Miracle-dz/UniFAR-FTUs huggingface dataset:
DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Deep neural networks are known to be annotation-hungry. Numerous efforts have been devoted to reducing the annotation cost when learning with deep networks. Two prominent directions include learning with noisy labels and semi-supervised learning by exploiting unlabeled data. In this work, we propose DivideMix, a novel framework for learning with noisy labels by leveraging semi-supervised learning techniques. In particular, DivideMix models the per-sample loss distribution with a mixture model to dynamically divide the training data into a labeled set with clean samples and an unlabeled set with noisy samples, and trains the model on both the labeled and unlabeled data in a semi-supervised manner. To avoid confirmation bias, we simultaneously train two diverged networks where each network uses the dataset division from the other network. During the semi-supervised training phase, we improve the MixMatch strategy by performing label co-refinement and label co-guessing on labeled and unlabeled samples, respectively. Experiments on multiple benchmark datasets demonstrate substantial improvements over state-of-the-art methods. Code is available at this https URL .
Performance Analysis of Quantized Uplink Massive MIMO-OFDM With Oversampling Under Adjacent Channel Interference

Massive multiple-input multiple-output (MIMO) systems have attracted much attention lately due to the many advantages they provide over single-antenna systems. Owing to the many antennas, low-cost implementation and low power consumption per antenna are desired. To that end, massive MIMO structures with low-resolution analog-to-digital converters (ADC) have been investigated in many studies. However, the effect of a strong interferer in the adjacent band on quantized massive MIMO systems have not been examined yet. In this study, we analyze the performance of uplink massive MIMO with low-resolution ADCs under frequency selective fading with orthogonal frequency division multiplexing (OFDM) in the perfect and imperfect receiver channel state information cases. We derive analytical expressions for the bit error rate and ergodic capacity. We show that the interfering band can be suppressed by increasing the number of antennas or the oversampling rate when a zero-forcing receiver is employed.
A Depth-Aware Swap Insertion Scheme for the Qubit Mapping Problem

The rapid progress of physical implementation of quantum computers paved the way of realising the design of tools to help users write quantum programs for any given quantum devices. The physical constraints inherent to the current NISQ architectures prevent most quantum algorithms from being directly executed on quantum devices. To enable two-qubit gates in the algorithm, existing works focus on inserting SWAP gates to dynamically remap logical qubits to physical qubits. However, their schemes lack the consideration of the depth of generated quantum circuits. In this work, we propose a depth-aware SWAP insertion scheme for qubit mapping problem in the NISQ era.
The Not So Simple Stellar System ω Cen. II. Evidence in Support of a Merging Scenario

We present multiband photometry covering ∼5° × 5° across ω Cen collected with the Dark Energy Camera on the 4 m Blanco telescope, combined with Hubble Space Telescope and Wide Field Imager data for the central regions. The unprecedented photometric accuracy and field coverage allows us to confirm the different spatial distribution of blue and red main-sequence stars, and of red giant branch (RGB) stars with different metallicities. The ratio of the number of blue to red main-sequence stars shows that the blue main-sequence stellar subpopulation has a more extended spatial distribution compared to the red main-sequence one, with the frequency of blue main-sequence stars increasing at a distance of ∼20′ from ω Cen’s center. Similarly, the more metal-rich RGB stars show a more extended spatial distribution compared to the more metal-poor ones in the outskirts of the cluster. Moreover, the centers of the distributions of metal-rich and metal-poor RGB stars are shifted in different directions with respect to the geometrical center of ω Cen. We constructed stellar density profiles for the blue and red main-sequence stars; they show that the blue main-sequence stellar subpopulation has a more extended spatial distribution compared to the red main-sequence one in the outskirts of ω Cen, confirming the results based on the number ratio. We also computed the ellipticity profile of ω Cen, which has a maximum value of 0.16 at a distance of ∼8′ from the center, and a minimum of 0.05 at ∼30′; the average ellipticity is ∼0.10. The circumstantial evidence presented in this work suggests a merging scenario for the formation of the peculiar stellar system ω Cen.
NGC 6397: The metallicity trend along the isochrone revisited

