Miracle-dz/UniFAR-FTUs huggingface dataset:
Fundamental Limits and Tradeoffs in Invariant Representation Learning

A wide range of machine learning applications such as privacy-preserving learning, algorithmic fairness, and domain adaptation/generalization among others, involve learning invariant representations of the data that aim to achieve two competing goals: (a) maximize information or accuracy with respect to a target response, and (b) maximize invariance or independence with respect to a set of protected features (e.g., for fairness, privacy, etc). Despite their wide applicability, theoretical understanding of the optimal tradeoffs -- with respect to accuracy, and invariance -- achievable by invariant representations is still severely lacking. In this paper, we provide an information theoretic analysis of such tradeoffs under both classification and regression settings. More precisely, we provide a geometric characterization of the accuracy and invariance achievable by any representation of the data; we term this feasible region the information plane. We provide an inner bound for this feasible region for the classification case, and an exact characterization for the regression case, which allows us to either bound or exactly characterize the Pareto optimal frontier between accuracy and invariance. Although our contributions are mainly theoretical, a key practical application of our results is in certifying the potential sub-optimality of any given representation learning algorithm for either classification or regression tasks. Our results shed new light on the fundamental interplay between accuracy and invariance, and may be useful in guiding the design of future representation learning algorithms.
Ground state energy threshold and blow-up for NLS with competing nonlinearities

We consider the nonlinear Schr\"odinger equation with combined nonlinearities, where the leading term is an intracritical focusing power-type nonlinearity, and the perturbation is given by a power-type defocusing one. We completely answer the question wether the ground state energy, which is a threshold between global existence and formation of singularities, is achieved. For any prescribed mass, for mass-supercritical or mass-critical defocusing perturbations, the ground state energy is achieved by a radially symmetric and decreasing solution to the associated stationary equation. For mass-subcritical perturbations, we show the existence of a critical prescribed mass, precisely the mass of the unique, static, positive solution to the associated elliptic equation, such that the ground state energy is achieved for any mass equal or smaller than the critical one. Moreover, the ground state energy is not achieved for mass larger than the critical one. As a byproduct of the variational characterization of the ground state energy, we prove the existence of blowing-up solutions in finite time, for any energy below the ground state energy threshold.
Monotone Circuits

Maximal monotonicity is explored as a generalization of the linear theory of passivity, which allows for algorithmic system analysis of an important physical property. The theory is developed for nonlinear 1-port circuits, modelled as port interconnections of the four fundamental elements: resistors, capacitors, inductors and memristors. An algorithm for computing the steady state periodic behavior of such a circuit is presented.
DCCRGAN: Deep Complex Convolution Recurrent Generator Adversarial Network for Speech Enhancement

Generative adversarial network (GAN) based speech enhancement (SE) methods still exist some problems. Some GAN-based systems adopt the same structure from Pixel-to-Pixel directly without special optimization. The importance of the generator network has not been fully explored. Other related researches change the generator network and operate in the time-frequency domain, which ignores the importance of the phase component. In this paper, the real and imaginary components of noisy spectrogram are trained simultaneously. In order to train the complex target effectively, a deep complex convolution recurrent GAN (DCCRGAN) structure is proposed. The complex module builds the correlation between real and imaginary components of noisy spectrogram and has been proved to be effective. For the first time that a complex value network been introduced in GAN-based SE task and the performance of the complex GAN-based network been discussed adequately. Different LSTM layers are used in the generator network to sufficiently explore the speech enhancement performance of DCCRGAN. The experimental results confirm that the proposed DCCRGAN outperforms the state-of-the-art GAN-based SE systems.
On Success and Simplicity: A Second Look at Transferable Targeted Attacks

Achieving transferability of targeted attacks is reputed to be remarkably difficult. Currently, state-of-the-art approaches are resource-intensive because they necessitate training model(s) for each target class with additional data. In our investigation, we find, however, that simple transferable attacks which require neither additional data nor model training can achieve surprisingly high targeted transferability. This insight has been overlooked until now, mainly due to the widespread practice of unreasonably restricting attack optimization to a limited number of iterations. In particular, we, for the first time, identify that a simple logit loss can yield competitive results with the state of the arts. Our analysis spans a variety of transfer settings, especially including three new, realistic settings: an ensemble transfer setting with little model similarity, a worse-case setting with low-ranked target classes, and also a real-world attack against the Google Cloud Vision API. Results in these new settings demonstrate that the commonly adopted, easy settings cannot fully reveal the actual properties of different attacks and may cause misleading comparisons. We also show the usefulness of the simple logit loss for generating targeted universal adversarial perturbations in a data-free and training-free manner. Overall, the aim of our analysis is to inspire a more meaningful evaluation on targeted transferability. Code is available at https://github.com/ZhengyuZhao/Targeted-Tansfer
A Note on Graph-Based Nearest Neighbor Search

