Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint Input-Label Embedding for Neural Text Classification

Neural text classification methods typically treat output classes as categorical labels which lack description and semantics. This leads to an inability to train them well on large label sets or to generalize to unseen labels and makes speed and parameterization dependent on the size of the label set. Joint input-label space methods ameliorate the above issues by exploiting label texts or descriptions, but often at the expense of weak performance on the labels seen frequently during training. In this paper, we propose a label-aware text classification model which addresses these issues without compromising performance on the seen labels. The model consists of a joint input-label multiplicative space and a label-set-size independent classification unit and is trained with cross-entropy loss to optimize accuracy. We evaluate our model on text classification for multilingual news and for biomedical text with a large label set. The label-aware model consistently outperforms both monolingual and multilingual classification models which do not leverage label semantics and previous joint input-label space models.
Should Italians, Croats and Slovenes Work Together in Developing a Northern Adriatic Tourist Destination?

Should Italians, Croats and Slovenes Work Together in Developing a Northern Adriatic Tourist Destination? The article assesses opportunities for creating a macro-tourist destination in the region of the Northern Adriatic. Research was based on a survey of more than 1700 summer tourists in three different sea towns, namely Grado in Italy, Opatija in Croatia and Portorož in Slovenia in 2004. The three countries share a common past under the Austro-Hungarian monarchy and are headed for a common future within the European Union. Survey results of tourists' motivations and destination quality perceptions support the notion that the Northern Adriatic is considered by many central Europeans as the "sea closest to home". Thus, EU efforts under the framework of the Cohesion Policy of EU regions could also be adopted in the area of joint destination management of the Northern Adriatic. A Destination Management Organization for the three regions should be established as the main agent of change in the destination management of the region.
Practical and theoretical aspects of mixture‐of‐experts modeling: An overview

Mixture‐of‐experts (MoE) models are a powerful paradigm for modeling data arising from complex data generating processes (DGPs). In this article, we demonstrate how different MoE models can be constructed to approximate the underlying DGPs of arbitrary types of data. Due to the probabilistic nature of MoE models, we propose the maximum quasi‐likelihood (MQL) approach as a method for estimating MoE model parameters from data, and we provide conditions under which MQL estimators are consistent and asymptotically normal. The blockwise minorization–maximization (blockwise‐MM) algorithm framework is proposed as an all‐purpose method for constructing algorithms for obtaining MQL estimators. An example derivation of a blockwise‐MM algorithm is provided. We then present a method for constructing information criteria for estimating the number of components in MoE models and provide justification for the classic Bayesian information criterion (BIC). We explain how MoE models can be used to conduct classification, clustering, and regression and illustrate these applications via two worked examples.
Polynomial Time Equilibria in Bottleneck Congestion Games

We consider bottleneck congestion games in an arbitrary graph G where player strategies are flows in G . The player's objective is to select a flow that minimizes the maximum load on any edge, that is, minimize the bottleneck congestion. We consider splittable and unsplittable games with pure strategies. It has been an open problem for many years to determine whether it is possible to compute in polynomial time Nash equilibriums for bottleneck congestion games in arbitrary graphs. For splittable games we provide a polynomial time algorithm to compute a Nash equilibrium which is also a global optimum. The unsplittable game problem is known to be PLS-complete, and so we focus on approximate Nash equilibria where players are approximately stable. For uniform player demands we give an algorithm to compute a O(łog m)-approximate unsplittable equilibrium in polynomial time, where m is the number of edges. For non-uniform player demands we give an algorithm to compute a O(ζ łog(ζ m))-approximate unsplittable equilibrium in polynomial time, where ζ = O(1 + łog (dmax /dmin)) and dmax, dmin are the respective maximum and minimum player demands. To our knowledge, these are the first general results for efficiently computing equilibria of pure bottleneck congestion games in arbitrary graphs, both for the splittable and unsplittable cases.
Poster: On Vulnerability Evolution in Android Apps

In this work, we reconstruct a set of Android app lineages which each of them represents a sequence of app versions that are historically released for the same app. Then, based on these lineages, we empirically investigate the evolution of app vulnerabilities, which are revealed by well-known vulnerability scanners, and subsequently summarise various interesting findings that constitute a tangible knowledge to the community.
NOD2 Modulates Serotonin Transporter and Interacts with TLR2 and TLR4 in Intestinal Epithelial Cells