Context. Recent work has used spectra of ∼5000 stars in NGC 6397 that were extracted from a MUSE mosaic to determine the atmospheric parameters for these stars by fitting the spectra against the Göttingen Spectral Library. A significant change in metallicity between the turn off and the red giant branch was found and was discussed as a possible manifestation of predicted effects of atomic diffusion. However, the small amplitude of the effect and inconsistency with earlier measurements call for more attention before this result is interpreted. Systematic effects due to the interpolation or to the synthetic spectra cannot be ruled out at this level of precision. Aims. We reanalyze the data with : the ELODIE and MILES reference libraries in order to assess the robustness of the result. These empirical libraries have a finer metallicity coverage down to approximately the cluster metalicity turn-off. Methods. We used the ULySS full-spectrum fitting package, together with the library interpolators to remeasure the three atmospheric parameters effective temperature, surface gravity, and [Fe/H] metallicity. Results. We find a very low [Fe/H] dispersion along the isochrone (0.07 dex), consistent with our error estimate (0.05 dex). However, the [Fe/H] trend is not reproducible. This shows that the data have the potential to reveal patterns of the magnitude of the expected physical effects, but the analysis methods need to be refined to cull systematic effects that currently dominate the patterns.
Statistically Efficient, Polynomial-Time Algorithms for Combinatorial Semi-Bandits

We consider combinatorial semi-bandits over a set of arms X \subset \0,1\ ^d where rewards are uncorrelated across items. For this problem, the algorithm ESCB yields the smallest known regret bound R(T) = O( d (łn m)^2 (łn T) / Δ_\min ) after T rounds, where m = \max_x \in X 1^\top x. However, ESCB it has computational complexity O(|X|), which is typically exponential in d, and cannot be used in large dimensions. We propose the first algorithm that is both computationally and statistically efficient for this problem with regret R(T) = O( d (łn m)^2 (łn T) / Δ_\min ) and computational asymptotic complexity O(δ_T^-1 poly(d)), where δ_T is a function which vanishes arbitrarily slowly. Our approach involves carefully designing AESCB, an approximate version of ESCB with the same regret guarantees. We show that, whenever budgeted linear maximization over X can be solved up to a given approximation ratio, AESCB is implementable in polynomial time O(δ_T^-1 poly(d)) by repeatedly maximizing a linear function over X subject to a linear budget constraint, and showing how to solve these maximization problems efficiently.
Tackling Contention Through Cooperation: A Distributed Federation in LoRaWAN Space

Low-Power Wide Area Networks (LPWAN) play a key role in the IoT marketplace wherein LoRaWAN is considered a leading solution. Despite the traction of LoRaWAN, research shows that the current contention management mechanisms of LoRaWAN do not scale. This paper tackles contention on LoRaWAN by introducing FLIP, a fully distributed and open architecture for LoRaWAN that fundamentally rethinks how LoRa gateways should be managed and coordinated. FLIP transforms LoRa gateways into a federated network that provides inherent support for roaming while tackling contention using consensus-driven load balancing. FLIP offers identical security guarantees to LoRaWAN, is compatible with existing gateway hardware and requires no updates to end-device hardware or firmware. These features ensure the practicality of FLIP and provide a path to its adoption. We evaluate the performance of FLIP in a large-scale real-world deployment and demonstrate that FLIP delivers scalable roaming and improved contention management in comparison to LoRaWAN. FLIP achieves these benefits within the resource constraints of conventional LoRa gateways and requires no server hardware.
Susceptibility of Chilo partellus Swinhoe, 1885 (Lepidoptera: Crambidae) to some commonly used insecticides

