Miracle-dz/UniFAR-FTUs huggingface dataset:
Beyond Desktop Computation: Challenges in Scaling a GPU Infrastructure

Enterprises and labs performing computationally expensive data science applications sooner or later face the problem of scale but unconnected infrastructure. For this up-scaling process, an IT service provider can be hired or in-house personnel can attempt to implement a software stack. The first option can be quite expensive if it is just about connecting several machines. For the latter option often experience is missing with the data science staff in order to navigate through the software jungle. In this technical report, we illustrate the decision process towards an on-premises infrastructure, our implemented system architecture, and the transformation of the software stack towards a scaleable GPU cluster system.
Towards Sample-efficient Apprenticeship Learning from Suboptimal Demonstration

Learning from Demonstration (LfD) seeks to democratize robotics by enabling non-roboticist end-users to teach robots to perform novel tasks by providing demonstrations. However, as demonstrators are typically non-experts, modern LfD techniques are unable to produce policies much better than the suboptimal demonstration. A previously-proposed framework, SSRR, has shown success in learning from suboptimal demonstration but relies on noise-injected trajectories to infer an idealized reward function. A random approach such as noise-injection to generate trajectories has two key drawbacks: 1) Performance degradation could be random depending on whether the noise is applied to vital states and 2) Noise-injection generated trajectories may have limited suboptimality and therefore will not accurately represent the whole scope of suboptimality. We present Systematic Self-Supervised Reward Regression, S3RR, to investigate systematic alternatives for trajectory degradation. We carry out empirical evaluations and find S3RR can learn comparable or better reward correlation with ground-truth against a state-of-the-art learning from suboptimal demonstration framework.
Recurrent Attention Models with Object-centric Capsule Representation for Multi-object Recognition

The visual system processes a scene using a sequence of selective glimpses, each driven by spatial and object-based attention. These glimpses reflect what is relevant to the ongoing task and are selected through recurrent processing and recognition of the objects in the scene. In contrast, most models treat attention selection and recognition as separate stages in a feedforward process. Here we show that using capsule networks to create an object-centric hidden representation in an encoder-decoder model with iterative glimpse attention yields effective integration of attention and recognition. We evaluate our model on three multi-object recognition tasks; highly overlapping digits, digits among distracting clutter and house numbers, and show that it learns to effectively move its glimpse window, recognize and reconstruct the objects, all with only the classification as supervision. Our work takes a step toward a general architecture for how to integrate recurrent object-centric representation into the planning of attentional glimpses.
The evolution of the barred galaxy population in the TNG50 simulation

We use the magnetic-hydrodynamical simulation TNG50 to study the evolution of barred massive disc galaxies. Massive spiral galaxies are already present as early as $z=4$, and bar formation takes place already at those early times. The bars grow longer and stronger as the host galaxies evolve, with the bar sizes increasing at a pace similar to that of the disc scale lengths. The bar fraction mildly evolves with redshift for galaxies with $M_{*}\geq10^{10}M\odot$, being greater than $\sim40\%$ at $0.5<z<3$ and $\sim30\%$ at $z=0$. When bars larger than a given physical size ($\geq 2\,\rm kpc$) or the angular resolution limit of twice the I-band angular PSF FWHM of the HST are considered, the bar fraction dramatically decreases with increasing redshift, reconciling the theoretical predictions with observational data. We find that barred galaxies have an older stellar population, lower gas fractions and star formation rates than unbarred galaxies. In most cases, the discs of barred galaxies assembled earlier and faster than the discs of unbarred galaxies. We also find that barred galaxies are typical in haloes with larger concentrations and smaller spin parameters than unbarred galaxies. Furthermore, the inner regions of barred galaxies are more baryon-dominated than those of unbarred galaxies but have comparable global stellar mass fractions. Our findings suggest that the bar population could be used as a potential tracer of the buildup of disc galaxies and their host haloes. With this paper, we release a catalogue of barred galaxies in TNG50 at $6$ redshifts between $z=4$ and $z=0$.
CLIP4Caption ++: Multi-CLIP for Video Caption

