Miracle-dz/UniFAR-FTUs huggingface dataset:
Projection-Free Algorithms in Statistical Estimation

Frank-Wolfe algorithm (FW) and its variants have gained a surge of interests in machine learning community due to its projection-free property. Recently people have reduced the gradient evaluation complexity of FW algorithm to $\log(\frac{1}{\epsilon})$ for the smooth and strongly convex objective. This complexity result is especially significant in learning problem, as the overwhelming data size makes a single evluation of gradient computational expensive. However, in high-dimensional statistical estimation problems, the objective is typically not strongly convex, and sometimes even non-convex. In this paper, we extend the state-of-the-art FW type algorithms for the large-scale, high-dimensional estimation problem. We show that as long as the objective satisfies {\em restricted strong convexity}, and we are not optimizing over statistical limit of the model, the $\log(\frac{1}{\epsilon})$ gradient evaluation complexity could still be attained.
Learning Implicit Tasks for Patient-Specific Risk Modeling in ICU

Accurate assessment of the severity of a patient’s condition plays a fundamental role in acute hospital care such as that provided in an intensive care unit (ICU). ICU clinicians are required to make sense of a large amount of clinical data in a limited time to estimate the severity of a patient’s condition, which ultimately leads to the planning of appropriate care. The ICU is an especially demanding environment for clinicians because of the diversity of patients who mostly suffer from multiple diseases of various types. In this paper, we propose a mortality risk prediction method for ICU patients. The method is intended to enhance the severity assessment by considering the diversity of patients. Our method produces patient-specific risk models that reflect the collection of diseases associated with the patient. Specifically, we assume a small number of latent basis tasks, where each latent task is associated with its own parameter vector; a parameter vector for a specific patient is constructed as a linear combination of these. The latent representation of a patient, namely, the coefficients of the combination, is learned based on the collection of diseases associated with the patient. Our method could be considered a multi-task learning method where latent tasks are learned based on the collection of diseases. We demonstrate the effectiveness of our proposed method using a dataset collected from a hospital. Our method achieved higher predictive performance compared with a single-task learning method, the “de facto standard,” and several multi-task learning methods including a recently proposed method for ICU mortality risk prediction. Furthermore, our proposed method could be used not only for predictions but also for uncovering patient-specificity from different viewpoints.
A Comparative Study of Inter-firm Transactional Relationship between Kyohokai and Yokokai

Although two major Japanese manufacturers, Toyota and Mazda, compete in the automotive market, their corporate performance, respectively, is different because the internal resources and the way they coordinate interrelationships among their network partners is known to be divergent. Both these two companies have their own collaborating organizations, Kyohokai for Toyota and Yokokai for Mazda. Kyohokai, in contrast to Yokokai, is a central-oriented organization with strong interactive cooperati on among network partners. Consistent with the stream of research on the coordination of internal resources and external inter-firm relationships, the purpose of this research is to identify and contrast the best practices of Toyota to Mazda using a comparative approach. This paper reviews relevant lliterature on network organizations to focus on the differences between Kyohokai and Yokokai. Specifically, it makes a contribution by proposing a new perspective to identify the determinants of corporate performance and clarify the difference among the external inter-firm’s relationships between Kyohokai and Yokokai, thus ascertaining the rational structure of network organizations.
GumBolt: Extending Gumbel trick to Boltzmann priors

Boltzmann machines (BMs) are appealing candidates for powerful priors in variational autoencoders (VAEs), as they are capable of capturing nontrivial and multi-modal distributions over discrete variables. However, non-differentiability of the discrete units prohibits using the reparameterization trick, essential for low-noise back propagation. The Gumbel trick resolves this problem in a consistent way by relaxing the variables and distributions, but it is incompatible with BM priors. Here, we propose the GumBolt, a model that extends the Gumbel trick to BM priors in VAEs. GumBolt is significantly simpler than the recently proposed methods with BM prior and outperforms them by a considerable margin. It achieves state-of-the-art performance on permutation invariant MNIST and OMNIGLOT datasets in the scope of models with only discrete latent variables. Moreover, the performance can be further improved by allowing multi-sampled (importance-weighted) estimation of log-likelihood in training, which was not possible with previous models.
Effects of Fasudil on Patients with Pulmonary Hypertension Associated with Left Ventricular Heart Failure with Preserved Ejection Fraction: A Prospective Intervention Study