Chilo partellus Swinhoe, 1885 (Lepidoptera: Crambidae), Turkiye’de Akdeniz Bolgesi’nde son zamanlarda saptanan oldukca zararli bir misir zararlisidir. Istilaci bir tur olarak degerlendirilen C. partellus , dunyanin bircok bolgesinde diger yerli misirkurtlarinin yerini almaktadir. Bu amacla, Turkiye’de misir uretim alanlarinda diger Lepidopter turlerine karsi yaygin olarak kullanilan 8 insektisidin onerilen dozlarinin C. partellus ’un yumurta ve birinci donem larvalarina etkisi, Cukurova Universitesi Ziraat Fakultesi Bitki Koruma Bolumu laboratuvarinda 27±2oC, %70 orantili nem, 14:10 (A: K) kosullarda arastirilmistir. Bu calismayla insektisitlerin yumurtalarin acilma oranlari ve larva olum oranlari uzerinde etkili oldugu bulunmustur. En dusuk acilma orani deltamethrinde %30.6 olarak belirlenmistir. Diger tum insektisit uygulamalarinda yumurta acilma oranlari %80’nin uzerinde saptanmistir. Larvalarin olum oranlarinda ise en yuksek oran %84.5 ile emamectin-benzoat’ta belirlenirken, en dusuk oran %38.2 ile lambda-cyhalothrinde belirlenmistir. Insektisite daldirilmis misir yaprak disklerinde ise birinci donem larvalarin en dusuk ve en yuksek olum oranlari, 72 saat sonra indoxacarb ve emamectin-benzoat icin sirasiyla %62.6 ve % 96.7 olmustur. Hayatta kalma analizleri, risk oranlarinin chlorpyrifos-ethyl ve emamectin-benzoat icin sirasiyla 4.9 (%95 CI: 1.66-14.6) ile 15.6 (%95 CI: 5.33-45.6) arasinda degistigini gostermistir. Birinci donem larvalarinin olum oranlarinin kontrol ile kiyaslandiginda emamectin-benzoate, lambda-cyhalothrin ve deltamethrinde sirasiyla 16, 10 ve 9 kat yuksek oldugu belirlenmistir. Ayrica tum uygulamalarda larvalarin davranislarinda azalma gozlenmistir.
Neural Model of Coding Stimulus Orientation and Adaptation

The coding of line orientation in the visual system has been investigated extensively. During the prolonged viewing of a stimulus, the perceived orientation continuously changes (normalization effect). Also, the orientation of the adapting stimulus and the background stimuli influence the perceived orientation of the subsequently displayed stimulus: tilt after-effect (TAE) or tilt illusion (TI). The neural mechanisms of these effects are not fully understood. The proposed model includes many local analyzers, each consisting of two sets of neurons. The first set has two independent cardinal detectors (CDs), whose responses depend on stimulus orientation. The second set has many orientation detectors (OD) tuned to different orientations of the stimulus. The ODs sum up the responses of the two CDs with respective weightings and output a preferred orientation depending on the ratio of CD responses. It is suggested that during prolonged viewing, the responses of the CDs decrease: the greater the excitation of the detector, the more rapid the decrease in its response. Thereby, the ratio of CD responses changes during the adaptation, causing the normalization effect and the TAE. The CDs of the different local analyzers laterally inhibit each other and cause the TI. We show that the properties of this model are consistent with both psychophysical and neurophysiological findings related to the properties of orientation perception, and we investigate how these mechanisms can affect the orientation's sensitivity.
Characterization of Rat ILCs Reveals ILC2 as the Dominant Intestinal Subset

Innate lymphoid cells (ILCs) are tissue-resident lymphocytes that lack antigen-specific receptors and exhibit innate effector functions such as cytokine production that play an important role in immediate responses to pathogens especially at mucosal sites. Mouse and human ILC subsets have been extensively characterized in various tissues and in blood. In this study, we present the first characterization of ILCs and ILC subsets in rat gut and secondary lymphoid organs using flow cytometry and single cell RNA sequencing. Our results show that phenotype and function of rat ILC subsets are conserved as compared to human and mouse ILCs. However, and in contrast to human and mouse, our study unexpectedly revealed that ILC2 and not ILC3 was the dominant ILC subset in the rat intestinal lamina propria. ILC2 predominance in the gut was independent of rat strain, sex or housing facility. In contrast, ILC3 was the predominant ILC subset in mesenteric lymph nodes and Peyer patches. In conclusion, our study demonstrates that in spite of highly conserved phenotype and function between mice, rat and humans, the distribution of ILC subsets in the intestinal mucosa is dependent on the species likely in response to both genetic and environmental factors.
Using Ultrasonic Waves to Determine the Microstructure of Snow