Background/Aims: Serotonin (5-HT) is a chief modulator of intestinal activity. The effects of 5-HT depend on its extracellular availability, which is mainly controlled by serotonin transporter (SERT), expressed in enterocytes. On the other hand, innate immunity, mediated by Toll-like receptors (TLRs) and nucleotide oligomerization domain (NOD)-like receptors (NLRs), is known to control intestinal microbiota and maintain intestinal homeostasis. The dysregulation of the intestinal serotonergic system and innate immunity has been observed in inflammatory bowel diseases (IBD), the incidence of which has severely increased all over the world. The aim of the present study, therefore, was to analyze the effect of NOD2 on intestinal SERT activity and expression, as well as to study the crosstalk of NOD2 with TLR2 and TLR4. Methods: Intestinal epithelial cell line Caco-2/TC7 was used to analyze SERT activity and SERT, NOD2, TLR2 and TLR4 molecular expression by real-time PCR and western blotting. Moreover, intestinal tract (ileum and colon) from mice deficient in TLR2, TLR4 or TLR2/4 receptors was used to test the interdependence of NOD2 with these TLR receptors. Results: NOD2 activation inhibits SERT activity in Caco-2/TC7 cells, mainly due to the decrement of SERT molecular expression, with RIP2/RICK being the intracellular pathway involved in this effect. This inhibitory effect on SERT would yield an increment of extracellular 5-HT availability. In this sense, 5-HT strongly inhibits NOD2 expression. In addition, NOD2 showed greater interdependence with TLR2 than with TLR4. Indeed, NOD2 expression significantly increased in both cells treated with TLR2 agonists and the intestinal tract of Tlr2-/- mice. Conclusions: It may be inferred from our data that NOD2 could play a role in intestinal pathophysiology not only through its inherent innate immune role but also due to its interaction with other receptors as TLR2 and the modulation of the intestinal serotonergic system decreasing SERT activity and expression.
Constructing Supply Chains in Open Source Software

The supply chain is an extremely successful way to cope with the risk posed by distributed decision making in product sourcing and distribution. While open source software has similarly distributed decision making and involves code and information flows similar to those in ordinary supply chains, the actual networks necessary to quantify and communicate risks in software supply chains have not been constructed on large scale. This work proposes to close this gap by measuring dependency, code reuse, and knowledge flow networks in open source software. We have done preliminary work by developing suitable tools and methods that rely on public version control data to measure and comparing these networks for R language and emberjs packages. We propose ways to calculate the three networks for the entirety of public software, evaluate their accuracy, and to provide public infrastructure to build risk assessment and mitigation tools for various individual and organizational participants in open sources software. We hope that this infrastructure will contribute to more predictable experience with OSS and lead to its even wider adoption.
Wireless Mediation of Multiple Equi-Priority Events in Time-Critical Industrial Applications

Wireless technologies are nowadays being considered for implementation in industrial automation. However due to strict reliability and timeliness requirements of time-critical applications, there are many open research challenges for the merger of wireless technologies with the industrial systems. Although many medium access and control (MAC) protocols are proposed in recent years, a coherent effort on both the physical (PHY) and MAC layers is needed. In this paper, we propose a protocol termed as multiple equi-priority MAC (MEP-MAC) which combines the functions of MAC and PHY layers: the MAC layer ensures a deterministic behavior of the system by assigning priorities to the nodes, while non-orthogonal multiple access (NOMA) at PHY layer enables multiple nodes of equal priorities to simultaneously gain the channel access and transmit data to the gateway. We adapt a discrete-time Markov chain (DTMC) model to handle multiple nodes of equal priorities and perform the analytical analysis of the proposed protocol. The results show that the proposed protocol can provide up to 70% and 40% improvement in terms of system throughput and latency respectively as compared to a system that does not leverage NOMA at PHY layer.
Separating the effects of nucleotide and EB binding on microtubule structure

Significance We report three high-resolution structures of microtubules in different nucleotide states—GMPCPP, GDP, and GTPγS—in the absence of any binding proteins, allowing us to separate the effects of nucleotide- and microtubule (MT)-associated protein (MAPs) binding on MT structure. End-binding (EB) proteins can bind and induce partial lattice compaction of a preformed GMPCPP-bound MT, a lattice type that is far from EBs’ ideal binding platform. We propose a model in which the MT lattice serves as a platform that integrates internal tubulin signals, such as nucleotide state, with outside signals, such as binding of MAPs. These global lattice rearrangements in turn affect the affinity of other MT partners and result in the exquisite regulation of the MT dynamics. Microtubules (MTs) are polymers assembled from αβ-tubulin heterodimers that display the hallmark behavior of dynamic instability. MT dynamics are driven by GTP hydrolysis within the MT lattice, and are highly regulated by a number of MT-associated proteins (MAPs). How MAPs affect MTs is still not fully understood, partly due to a lack of high-resolution structural data on undecorated MTs, which need to serve as a baseline for further comparisons. Here we report three structures of MTs in different nucleotide states (GMPCPP, GDP, and GTPγS) at near-atomic resolution and in the absence of any binding proteins. These structures allowed us to differentiate the effects of nucleotide state versus MAP binding on MT structure. Kinesin binding has a small effect on the extended, GMPCPP-bound lattice, but hardly affects the compacted GDP-MT lattice, while binding of end-binding (EB) proteins can induce lattice compaction (together with lattice twist) in MTs that were initially in an extended and more stable state. We propose a MT lattice-centric model in which the MT lattice serves as a platform that integrates internal tubulin signals, such as nucleotide state, with outside signals, such as binding of MAPs or mechanical forces, resulting in global lattice rearrangements that in turn affect the affinity of other MT partners and result in the exquisite regulation of MT dynamics.
Gene cassettes of class I integron-associated with antimicrobial resistance in isolates of Citrobacter spp. with multidrug resistance