Background Pulmonary hypertension due to left ventricular heart failure with preserved ejection fraction (PH-HFpEF) is an increasingly medical problem. The aim of the study was to evaluate the clinical efficacy of fasudil on PH-HFpEF elderly patients and to figure out the subtype of PH-HFpEF which may be the therapeutic object of fasudil. Method 58 PH-HFpEF elderly patients were enrolled. Patients were diagnosed with passive pulmonary hypertension (PPH) or reactive pulmonary hypertension (RPH) by right heart catheterization and all receiving Rho kinase inhibitor fasudil for 2 weeks. The endpoint includes changes in SpO2, NT-pro BNP, cardiac functional classification, and echocardiography measurements after 2 weeks treatment. Results The course of disease in the RPH group was longer than the PPH group (p < 0.05). Cardiac output was found to be worse in the RPH group than the PPH group (p < 0.01). Besides, the RPH group demonstrated a greater transpulmonary pressure gradient (TPG) and pulmonary vascular resistance (PVR) than the PPH group (p < 0.01 for both) as well as pulmonary arterial systolic pressure (PASP) and mean pulmonary arterial pressure (mPAP) (p < 0.01 for both), which fits the feature of RPH. After treatment of fasudil, in RPH group, PASP significantly decreased (p < 0.01) with decreased E/E′ and increased E/A (p < 0.05 for both), indicating that pulmonary haemodynamics and cardiac diastolic function were ameliorated, but the measurements in the PPH group had no significant changes. NT-pro BNP and 6 MWD of both groups were improved (p < 0.05). The total effective rate of the RPH group was 74.29%, which was higher than 47.83% of the PPH group (p < 0.05). Conclusion The Rho kinase inhibitor fasudil can improve pulmonary and left ventricular haemodynamics in patients with PH-HFpEF. The total effective rate was higher in the RPH group. Fasudil may be a promising targeted drug for the RPH in PH-HFpEF patients. This trial is registered with ChiCTR-INR-16009511.
Supplementation of Propionic Acid (PA), Butyric Acid (BA) or Antibiotic (AB) in Diets and their Influence on Broiler Performance, Carcass Parameters and Immune Response

The effectiveness of organic acids, Propionic acid (PA) and Butyric acid (BA) at two levels (0.2 and 0.3%) in replacing antibiotic (AB-Virginiamycin 11mg/kg) from chick diets was evaluated in a feeding trial with 270 broiler chicks (Vencobb) by recording their performance, meat yield and immune response up to 42 d of age. The experimental diets included two organic acids, PA and BA at two levels (0.2 and 0.3%) each in a factorial pattern, and compared with the antibiotic and control diets. Each diet was offered ad libitum to 9 replicates of 5 birds per replicate in battery brooders. The results indicated that organic acids, PA and BA at both levels of inclusion (0.2% and 0.3%) in diets enhanced (p< 0.01) broiler body weight by 8-10% over the AB and control diets (2154g vs 1960g) at 42d of age. Both organic acids significantly (p< 0.01) improved FCR (1.681.91), tibia weight (5.75-5.31g), tibia length (7.96-6.16 cm), breast muscle yield (13.68-14.77%), and reduced leg scores (1.04-1.50) and abdominal fat (7.5%) compared to AB and control diets. However, there was no statistical difference between PA and BA for all the effects recorded. Organic acids supplemented at 0.2% in diets was as effective as or better than 0.3% level suggesting that the former level was adequate in broiler diets. In effect, the study suggested that organic acids, PA and BA supplemented at 0.2% was effective in replacing Virginiamycin (11mg/kg) from broiler diets with the advantage of better performance up to 42 d of age.
Identification of Novel Somatic TP53 Mutations in Patients with High-Grade Serous Ovarian Cancer (HGSOC) Using Next-Generation Sequencing (NGS)

