Miracle-dz/UniFAR-FTUs huggingface dataset:
Practical cosmology with lenses

Surveys with submillimetre telescopes are revealing large numbers of gravitationally lensed high-redshift sources. I describe how, in practice, these lensed systems could be simultaneously used to estimate the values of cosmological parameters, test models for the evolution of the distribution of dark-matter haloes and investigate the properties of the source population. Even the existing sample of lenses found with the Herschel Space Observatory is enough to formally rule out the standard models of the evolving population of dark-matter haloes, with the likely explanation a combination of baryon physics and the perturbation by infalling baryons of the density distribution of dark matter at the centres of the haloes. Independently of the evolution of the haloes, observations of a sample of 100 lensed systems would be enough to estimate ΩΛ with a precision of 5 per cent and observations of 1000 lenses would be enough to estimate w, the parameter in the equation of state of dark energy, with a precision similar to that obtained from the Planck observations of the cosmic microwave background. While the fraction of submillimetre sources that are lensed depends weakly on the specific halo mass function that is used in the model, it depends very strongly on the evolution of the submillimetre luminosity function of the source population. Therefore measurements of the lensing fraction could be used to investigate galaxy evolution in a way that is independent of the properties of the intervening haloes.
Sutured annular Khovanov-Rozansky homology

We introduce an sl(n) homology theory for knots and links in the thickened annulus. To do so, we first give a fresh perspective on sutured annular Khovanov homology, showing that its definition follows naturally from trace decategorifications of enhanced sl(2) foams and categorified quantum gl(m), via classical skew Howe duality. This framework then extends to give our annular sl(n) link homology theory, which we call sutured annular Khovanov-Rozansky homology. We show that the sl(n) sutured annular Khovanov-Rozansky homology of an annular link carries an action of the Lie algebra sl(n), which in the n=2 case recovers a result of Grigsby-Licata-Wehrli.
Understanding Adversarial Examples Through Deep Neural Network's Classification Boundary and Uncertainty Regions

Although AI is developing rapidly, AI’s vulnerability under adversarial attacks remains an extraordinarily difficult problem. In this paper we study the root cause of adversarial examples through studying the deep neural network’s (DNN) classification boundary. The existing attack algorithms can generate from a handful to a few hundred adversarial examples given one clean sample. We show there are a lot more adversarial examples given one clean sample, all within a small neighborhood of the clean sample. We then define DNN uncertainty regions and show the transferability of adversarial examples is not universal. The results lead to two conjectures regarding the size of the DNN uncertainty regions and where DNN function becomes discontinuous. The conjectures offer a potential explanation for why the generalization error bound – the theoretical guarantee established for DNN – cannot adequately capture the phenomenon of adversarial examples.
Evaluating Neural Multi-Field Document Representations for Patent Classification

Patent classification constitutes a long-tailed hierarchical learning problem. Prior work has demonstrated the efficacy of neural representations based on pre-trained transformers, however, due to the limited input size of these models, using only title and abstract of patents as input. Patent documents consist of several textual fields, some of which are quite long. We show that a baseline using simple tf.idf-based methods can easily leverage this additional information. We propose a new architecture combining the neural transformer-based representations of the various fields into a meta-embedding, which we demonstrate to outperform the tf.idf-based counterparts especially on less frequent classes. Using a relatively simple architecture, we outperform the previous state of the art on CPC classification by a margin of 1.2 macro-avg. F1 and 2.6 micro-avg. F1. We identify the textual field giving a “brief-summary” of the patent as most informative with regard to CPC classification, which points to interesting future directions of research on less computation-intensive models, e.g., by summarizing long documents before neural classification.
FASED: A Framework for Data Ecosystems Health Evaluation

The growing availability of data in digital media has contributed to the creation of a large number of data ecosystems. However, having successful Data Ecosystem is still a challenge. In order to prevent the failure of a Data Ecosystem and ensure its survival, evaluating its health becomes fundamental. In a general way, the health of a Data Ecosystem can be defined as its ability to grow and survive over time. Indicators such as productivity, robustness, niche creation and sustainability can be employed to evaluate the health of a Data Ecosystem. In this paper, we propose a framework for data Ecosystem health evaluation composed of a set of indicators and metrics, which assess the Data Ecosystem’s current state and its ability to stay healthy over time. The results obtained when using the proposed framework offers evidence to assist in decision making on how data has being published and consumed in a Data Ecosystem, as well as to evaluate which ecosystems are more prosperous or need more investments.
EPK: Scalable and Efficient Memory Protection Keys