Nearest neighbor search has found numerous applications in machine learning, data mining and massive data processing systems. The past few years have witnessed the popularity of the graph-based nearest neighbor search paradigm because of its superiority over the space-partitioning algorithms. While a lot of empirical studies demonstrate the efficiency of graph-based algorithms, not much attention has been paid to a more fundamental question: why graph-based algorithms work so well in practice? And which data property affects the efficiency and how? In this paper, we try to answer these questions. Our insight is that"the probability that the neighbors of a point o tends to be neighbors in the KNN graph"is a crucial data property for query efficiency. For a given dataset, such a property can be qualitatively measured by clustering coefficient of the KNN graph. To show how clustering coefficient affects the performance, we identify that, instead of the global connectivity, the local connectivity around some given query q has more direct impact on recall. Specifically, we observed that high clustering coefficient makes most of the k nearest neighbors of q sit in a maximum strongly connected component (SCC) in the graph. From the algorithmic point of view, we show that the search procedure is actually composed of two phases - the one outside the maximum SCC and the other one in it, which is different from the widely accepted single or multiple paths search models. We proved that the commonly used graph-based search algorithm is guaranteed to traverse the maximum SCC once visiting any point in it. Our analysis reveals that high clustering coefficient leads to large size of the maximum SCC, and thus provides good answer quality with the help of the two-phase search procedure. Extensive empirical results over a comprehensive collection of datasets validate our findings.
Optimising Placement of Pollution Sensors in Windy Environments

Air pollution is one of the most important causes of mortality in the world. Monitoring air pollution is useful to learn more about the link between health and pollutants, and to identify areas for intervention. Such monitoring is expensive, so it is important to place sensors as efﬁciently as possible. Bayesian optimisation has proven useful in choosing sensor locations, but typically relies on kernel functions that neglect the statistical structure of air pollution, such as the tendency of pollution to propagate in the prevailing wind direction. We describe two new wind-informed kernels and investigate their advantage for the task of actively learning locations of maximum pollution using Bayesian optimisation.
Infrared image pedestrian target detection based on Yolov3 and migration learning

With the gradual application of infrared night vision vehicle assistance system in automatic driving, the accuracy of the collected infrared images of pedestrians is gradually improved. In this paper, the migration learning method is used to apply YOLOv3 model to realize pedestrian target detection in infrared images. The target detection model YOLOv3 is migrated to the CVC infrared pedestrian data set, and Diou loss is used to replace the loss function of the original YOLO model to test different super parameters to obtain the best migration learning effect. The experimental results show that in the pedestrian detection task of CVC data set, the average accuracy (AP) of Yolov3 model reaches 96.35%, and that of Diou-Yolov3 model is 72.14%, but the latter has a faster convergence rate of loss curve. The effect of migration learning can be obtained by comparing the two models.
Privacy Interpretation of Behavioural-based Anomaly Detection Approaches

This paper proposes the notion of 'Privacy-Anomaly Detection' and considers the question of whether behavioural-based anomaly detection approaches can have a privacy semantic interpretation and whether the detected anomalies can be related to the conventional (formal) definitions of privacy semantics such as k-anonymity. The idea is to learn the user's past querying behaviour in terms of privacy and then identifying deviations from past behaviour in order to detect privacy violations. Privacy attacks, violations of formal privacy definition, based on a sequence of SQL queries (query correlations) are also considered in the paper and it is shown that interactive querying settings are vulnerable to privacy attacks based on query sequences. Investigation on whether these types of privacy attacks can potentially manifest themselves as anomalies, specifically as privacy-anomalies was carried out. It is shown that in this paper that behavioural-based anomaly detection approaches have the potential to detect privacy attacks based on query sequences (violation of formal privacy definition) as privacy-anomalies.
Size Distribution of Emitted Energies in Local Load Sharing Fiber Bundles

We study the local load sharing fiber bundle model and its energy burst statistics. While it is known that the avalanche size distribution of the model is exponential, we numerically show here that the avalanche size (s) and the corresponding average energy burst (〈E〉) in this version of the model have a non-linear relation (〈E〉 ~ sγ). Numerical results indicate that γ ≈ 2.5 universally for different failure threshold distributions. With this numerical observation, it is then possible to show that the energy burst distribution is a power law, with a universal exponent value of −(γ + 1).
Stress relaxation in polymer melts and concentrated solutions

It is a widespread assumption that the backbone stress is dominant in polymer melts and concentrated solutions. This work suggests on the basis of Brownian simulations that the assumption is incorrect. The excluded volume stress seems to be much larger in entangled solutions of arbitrarily thin chains. A restriction to backbone stresses in the popular reptation model leads to the necessity for a rigid primitive path. To explain the persistence and eventual relaxation of the excluded volume stress, it suffices that entanglements provide elasticity on the distance scale of the radius of gyration.
The Endocannabinoid System as Modulator of Exercise Benefits in Mental Health

