Miracle-dz/UniFAR-FTUs huggingface dataset:
Gordon's inequality and condition numbers in conic optimization

The probabilistic analysis of condition numbers has traditionally been approached from different angles; one is based on Smale's program in complexity theory and features integral geometry, while the other is motivated by geometric functional analysis and makes use of the theory of Gaussian processes. In this note we explore connections between the two approaches in the context of the biconic homogeneous feasiblity problem and the condition numbers motivated by conic optimization theory. Key tools in the analysis are Slepian's and Gordon's comparision inequalities for Gaussian processes, interpreted as monotonicity properties of moment functionals, and their interplay with ideas from conic integral geometry.
Plasma CMV DNA Load Predicts CMV Disease and Survival in AIDS Patients

In this study, baseline plasma from 619 persons with acquired immunodeficiency syndrome (AIDS) (median CD4 1 lymphocyte count 2 21/ m l) who participated in a trial to determine the efficacy of oral ganciclovir for cytomegalovirus (CMV) disease prevention were evaluated for CMV DNA load by qualitative and quantitative polymerase chain reaction (PCR), and correlated with the development of CMV disease and survival. For participants without detectable plasma CMV DNA, the 12-mo Kaplan-Meier CMV disease event rate was 14% and 1% for the placebo and ganciclovir groups, respectively ( P , 0.001). For PCR positive participants, CMV disease developed in 43% of placebo and 26% ganciclovir recipients ( P , 0.017). Among placebo recipients, CMV PCR positivity was associated with a 3.4-fold increased risk of developing CMV disease ( P , 0.001) whereas CD4 1 lymphocyte count was not a useful predictor ( P 5 0.47). A positive plasma CMV DNA PCR was also associated with a 2.5-fold increased risk of death. Each log 10 increase in baseline CMV DNA load was associated with a 3.1-fold increase in CMV disease ( P , 0.001) and a 2.2-fold increase in mortality ( P , 0.001). These data indicate that the risk of developing CMV disease and death in persons with advanced AIDS is directly related to the quantity of CMV DNA in plasma, and is a better predictor than CD4 1 lymphocyte count in this population. ( J. Clin. Invest. 1998. 101:497–502.)
J. (2015). Finite field models in arithmetic combinatorics – ten years on. Finite Fields and Their Applications , 32 , 233-274.

It has been close to ten years since the publication of Green’s inﬂuential survey Finite ﬁeld models in additive combinatorics [ ? ], in which the author championed the use of high-dimensional vector spaces over ﬁnite ﬁelds as a toy model for tackling additive problems concerning the integers. The path laid out by Green has proven to be a very successful one to follow. In the present article we survey the highlights of the past decade and outline the challenges for the years to come.
2D layered MSe2 (M = Hf, Ti and Zr) for compact lasers: nonlinear optical properties and GHz lasing

Abstract Two-dimensional (2D) ternary transition-metal dichalcogenides (TMDCs) are of great research interest because their superior layer-dependent optical modulation properties. In this work, three different kinds of TMDC nanosheets, including hafnium diselenide (HfSe2), titanium diselenide (TiSe2) and zirconium diselenide (ZrSe2), are prepared by liquid phase exfoliation (LPE) technique. The high-quality material properties of these TMDC nanosheets are confirmed by Raman spectroscopy and X-ray diffraction analysis. Furthermore, the bandgap information of five-layer MSe2 has been investigated via utilizing density functional theory. The calculation results exhibit ultra-narrow bandgap structure (lower than 1.1 eV) for all these three materials, indicating that MSe2 is suitable for broadband photonic applications. By applying the fabricated MSe2 as saturable absorbers, high-performance Q-switched mode-locked laser operation has been realized. The laser gain media are Nd:GdVO4 cladding waveguides fabricated by femtosecond laser direct writing. As a result, the pulsed waveguide lasers are able to deliver approximately 6-GHz laser pulses with a signal-to-noise ratio of over 45 dB. The minimum pulse width is determined to be as short as 26 ps. The results demonstrated in this work exhibit the great potential of TMDCs and waveguide structures in applications of pulsed lasers with compact footprints.
Evaluating Performance of Spintronics-Based Spiking Neural Network Chips using Parallel Discrete Event Simulation