As a hardware mechanism for facilitating intra-process memory isolation, Intel Memory Protection Keys (MPK) has been leveraged to efﬁciently improve the isolation, security, or performance of the software. However, it can only support 16 isolated memory domains, which signiﬁcantly limits its applicability in many scenarios. In this paper, we present EPK which leverages off-the-shelf virtualization hardware features to extend the number of available protection domains in MPK. To demonstrate the effectiveness of EPK, we apply it in three scenarios, including better memory isolation for server applications as well as Non-Volatile Memory (NVM) applications, and a fast Inter-Process Communication (IPC) mechanism for microkernels. The evaluation results show that EPK can scale to provide hundreds of isolated domains. It can outperform the state-of-the-art (libmpk) by up to two orders of magnitude and usually achieve 95% of the performance of the system with no memory isolation.
An introduction to numerical analysis

Numerical analysis provides the theoretical foundation for the numerical algorithms we rely on to solve a multitude of computational problems in science. Based on a successful course at Oxford University, this book covers a wide range of such problems ranging from the approximation of functions and integrals to the approximate solution of algebraic, transcendental, differential and integral equations. Throughout the book, particular attention is paid to the essential qualities of a numerical algorithm - stability, accuracy, reliability and efficiency. The authors go further than simply providing recipes for solving computational problems. They carefully analyse the reasons why methods might fail to give accurate answers, or why one method might return an answer in seconds while another would take billions of years. This book is ideal as a text for students in the second year of a university mathematics course. It combines practicality regarding applications with consistently high standards of rigour.
Serving Heterogeneous Machine Learning Models on Multi-GPU Servers with Spatio-Temporal Sharing

As machine learning (ML) techniques are applied to a widening range of applications, high throughput ML inference serving has become critical for online services. Such ML inference servers with multiple GPUs pose new challenges in the scheduler design. First, they must provide a bounded latency for each request to support a consistent service-level objective (SLO). Second, they must be able to serve multiple heterogeneous ML models in a system, as cloud-based consolidation improves system utilization. To address the two requirements of ML inference servers, this paper proposes a new inference scheduling framework for multi-model ML inference servers. The paper shows that with SLO constraints, GPUs with growing parallelism are not fully utilized for ML inference tasks. To maximize the resource efﬁciency of GPUs, a key mechanism proposed in this paper is to exploit hardware support for spatial partitioning of GPU resources. With spatio-temporal sharing, a new abstraction layer of GPU resources is created with conﬁgurable GPU resources. The scheduler assigns requests to virtual GPUs, called gpulets , with the most effective amount of resources. The scheduler explores the three-dimensional search space with different batch sizes, temporal sharing, and spatial sharing efﬁciently. To minimize the cost for cloud-based inference servers, the framework auto-scales the required number of GPUs for a given workload. To consider the potential interference overheads when two ML tasks are running concurrently by spatially sharing a GPU, the scheduling decision is made with an interference prediction model. Our prototype implementation proves that the proposed spatio-temporal scheduling enhances throughput by 61.7% on average compared to the prior temporal scheduler, Abstract To maximize the resource efﬁciency of inference servers, we proposed a key mechanism to exploit hardware support for spatial partitioning of GPU resources. With the partitioning mechanism, a new abstraction layer of GPU resources is created with conﬁgurable GPU resources. The scheduler assigns requests to virtual GPUs, called gpulets, with the most effective amount of resources. The prototype framework auto-scales the required number of GPUs for a given workloads, minimizing the cost for cloud-based inference servers. The prototype framework also deploys a remedy for potential interference effects when two ML tasks are running concurrently in a GPU.
Diversifying Sentiments in News Recommendation

Personalized news recommender systems are widely deployed to filter the information overload caused by the sheer amount of news produced daily. Recommended news articles usually have a sentiment similar to the sentiment orientation of the previously consumed news, creating a self-reinforcing cycle of sentiment chambers around people. Wu et al. introduced SentiRec – a sentiment diversity-aware neural news recommendation model to counter this lack of diversity. In this work, we reproduce SentiRec without access to the original source code and data sample. We re-implement SentiRec from scratch and use the Microsoft MIND dataset (same source but different subset as in the original work) for our experiments. We evaluate and discuss our reproduction from different perspectives. While the original paper mainly has a user-centric perspective on sentiment diversity by comparing the recommendation list to the user’s interaction history, we also analyze the intra-list sentiment diversity of the recommendation list. Additionally, we study the effect of sentiment diversification on topical diversity. Our results suggest that SentiRec does not generalize well to other data since the compared baselines already perform well, opposing the original work’s findings. While the original SentiRec utilizes a rule-based sentiment analyzer, we also study a pre-trained neural sentiment analyzer. However, we observe no improvements in effectiveness nor in sentiment diversity. To foster reproducibility, we make our source code publicly available.
First-principles study on the adsorption of NO2 gas on Al-doped ZnO (001) and (100) surfaces