This report describes our solution to the VALUE Challenge 2021 in the captioning task. Our solution, named CLIP4Caption++, is built on X-Linear/X-Transformer, which is an advanced model with encoder-decoder architecture. We make the following improvements on the proposed CLIP4Caption++: We employ an advanced encoder-decoder model architecture X-Transformer as our main framework and make the following improvements: 1) we utilize three strong pre-trained CLIP models to extract the text-related appearance visual features. 2) we adopt the TSN sampling strategy for data enhancement. 3) we involve the video subtitle information to provide richer semantic information. 3) we introduce the subtitle information, which fuses with the visual features as guidance. 4) we design word-level and sentence-level ensemble strategies. Our proposed method achieves 86.5, 148.4, 64.5 CIDEr scores on VATEX, YC2C, and TVC datasets, respectively, which shows the superior performance of our proposed CLIP4Caption++ on all three datasets.
A Mutual Learning Framework for Few-Shot Sound Event Detection

Although prototypical network (ProtoNet) has proved to be an effective method for few-shot sound event detection, two problems still exist. Firstly, the small-scaled support set is insufficient so that the class prototypes may not represent the class center accurately. Secondly, the feature extractor is task-agnostic (or class-agnostic): the feature extractor is trained with base-class data and directly applied to unseen-class data. To address these issues, we present a novel mutual learning framework with transductive learning, which aims at iteratively updating the class prototypes and feature extractor. More specifically, we propose to update class prototypes with transductive inference to make the class prototypes as close to the true class center as possible. To make the feature extractor to be task-specific, we propose to use the updated class prototypes to fine-tune the feature extractor. After that, a fine-tuned feature extractor further helps produce better class prototypes. Our method achieves the F-score of 38.4% on the DCASE 2021 Task 5 evaluation set, which won the first place in the few-shot bioacoustic event detection task of Detection and Classification of Acoustic Scenes and Events (DCASE) 2021 Challenge.
Akt/mTOR and AMPK signaling pathways are responsible for liver X receptor agonist GW3965-enhanced gefitinib sensitivity in nonsmall cell lung cancer cell lines

Background: This study was to systemically analyze the mechanism of LXR ligand GW3965-induced sensitivity to EGFR-TKI in EGFR-TKI-resistant non-small cell lung cancer (NSCLC) cell lines. Methods: Gefitinib-resistant PC9 cell line (EGFR exon 19 deletion) was treated with single and combined treatment with GW3965 and gefitinib. Cell viability, apoptosis and autophagy were detected using MTT, flow cytometric analysis and immunofluorescent analysis, respectively. Autophagy-related signaling pathways were detected using Western blot analysis. Results: Inhibited cell viability by single and combined treatment with gefitinib and GW3965 were observed. Combined treatment with gefitinib and GW3965 increased LC3 II/I ratio and Beclin 1 expression. Synergistic effect of gefitinib and GW3965 on apoptosis and autophagosome accumulation as well as on the inhibition of Akt/mTOR signaling and activation of AMP-activated protein kinase (AMPK) was observed in gefitinib-resistant PC9 cells. AMPK expression showed similar profile with apoptosis and autophagy of PC9 cells. Conclusions: We confirmed that GW3965 and gefitinib showed synergistic effect on Akt/mTOR inhibition, apoptosis and autophagy of lung cancer cells. Gefitinib sensitivity in PC9 cell line might be mediated by Akt/mTOR, AMPK and JNK signaling pathways.
Contact Angle Measurement of Magnesite Mineral in Alkaline pH by Using the Simple Bubble Attachment Method

Contact angle is one of the most important parameters for the flotation process. Researchers have performed many experiments and used many different methods to determine the contact angle. In this present study, simple bubble attachment method (SBA), which was firstly utilized by Hunning and Rutter (1989), was used to determine the contact angle of magnesite mineral in the presence of various amount of collector such as oleic acid, R801, R845, sodium dodecyl sulphate and sodium oleat. According to the results of SBA, the highest degree of contact angle was obtained as 82ᵒ at the presence of oleic acid. The results of the study show that SBA method seems like a cheap and beneficial method for contact angle measurement.
Improving Distantly-Supervised Named Entity Recognition with Self-Collaborative Denoising Learning