Acoustic signals interact with the physical structure of porous media, are particularly sensitive to porosity and tortuosity, and can be used to measure physical properties in a non-destructive manner. Given the fragile nature of freshly fallen snow, non-contact, non-destructive characterization methods made possible via acoustic signals, are desirable. High frequency wave propagation methods can be used to determine in-situ, near-surface, micro-pore geometry parameters in snow using methods demonstrated on cohesive porous materials, including manufactured foams, porous metals, and sintered glass beads. High frequency (90 kHz), oblique-angle and near vertical reflection measurements were conducted on snow samples in a cold room to demonstrate the feasibility of acoustic characterization. A nonlinear least squares regression to the theoretical reflection response was used to derive the best-fitting values for the porosity and tortuosity. We compared the acoustically-derived snow physical parameters, including porosity and tortuosity, with values determined from X-ray micro-computed tomography (μCT) for different snow types. The μCT-measured and acoustically-derived methods demonstrated strong agreement for porosity with differences averaging 8% for all samples. Tortuosity values, however, had average differences of roughly 20% for all samples. The different error rates might be caused by the stronger dependence of the acoustic reflection on porosity than on tortuosity. For both parameters, the small grain snow and large grain firn samples had errors much larger than the fresh or 1 day samples. Fresh snow has the lowest reflection coefficients and demonstrates a steady decrease from <0.1 at normal incidence as the angle increases. Aging fresh snow for 1 day caused detectable changes in acoustic response, from slight decreases in porosity, and slight increases in tortuosity that occurred from sintering.
Asprosin: A Novel Player in Metabolic Diseases

Asprosin, a novel glucogenic adipokine, is encoded by two exons (exon 65 and exon 66) of the gene Fibrillin 1 (FBN1) and mainly synthesized and released by white adipose tissue during fasting. Asprosin plays a complex role in the central nervous system (CNS), peripheral tissues, and organs. It is involved in appetite, glucose metabolism, insulin resistance (IR), cell apoptosis, etc. In this review, we will summarize the newly discovered roles of asprosin in metabolic diseases including diabetes, obesity, polycystic ovarian syndrome (PCOS), and cardiovascular disease (CVD), which may contribute to future clinical diagnosis and treatment.
Drivers of the Low Metabolic Rates of Seagrass Meadows in the Red Sea

Tropical seagrass meadows are highly productive ecosystems that thrive in oligotrophic environments. The Red Sea is characterized by strong N–S latitudinal nutrient and temperature gradients, which constrain pelagic productivity. To date, the influence of these natural gradients have not been assessed in metabolic rates for local seagrass communities. Here we report metabolic rates [gross primary production (GPP), respiration (R), and net community production (NCP)] in four common species of seagrass (Halodule uninervis, Halophila ovalis, Halophila stipulacea, and Thalassia hemprichii) along latitudinal and thermal gradients in the Red Sea. In addition, we quantified leaf nutrient concentration (nitrogen, phosphorous, and iron), and correlate this with latitude. Our results show that average metabolic rates and aboveground biomass of seagrass meadows in the Red Sea were generally in the lower range when compared to global values reported for the same species elsewhere. The optimum temperature of Red Sea seagrass meadows varied among species with increases along the sequence: H. stipulacea < T. hemprichii < H. uninervis ∼ H. ovalis. GPP for H. uninervis – a seagrass thermophile – was lowest in higher latitudes and increased toward lower latitudes during the summer months. While temperature was identified as a strong driver of metabolic rates across seagrass meadows, leaf concentration of phosphorous and iron (but not nitrogen) was below nutrient sufficiency thresholds, indicating these two elements might be limiting for seagrass meadows in the Red Sea.
Growth and Nitrate Uptake in Nannochloropsis gaditana and Tetraselmis chuii Cultures Grown in Sequential Batch Reactors

This study aims to determine the maximum specific biomass growth rates of Nannochloropsis gaditana and Tetraselmis chuii grown in sequential batch reactors. The maximum specific growth rates of N. gaditana and T. chuii were found to be 0.23 ± 0.04 gVSS/gVSS⋅d and 0.22 ± 0.05 gVSS/gVSS⋅d respectively. Optical density measured at 550 nm (OD550) accurately predict volatile suspended solids (VSS) concentration in N. gaditana growth media but not in T. chuii growth media. We observed decoupling of nitrate uptake from cell growth for both microalgae as growth continued well beyond the point where concentrations of nitrate in the growth media fell below the detection limit. This nitrate decoupling highlights the challenges in current microalgae growth models as growth was independent of the extracellular dissolved nitrogen in the system.
Applying Machine Learning to Ultrafast Shape Recognition in Ligand-Based Virtual Screening