Somatic mutations in TP53 are a hallmark of high-grade serous ovarian cancer (HGSOC), although their prognostic and predictive value as markers is not well defined. Next-generation sequencing (NGS) can identify novel mutations with high sensitivity, that may be repurposed as potential druggable anti-cancer targets and aid in therapeutic decisions. Here, a commercial NGS cancer panel comprising 26 genes, including TP53, was used to identify new genetic markers of platinum resistance and patient prognosis in a retrospective set of patients diagnosed with epithelial ovarian cancer. Six novel TP53 somatic mutations in untreated tumors from six distinct patients diagnosed with HGSOC were identified: TP53 c. 728_739delTGGGCGGCATGA (p.Met243_Met247del, in-frame insertion or deletion (INDEL); TP53 c. 795_809delGGGACGGAACAGCTT (p.Gly266_Phe270del, in-frame INDEL); TP53 c. 826_827GC>AT (p.Ala276Ile, missense); TP53 c.1022insT (p.Arg342Profs*5, frameshift INDEL); TP53 c.1180delT (p.Ter394Aspfs*28, frameshift INDEL); and TP53 c.573insT (p.Gln192Serfs*17, frameshift INDEL). Novel TP53 variants were validated by classical sequencing methods and their impact on protein expression in tumors explored by immunohistochemistry. Further insights into the potential functional effect of the mutations were obtained by different in silico approaches, bioinformatics tools, and structural modeling. This discovery of previously unreported TP53 somatic mutations provides an opportunity to translate NGS technology into personalized medicine and identify new potential targets for therapeutic applications.
Definition, capabilities and components of a terrestrial carbon monitoring system

Research efforts for effectively and consistently monitoring terrestrial carbon are increasing in number. As such, there is a need to define carbon monitoring and how it relates to carbon cycle science and carbon management. There is also a need to identify capabilities of a carbon monitoring system and the system components needed to develop the capabilities. Capabilities that enable the effective application of a carbon monitoring system for monitoring and management purposes may include: reconciling carbon stocks and fluxes, developing consistency across spatial and temporal scales, tracking horizontal movement of carbon, attribution of emissions to originating sources, cross-sectoral accounting, uncertainty quantification, redundancy and policy relevance. Focused research is needed to integrate these capabilities for sustained estimates of carbon stocks and fluxes. Additionally, if monitoring is intended to inform management decisions, management priorities should be considered prior to development of a monitoring system.
Multiobjective evolutionary algorithm for the optimization of noisy combustion processes

This work introduces a multiobjective evolutionary algorithm capable of handling noisy problems with a particular emphasis on robustness against unexpected measurements (outliers). The algorithm is based on the Strength Pareto evolutionary algorithm of Zitzler and Thiele and includes the new concepts of domination dependent lifetime, re-evaluation of solutions and modifications in the update of the archive population. Several tests on prototypical functions underline the improvements in convergence speed and robustness of the extended algorithm. The proposed algorithm is implemented to the Pareto optimization of the combustion process of a stationary gas turbine in an industrial setup. The Pareto front is constructed for the objectives of minimization of NO/sub x/ emissions and reduction of the pressure fluctuations (pulsation) of the flame. Both objectives are conflicting affecting the environment and the lifetime of the turbine, respectively. The optimization leads a Pareto front corresponding to reduced emissions and pulsation of the burner. The physical implications of the solutions are discussed and the algorithm is evaluated.
Distributed Computation in the Node-Congested Clique

The Congested Clique model of distributed computing, which was introduced by Lotker, Patt-Shamir, Pavlov, and Peleg [SPAA'03, SICOMP'05] and was motivated as "a simple model for overlay networks", has received extensive attention over the past few years. In this model, nodes of the system are connected as a clique and can communicate in synchronous rounds, where per round each node can send $O(\log n)$ bits to each other node, all the same time. The fact that this model allows each node to send and receive a linear number of messages at the same time seems to limit the relevance of the model for overlay networks. Towards addressing this issue, in this paper, we introduce the Node-Congested Clique as a general communication network model. Similarly to the Congested Clique model, the nodes are connected as a clique and messages are sent in synchronous communication rounds. However, here, per round, every node can send and receive only $O(\log n)$ many messages of size $O(\log n)$. To initiate research on our network model, we present distributed algorithms for the Minimum Spanning Tree, BFS Tree, Maximal Independent Set, Maximal Matching, and Coloring problem for an input graph $G=(V,E)$, where each clique node initially only knows a single node of $G$ and its incident edges. For the Minimum Spanning Tree problem, our runtime is polylogarithmic. In all other cases the runtime of our algorithms mainly depends on the arboricity $a$ of $G$, which is a constant for many important graph families such as planar graphs. At the core of these algorithms is a distributed algorithm that assigns directions to the edges of $G$ so that at the end, every node is incident to at most $O(a)$ outgoing edges.
Parameterised Verification of Infinite State Multi-Agent Systems via Predicate Abstraction

