusion: CPT testing is effective in determining pharyngeal paresthesia in patients with depression and anxiety. Paresthesia of the pharyngeal sensory nerve region is caused by damaged C ﬁ bers.
The Spectrum of a Quasiperiodic Schrδdinger Operator

The spectrum σ(H) of the tight binding Fibonacci Hamiltonian (H mn = <5 m , n + ! + δ m+Un + δ mtΛ μv(n) 9 υ(n) = χ { - ω 3, ω i { ((n - l)ω), 1/ω is the golden number) is shown to coincide with the dynamical spectrum, the set on which an infinite subsequence of traces of transfer matrices is bounded. The point spectrum is absent for any μ, and σ(H) is a Cantor set for |μ| ^ 4. Combining this with Casdagli's earlier result, one finds that the spectrum is singular continuous for |μ| ^ 16.
THEMES Neuroimmune Cross Talk in the Gut Plasticity of neuroeffector transmission during bowel inﬂammation 1

of neuroeffector transmission during bowel inﬂammation. Am J Physiol 312: G165–G170, gas- trointestinal (GI) function contributes to the debilitating symptoms of inﬂammatory bowel diseases (IBD). Nerve circuits contained within the gut wall and outside of the gut play important roles in modulating motility, mucosal ﬂuid transport, and blood ﬂow. The structure and function of these neuronal populations change during IBD. Superimposed on this plasticity is a diminished responsiveness of effector cells — smooth muscle cells, enterocytes, and vascular endothelial cells — to neurotransmitters. The net result is a breakdown in the precisely orchestrated coordination of motility, ﬂuid secretion, and GI blood ﬂow required for health. In this review, we consider how inﬂammation-induced changes to the effector innervation of these tissues, and changes to the tissues themselves, contribute to defective GI function in models of IBD. We also explore the evidence that reversing neuronal plasticity is sufﬁcient to normalize function during IBD.