Distantly supervised named entity recognition (DS-NER) efficiently reduces labor costs but meanwhile intrinsically suffers from the label noise due to the strong assumption of distant supervision. Typically, the wrongly labeled instances comprise numbers of incomplete and inaccurate annotations, while most prior denoising works are only concerned with one kind of noise and fail to fully explore useful information in the training set. To address this issue, we propose a robust learning paradigm named Self-Collaborative Denoising Learning (SCDL), which jointly trains two teacher-student networks in a mutually-beneficial manner to iteratively perform noisy label refinery. Each network is designed to exploit reliable labels via self denoising, and two networks communicate with each other to explore unreliable annotations by collaborative denoising. Extensive experimental results on five real-world datasets demonstrate that SCDL is superior to state-of-the-art DS-NER denoising methods.
Graph Neural Networks in Real-Time Fraud Detection with Lambda Architecture

Transaction checkout fraud detection is an essential risk control components for E-commerce marketplaces. In order to leverage graph networks to decrease fraud rate efficiently and guarantee the information flow passed through neighbors only from the past of the checkouts, we first present a novel Directed Dynamic Snapshot (DDS) linkage design for graph construction and a Lambda Neural Networks (LNN) architecture for effective inference with Graph Neural Networks embeddings. Experiments show that our LNN on DDS graph, outperforms baseline models significantly and is computational efficient for real-time fraud detection.
Two-weight dyadic Hardy inequalities

We present various results concerning the two-weight Hardy's inequality on infinite trees. Our main scope is to survey known characterizations (and proofs) for trace measures, as well as to provide some new ones. Also for some of the known characterizations we provide here new proofs. In particular, we obtain a new characterization based on a new reverse H\"older inequality for trace measures, and one based on the well known Muckenhoupt-Wheeden-Wolff inequality, of which we here give a new probabilistic proof. We provide a new direct proof for the so called isocapacitary characterization and a new simple proof, based on a monotonicity argument, for the so called mass-energy characterization. Furthermore, we introduce a conformally invariant version of the two-weight Hardy's inequality, we characterize the compactness of the Hardy operator, we provide a list of open problems and suggest some possible lines of future research.
Role of graphene oxide in mitigated toxicity of heavy metal ions on Daphnia magna

Graphene oxide (GO) is increasingly used and inevitably released into aquatic environments, facilitating its interaction with traditional pollutants such as heavy metal ions. However, the potential effect of GO on the toxicity of heavy metal ions to aquatic animals is unknown. This work aims to assess the toxicity of heavy metal ions (Cu(ii), Cd(ii), and Zn(ii)) on Daphnia magna (D. magna) in the presence of GO. GO nanoparticles remarkably reduced the concentrations of heavy metal ions by adsorption and decreased the metal accumulation in D. magna. The maximum desorption rate of heavy metal ions from metal-adsorbed GO was below 5%. At pH 7.8, with addition of 2 mg L−1 GO, the 72 h median lethal concentration (LC50) values of Cu(ii), Cd(ii), and Zn(ii) were increased from 14.3, 38, and 780 μg L−1 to 36.6, 72, and 1010 μg L−1, respectively. The analyses of oxidative stress indicators suggested that the oxidative damage to D. magna by heavy metal ions was reduced after addition of GO at pH 7.8. Moreover, a higher pH level in the growing range (6.5 to 8.5) of D. magna led to weaker toxicity of metal ions with GO addition due to more adsorption and less bioaccumulation. The results revealed the role of GO nanoparticles in the mitigated toxicity of heavy metal ions in the aquatic environment.
It Is Not As Good As You Think! Evaluating Simultaneous Machine Translation on Interpretation Data

Most existing simultaneous machine translation (SiMT) systems are trained and evaluated on offline translation corpora. We argue that SiMT systems should be trained and tested on real interpretation data. To illustrate this argument, we propose an interpretation test set and conduct a realistic evaluation of SiMT trained on offline translations. Our results, on our test set along with 3 existing smaller scale language pairs, highlight the difference of up-to 13.83 BLEU score when SiMT models are evaluated on translation vs interpretation data. In the absence of interpretation training data, we propose a translation-to-interpretation (T2I) style transfer method which allows converting existing offline translations into interpretation-style data, leading to up-to 2.8 BLEU improvement. However, the evaluation gap remains notable, calling for constructing large-scale interpretation corpora better suited for evaluating and developing SiMT systems.
Value-Function-Based Sequential Minimization for Bi-Level Optimization