We define a class of parameterised infinite state multi-agent systems (MAS) that is unbounded in both the number of agents composing the system and the domain of the variables encoding the agents. We analyse their verification problem by combining and extending existing techniques in parameterised model checking with predicate abstraction procedures. The resulting methodology addresses both forms of unboundedness and provides a technique for verifying unbounded MAS defined on infinite-state variables. We illustrate the effectiveness of the technique on an infinite-domain variant of an unbounded version of the train-gate-controller.
Scanner

A growing number of visual computing applications depend on the analysis of large video collections. The challenge is that scaling applications to operate on these datasets requires efficient systems for pixel data access and parallel processing across large numbers of machines. Few programmers have the capability to operate efficiently at these scales, limiting the field's ability to explore new applications that leverage big video data. In response, we have created Scanner, a system for productive and efficient video analysis at scale. Scanner organizes video collections as tables in a data store optimized for sampling frames from compressed video, and executes pixel processing computations, expressed as dataflow graphs, on these frames. Scanner schedules video analysis applications expressed using these abstractions onto heterogeneous throughput computing hardware, such as multi-core CPUs, GPUs, and media processing ASICs, for high-throughput pixel processing. We demonstrate the productivity of Scanner by authoring a variety of video processing applications including the synthesis of stereo VR video streams from multi-camera rigs, markerless 3D human pose reconstruction from video, and data-mining big video datasets such as hundreds of feature-length films or over 70,000 hours of TV news. These applications achieve near-expert performance on a single machine and scale efficiently to hundreds of machines, enabling formerly long-running big video data analysis tasks to be carried out in minutes to hours.
Approximate Model Counting by Partial Knowledge Compilation

Model counting is the problem of computing the number of satisfying assignments of a given propositional formula. Although exact model counters can be naturally furnished by most of the knowledge compilation (KC) methods, in practice, they fail to generate the compiled results for the exact counting of models for certain formulas due to the explosion in sizes. Decision-DNNF is an important KC language that captures most of the practical compilers. We propose a generalized Decision-DNNF (referred to as partial Decision-DNNF) via introducing a class of new leaf vertices (called unknown vertices), and then propose an algorithm called PartialKC to generate randomly partial Decision-DNNF formulas from the given formulas. An unbiased estimate of the model number can be computed via a randomly partial Decision-DNNF formula. Each calling of PartialKC consists of multiple callings of MicroKC, while each of the latter callings is a process of importance sampling equipped with KC technologies. The experimental results show that PartialKC is more accurate than both SampleSearch and SearchTreeSampler, PartialKC scales better than SearchTreeSampler, and the KC technologies can obviously accelerate sampling.
Anti-Pseudomonas aeruginosa activity of hemlock (Conium maculatum, Apiaceae) essential oil

Abstract Conium maculatum is a nitrophilous weed belonging to the Apiaceae family and occurring in hedgerows, pastures, waste ground, along rivers and roadsides. Little is known on the chemistry and bioactivity of other secondary metabolites occurring in the plant. In the present work, we have analysed the chemical composition and antimicrobial activity of the essential oils hydrodistilled from leaves and inflorescenes of C. maculatum growing in Sicily, Italy. The composition of essential oils was achieved by gas chromatography-mass spectrometry (GC-MS) analysis, whereas the inhibitory effects on the growth of two Gram negative strains, namely Escherichia coli and Pseudomonas aeruginosa were assessed by two different analysis. The essential oils exhibited different chemical profiles (1-butylpiperidine and myrcene in the inflorescenes), (mostly (E)-caryophyllene in the leaves). The latter oil was particularly active in inhibiting the growth of P. aeruginosa. These results shed light on the possible application of hemlock essential oils as antimicrobial agents.
Small and Adaptive Antennas and Arrays for GNSS Applications