Abstract The structure model of Al-doped ZnO (001) and (100) surface adsorbing NO2 is constructed. Based on the first-principles method of density functional theory, the energy band, adsorption energy and density of state are calculated using the Castep software package. The Al doping significantly increases the energy and adsorption energy of ZnO(001) and ZnO(100) crystal plane systems, and promotes the transfer of charge from NO2 to ZnO(001) and ZnO(100) surface, which promotes the Fermi energy level to move to the high energy potential, resulting in the decrease of the band gap width, thereby enhancing the adsorption capacity of NO2 gas molecules.
EFFECTIVE INNER RADIUS OF TILTED BLACK HOLE ACCRETION DISKS

One of the primary means of determining the spin a of an astrophysical black hole is by actually measuring the inner radius rin of a surrounding accretion disk and using that to infer a. By comparing a number of different estimates of rin from simulations of tilted accretion disks with differing black hole spins, we show that such a procedure can give quite wrong answers. Over the range 0 ⩽ a/M ⩽ 0.9, we find that, for moderately thick disks (H/r ∼ 0.2) with modest tilt (15°), rin is nearly independent of spin. This result is likely dependent on tilt, such that for larger tilts, it may even be that rin would increase with increasing spin. In the opposite limit, we confirm through numerical simulations of untilted disks that, in the limit of zero tilt, rin recovers approximately the expected dependence on a.
Development of bifunctional reagent based on porphyrinized gold nanoparticles for biology application

Abstract Gold nanoparticles (GNPs) are increasingly received great attentions in the field of biomedical as it exhibits extraordinary unique optical and electrochemical performances. This study mainly presented that synthesis of meso-tetra(p-hydroxyphenyl)porphyrin(THPP) through Alder-Longo method undergoing modification by chloroacetyl chloride coupling with cysteine to obtain tetrakis (p-mercaptophenyl) porphyrin(TMPP), and finally covalently linked with GNPs to gain porphyrinized gold nanoparticles (PGNPs) conjugate. Then, the antiHSA decorated PGNPs can be practically utilized for immunoassay of human serum albumin(HSA). Results illustrated that we successfully synthesized PGNPs that were confirmed by UV-vis, Fluorescence spectrum, Raman spectrum, XPS, TG and TEM/SEM analysis. The prepared PGNPs nanocomposite will aggregate in DMSO, DMF, CH3OH, H2O, EtOH or in specific PBS solution with a size of approximately 147.3 nm. The constructed immunoassay strategy for HSA analysis indicated that there have function relationships of y = 0.1299x+ 0.0744 (R2=0.85) and y = 12.167x + 21.097(R2=0.31) with detection limit of 4.0 ng/mL from UV-vis absorbance values and 20.0 ng/mL from fluorescence intensity, respectively. The prepared PGNPs and proposed immunoassay using bifunctional reagent that it can upgrade at least 4 times accuracy for HSA detection opened a strategy for biological analysis.
Soft-Collinear Factorization and Zero-Bin Subtractions

We study the Sudakov form factor for a spontaneously broken gauge theory using a (new) {delta}-regulator. To be well defined, the effective theory requires zero-bin subtractions for the collinear sectors. The zero-bin subtractions depend on the gauge boson mass M and are not scaleless. They have both finite and 1/{epsilon} contributions and are needed to give the correct anomalous dimension and low-scale matching contributions. We also demonstrate the necessity of zero-bin subtractions for soft-collinear factorization. We find that after zero-bin subtractions the form factor is the sum of the collinear contributions minus a soft mass-mode contribution, in agreement with a previous result of Idilbi and Mehen in QCD. This appears to conflict with the method-of-regions approach, where one gets the sum of contributions from different regions.
CBD resistant Salmonella strains are susceptible to epsilon 34 phage tailspike protein

The rise of antimicrobial resistance is a global public health crisis that threatens the effective control and prevention of infections. Due to the emergence of pandrug-resistant bacteria, most antibiotics have lost their efficacy. Meanwhile, the development of new antimicrobials has stagnated, which leads to the creation of new and unconventional treatments. Bacteriophages or their components are known to target bacterial cell walls, cell membranes, and lipopolysaccharides (LPS) and hydrolyze them. Bacteriophages being the natural predators of pathogenic bacteria, are inevitably categorized as “human friends”, thus fulfilling the adage that “the enemy of my enemy is my friend”. Leveraging on their lethal capabilities against pathogenic bacteria, researchers are searching for more ways to overcome the current antibiotic resistance challenge. Bacteriophages are considered to be one of the most effective alternative therapies for multidrug resistant bacteria. In this study, we expressed and purified epsilon 34 phage tailspike protein (E34 TSP) from the E34 TSP gene which was previously cloned into a pET30a-LIC vector, then assessed the ability of this bacteriophage protein in the killing of two CBD-resistant strains of Salmonella spp. We observed that the combined treatment of CBD-resistant strains of Salmonella with CBD and E34 TSP showed poor killing ability whereas the monotreatment with E34 TSP showed considerably higher killing efficiency.
EquiFold: Protein Structure Prediction with a Novel Coarse-Grained Structure Representation