It has long been said that neuromorphic computing will yield enormous energy improvements on machine learning based computations and will be part of the next computing revolution. Yet, how likely is it that these goals are met once hardware-level constraints have been accounted for? In this paper, we benchmark the performance of a spintronics hardware platform designed for handling neuromorphic tasks. Spintronics devices that use the spin of electrons as the information state variable have the potential to emulate neuro-synaptic dynamics in hardware. Unlike their CMOS counterparts, spintronics-based neurons and synapses can be realized within a compact form-factor, while operating at ultra-low energy-delay point. To explore the benefits of spintronics-based hardware on realistic neuromorphic workloads, we developed a Parallel Discrete-Event Simulation model called Doryta, which is further integrated with a materials-to-systems benchmarking framework. The benchmarking framework allows us to obtain quantitative metrics on the throughput and energy of spintronics-based neuromorphic computing and compare these against standard CMOS-based approaches. Although spintronics hardware offers significant energy and latency advantages, we find that for larger neuromorphic circuits, the performance is evidently limited by the interconnection networks rather than the spintronics-based neurons and synapses. Thus, it becomes imperative to identify interconnect materials that would natively offer low latency and consume less energy than the current copper-based interconnects. Through Doryta we are also able to show the power of neuromorphic computing by simulating Conway’s Game of Life. We show that Doryta obtains over 400 × speedup using 1,280 CPU cores when tested on a convolutional, sparse, neural architecture.
A Data-Driven Approach for Pedestrian Intention Prediction in Large Public Places

Pedestrian intention prediction is an important issue in crowd modeling and simulation. Existing approaches focus on short term intention prediction, which limits their applications in large public places that require long term intention prediction. To this end, this paper proposes a data-driven approach to predict long term pedestrian intention. In the proposed approach, local velocity fields are constructed based on historical trajectories of pedestrians. A similarity function is further defined based on the velocity fields to predict the intermediate destinations of pedestrians. To evaluate its effectiveness, we evaluated the proposed approach in a real world example – an airport terminal. The simulation results have demonstrated that our approach can offer effective prediction performance.
Customer behaviour forecast using deep learning networks

The paper presents a case study on customer behaviour forecast within a parking products domain. In particular, authors compare different LSTM-based DL networks with and without data preprocessing to decide which of the selected architectures and hyperparameter combinations provide the least square error estimate. Unfortunately, well-known forecast methods like regression and ARIMA did not deliver the needs forecast reliability, which brought the authors to the application of DL models. While the paper does not offer novel DL models or architectures, it provides a convenient application insight enabling a better understanding of the application of the selected models. Data has been collected for several months and offers a good study backbone.
Expanders via local edge flips in quasilinear time

Mahlmann and Schindelhaue (2005) proposed the following simple process, called flip-chain, for transforming any given connected d-regular graph into a d-regular expander: In each step, a random 3-path abcd is selected, and edges ab and cd are replaced by two new edges ac and bd, provided that ac and bd do not exist already. A motivation for the study of the flip-chain arises in the design of overlay networks, where it is common practice that adjacent nodes periodically exchange random neighbors, to maintain good connectivity properties. It is known that the flip-chain converges to the uniform distribution over connected d-regular graphs, and it is conjectured that an expander graph is obtained after O(ndlogn) steps, w.h. p., where n is the number of vertices. However, the best known upper bound on the number of steps is O(n2d2√logn), and the best bound on the mixing time of the chain is O(n16d36logn). We provide a new analysis of a natural flip-chain instantiation, which shows that starting from any connected d-regular graph, for d = Ω(log2 n), an expander is obtained after O(ndlog2n) steps, w.h. p. This result is tight within logarithmic factors, and almost matches the conjectured bound. Moreover, it justifies the use of edge flip operations in practice: for any d-regular graph with d = poly(logn), an expander is reached after each vertex participates in at most poly(logn) operations, w.h. p. Our analysis is arguably more elementary than previous approaches. It uses the novel notion of the strain of a cut, a value that depends both on the crossing edges and their adjacent edges. By keeping track of the cut strains, we form a recursive argument that bounds the time before all sets of a given size have large expansion, after all smaller sets have already attained large expansion.
A Sliding Window Based Approach With Majority Voting for Online Human Action Recognition using Spatial Temporal Graph Convolutional Neural Networks