Ultrafast Shape Recognition (USR), along with its derivatives, are Ligand-Based Virtual Screening (LBVS) methods that condense 3-dimensional information about molecular shape, as well as other properties, into a small set of numeric descriptors. These can be used to efficiently compute a measure of similarity between pairs of molecules using a simple inverse Manhattan Distance metric. In this study we explore the use of suitable Machine Learning techniques that can be trained using USR descriptors, so as to improve the similarity detection of potential new leads. We use molecules from the Directory for Useful Decoys-Enhanced to construct machine learning models based on three different algorithms: Gaussian Mixture Models (GMMs), Isolation Forests and Artificial Neural Networks (ANNs). We train models based on full molecule conformer models, as well as the Lowest Energy Conformations (LECs) only. We also investigate the performance of our models when trained on smaller datasets so as to model virtual screening scenarios when only a small number of actives are known a priori. Our results indicate significant performance gains over a state of the art USR-derived method, ElectroShape 5D, with GMMs obtaining a mean performance up to 430% better than that of ElectroShape 5D in terms of Enrichment Factor with a maximum improvement of up to 940%. Additionally, we demonstrate that our models are capable of maintaining their performance, in terms of enrichment factor, within 10% of the mean as the size of the training dataset is successively reduced. Furthermore, we also demonstrate that running times for retrospective screening using the machine learning models we selected are faster than standard USR, on average by a factor of 10, including the time required for training. Our results show that machine learning techniques can significantly improve the virtual screening performance and efficiency of the USR family of methods.
The Streptomyces coelicolor Small ORF trpM Stimulates Growth and Morphological Development and Exerts Opposite Effects on Actinorhodin and Calcium-Dependent Antibiotic Production

In actinomycetes, antibiotic production is often associated with a morpho-physiological differentiation program that is regulated by complex molecular and metabolic networks. Many aspects of these regulatory circuits have been already elucidated and many others still deserve further investigations. In this regard, the possible role of many small open reading frames (smORFs) in actinomycete morpho-physiological differentiation is still elusive. In Streptomyces coelicolor, inactivation of the smORF trpM (SCO2038) – whose product modulates L-tryptophan biosynthesis – impairs production of antibiotics and morphological differentiation. Indeed, it was demonstrated that TrpM is able to interact with PepA (SCO2179), a putative cytosol aminopeptidase playing a key role in antibiotic production and sporulation. In this work, a S. coelicolor trpM knock-in (Sco-trpMKI) mutant strain was generated by cloning trpM into overexpressing vector to further investigate the role of trpM in actinomycete growth and morpho-physiological differentiation. Results highlighted that trpM: (i) stimulates growth and actinorhodin (ACT) production; (ii) decreases calcium-dependent antibiotic (CDA) production; (iii) has no effect on undecylprodigiosin production. Metabolic pathways influenced by trpM knock-in were investigated by combining two-difference in gel electrophoresis/nanoliquid chromatography coupled to electrospray linear ion trap tandem mass spectrometry (2D-DIGE/nanoLC-ESI-LIT-MS/MS) and by LC-ESI-MS/MS procedures, respectively. These analyses demonstrated that over-expression of trpM causes an over-representation of factors involved in protein synthesis and nucleotide metabolism as well as a down-representation of proteins involved in central carbon and amino acid metabolism. At the metabolic level, this corresponded to a differential accumulation pattern of different amino acids – including aromatic ones but tryptophan – and central carbon intermediates. PepA was also down-represented in Sco-trpMKI. The latter was produced as recombinant His-tagged protein and was originally proven having the predicted aminopeptidase activity. Altogether, these results highlight the stimulatory effect of trpM in S. coelicolor growth and ACT biosynthesis, which are elicited through the modulation of various metabolic pathways and PepA representation, further confirming the complexity of regulatory networks that control antibiotic production in actinomycetes.