Designing proteins to achieve specific functions often requires in silico modeling of their properties at high throughput scale and can significantly benefit from fast and accurate protein structure prediction. We introduce EquiFold, a new end-to-end differentiable, SE(3)-equivariant, all-atom protein structure prediction model. EquiFold uses a novel coarse-grained representation of protein structures that does not require multiple sequence alignments or protein language model embeddings, inputs that are commonly used in other state-of-the-art structure prediction models. Our method relies on geometrical structure representation and is substantially smaller than prior state-of-the-art models. In preliminary studies, EquiFold achieved comparable accuracy to AlphaFold but was orders of magnitude faster. The combination of high speed and accuracy make EquiFold suitable for a number of downstream tasks, including protein property prediction and design.
Inflammation of the retinal pigment epithelium drives early-onset photoreceptor degeneration in Mertk-associated retinitis pigmentosa

Severe, early-onset photoreceptor (PR) degeneration associated with MERTK mutations is thought to result from failed phagocytosis by retinal pigment epithelium (RPE). Notwithstanding, the severity and onset of PR degeneration in mouse models of Mertk ablation is determined by the hypomorphic expression or the loss of the Mertk paralog Tyro3. Here we find that loss of Mertk and reduced expression/loss of Tyro3 led to RPE inflammation even before eye-opening. Incipient RPE inflammation cascaded to involve microglia activation and PR degeneration with monocyte infiltration. Inhibition of RPE inflammation with the JAK1/2 inhibitor ruxolitinib mitigated PR degeneration in Mertk-/- mice. Neither inflammation nor severe, early-onset PR degeneration were observed in mice with defective phagocytosis alone. Thus, inflammation drives severe, early-onset PR degeneration-associated with Mertk loss of function.
Impact of Holocene environmental change on the evolutionary ecology of an Arctic top predator

The Arctic is among the most climatically sensitive environments on Earth, and the disappearance of multiyear sea-ice in the Arctic Ocean is predicted within decades. As apex predators, polar bears are sentinel species for addressing the impact of environmental variability on Arctic marine ecosystems. By integrating genomics, isotopic analysis, morphometrics, and ecological modelling, we investigate how Holocene environmental changes affected the evolutionary ecology of polar bears around Greenland. We show that throughout the last ∼11,000 years, Greenlandic polar bears have been heavily influenced by changes in sea-surface temperature (SST) and sea-ice cover. Most notable are major reductions in effective population size at the beginning of the Holocene and during the Holocene Thermal Maximum ∼6 kya, which coincide with increases in annual mean SST, reduction in sea-ice covers, declines in suitable habitat, and shifts in suitable habitat northwards. Furthermore, we show how individuals sampled from west and east Greenland are genetically, morphologically, and ecologically distinct. We find bears sampled in west Greenland to be larger, more genetically diverse and have diets dominated by ringed seals, whereas bears from east Greenland are smaller and less diverse with more varied diets, putatively driven by regional biotic differences. Taken together, we provide novel insights into the vulnerability of polar bears to environmental change, and how the Arctic marine ecosystem plays a vital role in shaping the evolutionary and ecological trajectories of its inhabitants. Teaser Multivariate investigations of the environment’s role in the evolutionary ecology of Greenlandic polar bears.
Drug repurposing screens identify compounds that inhibit α-synuclein oligomers' membrane disruption and block antibody interactions

Small soluble oligomers of the protein α-synuclein (αSO) have been linked to disruptions in neuronal homeostasis, contributing to the development of Parkinson's Disease (PD). While this makes αSO an obvious drug target, the development of effective therapeutics against αSO is challenged by its low abundance and structural and morphological complexity. Here, we employ two different approaches to neutralize toxic interactions made by αSOs with different cellular components. First, we use available data to identify four neuronal proteins as likely candidates for αSO interactions, namely Cfl1, Uchl1, Sirt2 and SerRS. However, despite promising results when immobilized, all 4 proteins only bind weakly to αSO in solution in microfluidic assays, making them inappropriate for screening. In contrast, the formation of stable contacts formed between αSO and vesicles consisting of anionic lipids not only mimics a likely biological role of αSO but also provided a platform to screen two small molecule libraries for disruptors of these contacts. Of the 7 best leads obtained in this way, 2 significantly impaired αSO contacts with other proteins in a sandwich ELISA assay using αSO-binding monoclonal antibodies and nanobodies. In addition, 5 of these leads suppressed α-synuclein amyloid formation. Thus, a repurposing screening that directly targets a key culprit in PD pathogenesis shows therapeutic potential.