This paper reviews Global Navigation Satellite Systems (GNSS) antennas and arrays for robust coverage in presence of interference signals with a focus on antenna design aspects. A number of small antenna designs are presented that cover multiple current and future GNSS frequency bands (1150-1610 MHz) with sizes as small as 1 in (25 mm) in diameter. Several arrays are presented with a size as small as 3.5 in (8.9 cm) in diameter and approximately 0.5 in (13 mm) thick as compared to commercially available apertures 14 in (35.6 cm) and 5.5 in (14.0 cm) in diameter. These small arrays are shown to satisfy the gain requirements of GNSS receivers while simultaneously offering four to six antenna elements for adaptive interference suppression.
Términos del intercambio y desequilibrios gemelos: evidencia para Argentina

Este trabajo investiga la validez de la hipotesis de los desequilibrios gemelos (fiscal y externo) en el caso de una economia pequena, como Argentina. El trabajo analiza, a traves de modelos de VEC (modelo de correccion de errores), las relaciones de largo plazo entre la cuenta corriente, el deficit fiscal (o excedente), la inversion y los terminos de intercambio, para el periodo 1993-2012. Los resultados sugieren que incrementos en el deficit fiscal y en la inversion deterioran el saldo de la cuenta corriente. A su vez, el resultado fiscal anticipa (causalidad, en sentido de Granger) a la cuenta corriente y a la inversion, pero no a la inversa.
An optimal approximation of discrete random variables with respect to the Kolmogorov distance

We present an algorithm that takes a discrete random variable $X$ and a number $m$ and computes a random variable whose support (set of possible outcomes) is of size at most $m$ and whose Kolmogorov distance from $X$ is minimal. In addition to a formal theoretical analysis of the correctness and of the computational complexity of the algorithm, we present a detailed empirical evaluation that shows how the proposed approach performs in practice in different applications and domains.
Molecular Survey of Respiratory and Immunosuppressive Pathogens Associated with Low Pathogenic Avian Influenza H9N2 Subtype and Virulent Newcastle Disease Viruses in Commercial Chicken Flocks

The study was carried out in 48 poultry flocks to elucidate the roles of various complicating pathogens involved along with Newcastle disease (ND)/ low pathogenic avian influenza (LPAI) outbreaks. Necropsy was conducted and samples were collected for the isolation of Newcastle disease virus (NDV), Influenza A virus, infectious bronchitis virus (IBV), pathogenic bacteria; molecular detection of infectious laryngotracheitis virus (ILTV), fowl adeno virus (FAV), chicken anaemia virus (CAV), Mycoplasma synoviae (MS) and Mycoplasma gallisepticum (MG). The isolation results confirmed that 18/48 flocks (37%) were positive for the presence of hemagglutinating agents. Out of 18 hemagglutination (HA) positive flocks, 11 flocks (61%) were positive for both avian influenza virus (AIV) and NDV; 4 flocks (22%) were positive for NDV; and 3 flocks (17%) were positive for AIV. Sequence analysis of hemagglutinin and neuraminidase genes of AIV revealed that all were belonging to LPAI-H9N2 subtype. Sequence analysis of F gene of NDV revealed that they belong to virulent type. The PCR results confirmed the presence of three to seven etiological agents (CAV, FAV, ILTV, MG, MS and avian pathogenic E. coli along with LPAI/NDV from all the 18 HA-positive flocks. The detection rate of triple, quadruple, quintuple, sextuple and sevenfold infections was 17% (3 flocks), 28% (5 flocks), 11%, (2 flocks) 28% (5 flocks) and 17% (3 flocks), respectively. In conclusion, the disease complex involved more than one pathogen, primarily resulting from the interplay between LPAI-H9N2 and NDV; subsequently this could be exacerbated by co-infection with other agents which may cause exacerbated outbreaks that may otherwise go undetected in field.