Background and Objectives: Integrons play a major role in the transmission and accumulation of resistance factors in multidrug resistant bacteria. This study was aimed to evaluate the gene cassettes of class I integron and antimicrobial resistance in isolates of Citrobacter with multidrug resistance (MDR). Materials and Methods: Ninety isolates of Citrobacter spp. were collected from the largest hospital in Kermanshah, Iran. Antimicrobial resistance patterns were determined using disc diffusion method. The class I integron were detected by PCR. The integrase positive isolates were further analyzed for the presence of gene cassettes using 5′ and 3′ conserved sequences (CSs) primers and PCR products were sequenced. The data were analyzed using the chi-square test. Results: Of 90 Citrobacter isolates, 46 (51.1%) were multidrug resistant. Class I integron and gene cassettes were determined in 30 isolates (65.2%). Gene cassettes were found which contained genes encoded resistance to aminoglycosides and trimethoprim and a putative gene. Gene cassettes of dfrA12-orfF-aadA2, dfrA1-aadA1, aadA1 and dfrA15-aadA2 were also found in Citrobacter isolates. Conclusion: Our results indicate there is a high frequency of class I integron among multi-drug resistant strains of Citrobacter isolated from clinical settings. A high frequency of class I integron associated gene cassettes, in particular dfr and aadA, present in MDR strains of Citrobacter. This data indicates an important role of integrons in the creation and transmission of MDR strains in health care centers.
Atomic-scale strain manipulation of a charge density wave

Significance Charge density waves (CDWs) are simple periodic reorganizations of charge in a crystal, and yet they are still poorly understood and continue to bear surprises. External perturbations, such as strain or pressure, can in principle push a CDW phase into a different ordering geometry. However, engineering this type of quantum criticality has been experimentally challenging. Here, we implement a simple method for straining bulk materials. By applying it to 2H-NbSe2, a prototypical CDW system studied for decades, we discover two dramatic strain-induced CDW phase transitions. Our atomic-scale spectroscopic imaging measurements, combined with theory, reveal the distinct roles of electrons and phonons in forming these emergent states, thus opening a window into the rich phenomenology of CDWs. A charge density wave (CDW) is one of the fundamental instabilities of the Fermi surface occurring in a wide range of quantum materials. In dimensions higher than one, where Fermi surface nesting can play only a limited role, the selection of the particular wavevector and geometry of an emerging CDW should in principle be susceptible to controllable manipulation. In this work, we implement a simple method for straining materials compatible with low-temperature scanning tunneling microscopy/spectroscopy (STM/S), and use it to strain-engineer CDWs in 2H-NbSe2. Our STM/S measurements, combined with theory, reveal how small strain-induced changes in the electronic band structure and phonon dispersion lead to dramatic changes in the CDW ordering wavevector and geometry. Our work unveils the microscopic mechanism of a CDW formation in this system, and can serve as a general tool compatible with a range of spectroscopic techniques to engineer electronic states in any material where local strain or lattice symmetry breaking plays a role.
Feature Learning and Classification in Neuroimaging: Predicting Cognitive Impairment from Magnetic Resonance Imaging

Due to the rapid innovation of technology and the desire to find and employ biomarkers for neurodegenerative disease, high-dimensional data classification problems are routinely encountered in neuroimaging studies. To avoid over-fitting and to explore relationships between disease and potential biomarkers, feature learning and selection plays an important role in classifier construction and is an important area in machine learning. In this article, we review several important feature learning and selection techniques including lasso-based methods, PCA, the two-sample t-test, and stacked auto-encoders. We compare these approaches using a numerical study involving the prediction of Alzheimer's disease from Magnetic Resonance Imaging.
Tricalcium citrate – a new brittle tableting excipient for direct compression and dry granulation with enormous hardness yield