Nowadays, Human Action Recognition (HAR) has become an important issue since it is widely used in video surveillance, human-robot collaboration in industry, etc. Developing such accurate and efficient algorithms remains a difficult task because of the high variability of the human shapes, postures, as well as the complexity of their movements but more importantly when using continuous/untrimmed data streams. Since HAR from Segmented/trimmed sequences has been intensively studied and developed in the recent years, Online HAR in the other hand remains a challenging task and is less developed. In this paper, we propose a Sliding Window and Majority Voting skeleton-based approach for Online HAR using Spatial Temporal Graph Convolutional Neural Networks (STGCN-SWMV). Our method is evaluated on two Online skeleton-based datasets named OAD and UOW. In comparison with existing methods, the obtained results exceed state-of-the-art algorithms and show the efficiency of the proposed approach.
Parallel scan as a multidimensional array problem

For many algorithms, it is challenging to identify a suitable parallel version, as the design space is typically very large. In this paper we demonstrate how rank-polymorphic array languages can be used as a tool to explore such design spaces through concise high-level specifications. If input data can be organised into a multi-dimensional array, and the algorithm can be stated as a recursive traversal over sub-arrays, array languages offer a lot of expressive power. The reason for this is that array shapes can be used to guide recursive traversals. Conciseness of specifications comes from array reshapes that move the desired elements into canonical hyperplanes. As a case study, we discuss several variants of implementing prefix sums (also known as scans) in SaC. We demonstrate how small code adjustments suffice to change the concurrency pattern exposed to the compiler. It turns out that variability that is typically achieved by generic inductive data types such as binary trees is a special case of what is offered by the array paradigm.
Robustness of average-case meta-complexity via pseudorandomness

We show broad equivalences in the average-case complexity of many different meta-complexity problems, including Kolmogorov complexity, time-bounded Kolmogorov complexity, and the Minimum Circuit Size Problem. These results hold for a wide range of parameters (various thresholds, approximation gaps, weak or strong average-case hardness, etc.) and complexity notions, showing the theory of meta-complexity is very *robust* in the average-case setting. Our results are shown by establishing new and generic connections between meta-complexity and the theory of pseudorandomness and one-way functions. Using these connections, we give the first unconditional characterization of one-way functions based on the average-case hardness of the Minimum Circuit Size Problem. We also give a surprising and clean characterization of one-way functions based on the average-case hardness of (the worst-case uncomputable) Kolmogorov complexity. Moreover, the latter is the first characterization of one-way functions based on the average-case hardness of a fixed problem on *any* samplable distribution. We give various applications of these results to the foundations of cryptography and the theory of meta-complexity. For example, we show that the average-case hardness of deciding k-SAT or Clique on any samplable distribution of high enough entropy implies the existence of one-way functions. We also use our results to unconditionally solve various meta-complexity problems in CZK (computational zero-knowledge) on average, and give implications of our results for the classic question of proving NP-hardness for the Minimum Circuit Size Problem.
From perception to programs: regularize, overparameterize, and amortize