Gradient-based Bi-Level Optimization (BLO) methods have been widely applied to handle modern learning tasks. However, most existing strategies are theoretically designed based on restrictive assumptions (e.g., convexity of the lower-level sub-problem), and computationally not applicable for high-dimensional tasks. Moreover, there are almost no gradient-based methods able to solve BLO in those challenging scenarios, such as BLO with functional constraints and pessimistic BLO. In this work, by reformulating BLO into approximated single-level problems, we provide a new algorithm, named Bi-level Value-Function-based Sequential Minimization (BVFSM), to address the above issues. Specifically, BVFSM constructs a series of value-function-based approximations, and thus avoids repeated calculations of recurrent gradient and Hessian inverse required by existing approaches, time-consuming especially for high-dimensional tasks. We also extend BVFSM to address BLO with additional functional constraints. More importantly, BVFSM can be used for the challenging pessimistic BLO, which has never been properly solved before. In theory, we prove the asymptotic convergence of BVFSM on these types of BLO, in which the restrictive lower-level convexity assumption is discarded. To our best knowledge, this is the first gradient-based algorithm that can solve different kinds of BLO (e.g., optimistic, pessimistic, and with constraints) with solid convergence guarantees. Extensive experiments verify the theoretical investigations and demonstrate our superiority on various real-world applications.
Simultaneous cluster structure learning and estimation of heterogeneous graphs for matrix‐variate fMRI data

Graphical models play an important role in neuroscience studies, particularly in brain connectivity analysis. Typically, observations/samples are from several heterogenous groups and the group membership of each observation/sample is unavailable, which poses a great challenge for graph structure learning. In this paper, we propose a method which can achieve Simultaneous Clustering and Estimation of Heterogeneous Graphs (briefly denoted as SCEHG) for matrix‐variate functional magnetic resonance imaging (fMRI) data. Unlike the conventional clustering methods which rely on the mean differences of various groups, the proposed SCEHG method fully exploits the group differences of conditional dependence relationships among brain regions for learning cluster structure. In essence, by constructing individual‐level between‐region network measures, we formulate clustering as penalized regression with grouping and sparsity pursuit, which transforms the unsupervised learning into supervised learning. A modified difference of convex programming with the alternating direction method of multipliers (DC‐ADMM) algorithm is proposed to solve the corresponding optimization problem. We also propose a generalized criterion to specify the number of clusters. Extensive simulation studies illustrate the superiority of the SCEHG method over some state‐of‐the‐art methods in terms of both clustering and graph recovery accuracy. We also apply the SCEHG procedure to analyze fMRI data associated with attention‐deficit hyperactivity disorder (ADHD), which illustrates its empirical usefulness.
Solidified liquid crystals of cellulose with optically variable properties

Carefully to produce a solid film having the optical characteristics in the new colloidal suspension which was prepared by decomposing a crystalline acid hydrolysis of cellulose under conditions controlled microcrystalline cellulose; Solids and have a spiral arrangement of the configuration undetermined; By suitably selecting the manufacturing and processing conditions of the colloidal suspension, to prepare a solid film that reflects visible light polarized in a circle; By controlling the wavelength of the reflection in the visible spectrum to the red to purple, it can be assigned a color from infrared to ultraviolet wavelengths, if necessary. The reflected pearl light color varies with viewing angle occurs from the optical interference effect, and any printing or copying techniques also create the ideally suited material in because they can not reproduce such an effect This optical proved device, all of which are optically in more and other optical interference device because of its properties can be easily distinguished.
Topological Data Analysis (TDA) Techniques Enhance Hand Pose Classification from ECoG Neural Recordings