Abstract Objectives: Tricalcium citrate (TCC) was characterized as a tableting excipient for direct compression (DC) and dry granulation (DG). Significance: Brittle materials usually lead to tablets of inferior mechanical strength compared to plastic deforming materials. A brittle material exhibiting a high tabletability with the ability to retain that behavior during recompression would represent a valuable alternative to the commonly used microcrystalline cellulose. Methods: Tablets of TCC and other common fillers were directly compressed for the purpose of compression analysis including Heckel analysis, speed dependency, and lubricant sensitivity. DG by roller compaction of TCC was first simulated via briquetting and experiments were subsequently repeated on a roller compactor. Results: TCC appears as an excellent flowing powder of large agglomerates consisting of lower micron to submicron platelets. Despite the brittle deformation mechanism identified in the Heckel analysis, TCC demonstrated a very high mechanical strength up to 11 MPa in conjunction with an astonishingly low solid fraction of 0.85 at a compression pressure of 400 MPa. This was seen along with hardly any speed and lubricant sensitivity. Nevertheless, disintegration time was very short. TCC tablets suffered only a little from the re-compression: a slight loss in tensile strength of 1–2 MPa was observed for granules produced via roller compaction. Conclusions: TCC was found to be suitable for DC as a predominantly brittle deforming filler, nevertheless demonstrating an enormous hardness yield while being independent of lubrication and tableting speed. TCC furthermore retained enough bonding capacity after DG to maintain this pronounced tabletability.
CloudHeat

Datacenters are major energy consumers and dissipate an enormous amount of waste heat. Simple outdoor discharging of datacenter heat is energy-consuming and environmentally unfriendly. By harvesting datacenter waste heat and selling to the district heating system (DHS), both energy cost compensation and environment protection can be achieved. To realize such benefits in practice, an efficient market mechanism is required to incentivize the participation of datacenters. This work proposes CloudHeat, an online reverse auction mechanism for the DHS to solicit heat bids from datacenters. To minimize long-term social operational cost of the DHS and the datacenters, we apply a RFHC approach for decomposing the long-term problem into a series of one-round auctions, guaranteeing a small loss in competitive ratio. The one-round optimization is still NP-hard, and we employ a randomized auction framework to simultaneously guarantee truthfulness, polynomial running time, and an approximation ratio of 2. The performance of CloudHeat is validated through theoretical analysis and trace-driven simulation studies.
The Prescription of Benzodiazepines for Panic Disorder: Time for an Evidence-Based Educational Approach.

Antonio E. Nardi, MD,* Fiammetta Cosci, MD,† Richard Balon, MD,‡ Steven J. Weintraub, MD,§|| Rafael C. Freire, MD,* John H. Krystal, MD,¶#**†† Thomas Roth, PhD,‡‡ Edward K. Silberman, MD,§§ Nicoletta Sonino, MD,|||| Giovanni A. Fava, MD,¶¶## Vladan Starcevic, MD,*** Steven L. Dubovsky, MD,††† Carl Salzman, MD,‡‡‡ Karl Rickels, MD,§§§ David J. Greenblatt, MD,|||||| Richard I. Shader, MD,¶¶¶ and Guy Chouinard, MD,### The International Task Force on Benzodiazepines
Attack Detection and Isolation for Discrete-Time Nonlinear Systems

We address the problem of attack detection and isolation for a class of discrete-time nonlinear systems under (potentially unbounded) sensor attacks and measurement noise. We consider the case when a subset of sensors is subject to additive false data injection attacks. Using a bank of the observers, each observer leading to an Input-to-State Stable (ISS) estimation error, we propose two algorithms for detecting and isolating sensor attacks. These algorithms make use of the ISS property of the observers to check whether the trajectories of observers are "consistent" with the attack-free trajectories of the system. Simulations results are presented to illustrate the performance of the proposed algorithms.
The Role of Immune Checkpoint Inhibitors in Classical Hodgkin Lymphoma

Hodgkin Lymphoma (HL) is a unique disease entity both in its pathology and the young patient population that it primarily affects. Although cure rates are high, survivorship can be linked with significant long-term morbidity associated with both chemotherapy and radiotherapy. The most significant recent advances have been with the use of the anti-CD30-drug conjugated antibody brentuximab vedotin (BV) and inhibitors of program death 1 (PD-1). HL is genetically wired to up-regulate program death ligand 1 (PD-L1) in >95% of cases, creating a state of so-called “T cell exhaustion”, which can be reversed with immune checkpoint-inhibitor blockade. The overall and complete response rates to PD-1 inhibitors in patients with relapsed or refractory HL are 70% and 20%, respectively, with a long median duration of response of ~16 months. In fact, PD-1 inhibitors can benefit a wide spectrum of relapsed HL patients, including some who have “progressive disease” by strict response criteria. We review the biology of HL, with a focus on the immune micro-environment and mechanisms of immune evasion. We also provide the rationale supporting the use of PD-1 inhibitors in HL and highlight some of the challenges of monitoring disease response in patients treated with this immunotherapy.