Toward combining inductive reasoning with perception abilities, we develop techniques for neurosymbolic program synthesis where perceptual input is first parsed by neural nets into a low-dimensional interpretable representation, which is then processed by a synthesized program. We explore several techniques for relaxing the problem and jointly learning all modules end-to-end with gradient descent: multitask learning; amortized inference; overparameterization; and a differentiable strategy for penalizing lengthy programs. Collectedly this toolbox improves the stability of gradient-guided program search, and suggests ways of learning both how to perceive input as discrete abstractions, and how to symbolically process those abstractions as programs.
TherapeuticEffectsofModifiedSi-Miao-Yong-AnDecoction in the Treatment of Rat Myocardial Ischemia/Reperfusion Injury

Objective. Modied Si-Miao-Yong-An decoction (MSMYA) was empirically originated from Si-Miao-Yong-An Decoction, which has been utilized for centuries to treat vasculopathy as well as heart diseases through clearing heat and detoxifying. ­is study aimed at conrming MSMYA’s therapeutic eects for treating myocardial ischemia/reperfusion (I/R) injury and its underlying mechanisms. Methods. Rats were intragastrically administered with MSMYA for 4 weeks after ischemia/reperfusion (I/R) operation. Superoxide dismutase (SOD) and malondialdehyde (MDA) concentration were determined by calorimetry. Coagulation function was determined using an automated coagulation analyzer. Levels of cysteinyl aspartate specic proteinase (caspase)-1, interleukin (IL)-1β, interleukin (IL)-18, and lactate dehydrogenase (LDH) were measured by an enzyme-linked immunosorbent assay (ELISA). Infarct size was determined by triphenyltetrazolium chloride (TTC) staining. Myocardial histopathological and ultrastructure changes were examined by H&E staining and electron microscopy, respectively. Relative mRNA expression of NLRP3, an apoptosis-associated speck-like proteins containing the caspase activation and recruitment domain (ASC), caspase-1, IL-1β, and IL-18 were analyzed using quantitative real-time polymerase chain reaction (PCR). Meanwhile, their relative protein expressions were measured using western blotting. Results. ­e results showed MSMYA can inhibit oxidative stress by increasing SOD and reducing MDA, suppress inammatory reaction by decreasing NLRP3 inammasome-related cytokines’ level, improve coagulation function by increasing prothrombin time (PT) and activating partial thromboplastin time (APTT), and ameliorate myocardial histopathological and ultrastructural changes. In addition, MSMYA’s cardioprotective eects probably related to suppressing NLRP3 inammasome pathway activation by reducing NLRP3 inammasome molecular mRNA and protein relative expression. Conclusion. ­e results indicated that MSMYA played an important role in protecting the myocardium from I/R injury. ­e likely mechanism is the inhibition of oxidative stress, improvement of cardiac injury, and the reduction of NLRP3-related inammatory cytokines release. ­is provides a basis for further research on the mechanism and clinical application of MSMYA to improve myocardial I/R injury.
Untargeted LC-HRMS Based-Plasma Metabolomics Reveals 3-O-Methyldopa as a New Biomarker of Poor Prognosis in High-Risk Neuroblastoma

Neuroblastoma (NB) is the most common extracranial malignant tumor in children. Although the survival rate of NB has improved over the years, the outcome of NB still remains poor for over 30% of cases. A more accurate risk stratification remains a key point in the study of NB and the availability of novel prognostic biomarkers of “high-risk” at diagnosis could help improving patient stratification and predicting outcome. In this paper we show a biomarker discovery approach applied to the plasma of 172 NB patients. Plasma samples from a first cohort of NB patients and age-matched healthy controls were used for untargeted metabolomics analysis based on high-resolution mass spectrometry (HRMS). Differential expression analysis highlighted a number of metabolites annotated with a high degree of identification. Among them, 3-O-methyldopa (3-O-MD) was validated in a second cohort of NB patients using a targeted metabolite profiling approach and its prognostic potential was also analyzed by survival analysis on patients with 3 years follow-up. High expression of 3-O-MD was associated with worse prognosis in the subset of patients with stage M tumor (log-rank p < 0.05) and, among them, it was confirmed as a prognostic factor able to stratify high-risk patients older than 18 months. 3-O-MD might be thus considered as a novel prognostic biomarker of NB eligible to be included at diagnosis among catecholamine metabolite panels in prospective clinical studies. Further studies are warranted to exploit other potential biomarkers highlighted using our approach.
The Impact of Scarcity on Consumers’ Impulse Buying Based on the S-O-R Theory