Electrocorticogram (ECoG) well characterizes hand movement intentions and gestures. In the present work we aim to investigate the possibility to enhance hand pose classification, in a Rock-Paper-Scissor - and Rest - task, by introducing topological descriptors of time series data. We hypothesized that an innovative approach based on topological data analysis can extract hidden information that are not detectable with standard Brain Computer Interface (BCI)techniques. To investigate this hypothesis, we integrate topological features together with power band features and feed them to several standard classifiers, e.g. Random Forest,Gradient Boosting. Model selection is thus completed after a meticulous phase of bayesian hyperparameter optimization. With our method, we observed robust results in terms of ac-curacy for a four-labels classification problem, with limited available data. Through feature importance investigation, we conclude that topological descriptors are able to extract useful discriminative information and provide novel insights.Since our data are restricted to single-patient recordings, generalization might be limited. Nevertheless, our method can be extended and applied to a wide range of neurophysiological recordings and it might be an intriguing point of departure for future studies.
Boosting Room Temperature Tunnel Magnetoresistance in Hybrid Magnetic Tunnel Junctions Under Electric Bias

Spin‐resolved electron symmetry filtering is a key mechanism behind giant tunneling magnetoresistance (TMR) in Fe/MgO/Fe and similar magnetic tunnel junctions (MTJs), providing room temperature functionality in spin electronics. However, the electron symmetry filtering breaks down under applied bias, dramatically reducing the TMR above 0.5 V. This strongly hampers the application range of MTJs. To circumvent the problem, resonant tunneling through quantum well states in thin layers has been used so far. This mechanism, however, is mainly effective at low temperatures. Here, a fundamentally different approach is demonstrated, providing a strong TMR boost under applied bias in V/MgO/Fe/MgO/Fe/Co hybrids. This pathway uses spin orbit coupling (SOC) controlled interfacial states in vanadium, which contrary to the V(001) bulk states are allowed to tunnel to Fe(001) at low biases. The experimentally observed strong increase of TMR with bias is modeled using two nonlinear resistances in series, with the low bias conductance of the first (V/MgO/Fe) element being boosted by the SOC‐controlled interfacial states, while the conductance of the second (Fe/MgO/Fe) junctions are controlled by the relative alignment of the ferromagnetic layers. These results pave a way to unexplored and fundamentally different spintronic device schemes, with tunneling magnetoresistance uplifted under applied electric bias.
Maximal Gap Between Local and Global Distinguishability of Bipartite Quantum States

We prove a tight and close-to-optimal lower bound on the effectiveness of local quantum measurements (without classical communication) at discriminating any two bipartite quantum states. Our result implies, for example, that any two orthogonal quantum states of a <inline-formula> <tex-math notation="LaTeX">$n_{A}\times n_{B}$ </tex-math></inline-formula> bipartite quantum system can be discriminated via local measurements with an error probability no larger than <inline-formula> <tex-math notation="LaTeX">$\frac {1}2 \left ({1 - \frac {1}{c \min \{n_{A}, n_{B}\}} }\right)$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$1\leq c\leq 2\sqrt {2}$ </tex-math></inline-formula> is a universal constant, and our bound scales provably optimally with the local dimensions <inline-formula> <tex-math notation="LaTeX">$n_{A},n_{B}$ </tex-math></inline-formula>. Mathematically, this is achieved by showing that the distinguishability norm <inline-formula> <tex-math notation="LaTeX">$\|\cdot \|_{ \mathrm {LO}}$ </tex-math></inline-formula> associated with local measurements satisfies that <inline-formula> <tex-math notation="LaTeX">$\|\cdot \|_{1}\leq 2\sqrt {2} \min \{n_{A},n_{B}\} \|\cdot \|_{ \mathrm {LO}}$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$\|\cdot \|_{1}$ </tex-math></inline-formula> is the trace norm.
APPROXIMATING FAMILYWISE ERROR RATE FOR CORRELATED NORMAL

We study the limiting behavior of the familywise error rate (FWER) of the Bonferroni procedure in a multiple testing problem. We establish that, in the equicorrelated normal setup, the FWER of Bonferroni’s method tends to zero asymptotically (i.e for a sufficiently large number of hypotheses) for any positive equicorrelation. We extend this result for generalized familywise error rates.