Purpose The global COVID-19 pandemic impacted the healthcare systems of every nation. The scarcity of medical protective equipment led to impulse buying at the early stages of the COVID-19 outbreak in China which resulted in stockpiling and the increase of prices by retailers and insufficiencies among frontline workers. This situation impacted epidemic control work and market order and is the context from which this paper identifies how the scarcity of medical protective equipment affected Chinese consumers’ impulse buying based on the theories of S-O-R model and bandwagon effect. The research provides insight into the mechanism of mediation (fear of missing out) and moderation (bandwagon) in the relationship between scarcity and impulse buying. Design/Methodology/Approach This study uses convenience sampling, surveying 488 Chinese consumers through an online questionnaire. Smart-PLS was used to test the hypotheses. Findings The empirical findings demonstrate that scarcity makes consumers fear missing the chance of getting protective medical equipment, leading ultimately to impulse buying. Besides, the scarcity effect on consumers’ impulse buying was found to depend on other consumers’ follow up behaviour in such emergency situations. Research Limitations/Implications The findings provide managerial and theoretical insight and a point of reference for businesses in the implementation of a scarcity strategy. The findings will also prove useful to the Chinese Risk Response Department as it continuously improves its responses to the risk of consumers’ impulse buying during a pandemic. Originality/Value This study consolidates and takes research forward in the areas of impulse buying and consumer behaviour, confirming the mediating effect of fear of missing out and the moderating effect of the bandwagon in the relationship between scarcity and impulse buying.
Astrocytes: GABAceptive and GABAergic Cells in the Brain

Astrocytes, the most numerous glial cells in the brain, play an important role in preserving normal neural functions and mediating the pathogenesis of neurological disorders. Recent studies have shown that astrocytes are GABAceptive and GABAergic astrocytes express GABAA receptors, GABAB receptors, and GABA transporter proteins to capture and internalize GABA. GABAceptive astrocytes thus influence both inhibitory and excitatory neurotransmission by controlling the levels of extracellular GABA. Furthermore, astrocytes synthesize and release GABA to directly regulate brain functions. In this review, we highlight recent research progresses that support astrocytes as GABAceptive and GABAergic cells. We also summarize the roles of GABAceptive and GABAergic astrocytes that serve as an inhibitory node in the intercellular communication in the brain. Besides, we discuss future directions for further expanding our knowledge on the GABAceptive and GABAergic astrocyte signaling.
Biomaterials and Electroactive Bacteria for Biodegradable Electronics

The global production of unrecycled electronic waste is extensively growing each year, urging the search for alternatives in biodegradable electronic materials. Electroactive bacteria and their nanowires have emerged as a new route toward electronic biological materials (e-biologics). Recent studies on electron transport in cable bacteria—filamentous, multicellular electroactive bacteria—showed centimeter long electron transport in an organized conductive fiber structure with high conductivities and remarkable intrinsic electrical properties. In this work we give a brief overview of the recent advances in biodegradable electronics with a focus on the use of biomaterials and electroactive bacteria, and with special attention for cable bacteria. We investigate the potential of cable bacteria in this field, as we compare the intrinsic electrical properties of cable bacteria to organic and inorganic electronic materials. Based on their intrinsic electrical properties, we show cable bacteria filaments to have great potential as for instance interconnects and transistor channels in a new generation of bioelectronics. Together with other biomaterials and electroactive bacteria they open electrifying routes toward a new generation of biodegradable electronics.