According to the World Health Organization (WHO), 47 million people display mental health disorders Worldwide. In addition, epidemiological studies have shown that the extension of life expectancy and the increase in aged population will significantly impact the prevalence of several mental impairments. Although there are strategies for preventing and alleviating mental illnesses, such as pharmacological and psychological approaches, limited results have been observed. Thus, the search for new therapeutics for managing psychiatric disorders has explored multiple roads. In recent years, it has been demonstrated that physical activity and exercise promote health benefits. On the other hand, among the neurobiological systems that participate in the genesis and development of mental disruptions, the endocannabinoid system has been suggested as an active player. Supporting this hypothesis, data suggest that the elements comprising the endocannabinoid system, such as the CB1/CB2 cannabinoid receptors, endogenous ligands (N-arachidonoylethanolamine [anandamide, AEA] and 2-arachidonoylglycerol [2-AG]), transporters and the enzymes involved in the biosynthesis and degradation of the AEA and 2-AG, modulate mental diseases. In this review, we discuss that the endocannabinoid system might be considered as a modulator for the positive outcomes of exercise in the management of mental disorders. Clinically, this promising field might be exploited by targeting the elements of the endocannabinoid system aimed to increase the exercise benefits applied to patients with mental illnesses.
Gene Expression Profiling in the Striatum of Per2 KO Mice Exhibiting More Vulnerable Responses against Methamphetamine

Drug addiction influences most communities directly or indirectly. Increasing studies have reported the relationship between circadian-related genes and drug addiction. Per2 disrupted mice exhibited more vulnerable behavioral responses against some drugs including methamphetamine (METH). However, its roles and mechanisms are still not clear. Transcriptional profiling analysis in Per2 knockout (KO) mice may provide a valuable tool to identify potential genetic involvement and pathways in enhanced behavioral responses against drugs. To explore the potential genetic involvement, we examined common differentially expressed genes (DEGs) in the striatum of drug naïve Per2 KO/wild-type (WT) mice, and before/after METH treatment in Per2 KO mice, but not in WT mice. We selected 9 common DEGs (Ncald, Cpa6, Pklr, Ttc29, Cbr2, Egr2, Prg4, Lcn2, and Camsap2) based on literature research. Among the common DEGs, Ncald, Cpa6, Pklr, and Ttc29 showed higher expression levels in drug naïve Per2 KO mice than in WT mice, while they were downregulated in Per2 KO mice after METH treatment. In contrast, Cbr2, Egr2, Prg4, Lcn2, and Camsap2 exhibited lower expression levels in drug naïve Per2 KO mice than in WT mice, while they were upregulated after METH treatment in Per2 KO mice. qRT-PCR analyses validated the expression patterns of 9 target genes before/after METH treatment in Per2 KO and WT mice. Although further research is required to deeply understand the relationship and roles of the 9 target genes in drug addiction, the findings from the present study indicate that the target genes might play important roles in drug addiction.
Endocrine Adverse Events Caused by Different Types and Different Doses of Immune Checkpoint Inhibitors in the Treatment of Solid Tumors: A Meta‐Analysis and Systematic Review

The aim of this meta‐analysis was to assess the risks of endocrine adverse events in patients with malignancies treated with different types and different doses of immune checkpoint inhibitors (ICIs). PubMed and Embase were searched for randomized controlled trials on ICIs and endocrine adverse events since 2000, and meta‐analysis was carried out. Twenty‐six randomized controlled trials comprising 13 824 patients with malignancies were included. Compared with the other tumor therapies (used as a control group), patients treated with programmed death‐1 inhibitors appeared to be at higher risks of hypothyroidism, hyperthyroidism, thyroiditis, hypophysitis or hypopituitarism, and type 1 diabetes mellitus, while there was no difference in the risk of primary adrenal insufficiency. It was also found that patients treated with cytotoxic T‐lymphocyte–associated protein‐4 inhibitors were at higher risk of hypophysitis or hypopituitarism, primary adrenal insufficiency, and hypothyroidism. In comparison, patients treated with programmed death‐ligand 1 inhibitors were at higher risk of hyperthyroidism and hypothyroidism. Compared with the control group, both low‐dose and high‐dose ICI groups were at higher risk of hypothyroidism and hyperthyroidism, and the low‐dose group had increased risk of thyroiditis and primary adrenal insufficiency. There was no significant difference in the risk of type 1 diabetes between the low‐dose group and the high‐dose group. The risk of hypophysitis or hypopituitarism in the high‐dose group (relative risk, 20.12; 95% confidence interval, 8.02‐50.46) was significantly higher than that in the low‐dose group (relative risk, 4.92; 95% confidence interval, 2.11‐11.47). The risk of endocrine adverse events was increased in patients treated with ICIs. Different types and doses of ICIs have varying characteristics of endocrine adverse events.
DESCRIPTIVE PROFILE OF SHOULDER RANGE OF MOTION AND STRENGTH IN YOUTH ATHLETES PARTICIPATING IN OVERHEAD SPORTS.

Background The unilateral and repetitive nature of overhead sports, often result in a biomechanical overload of the upper extremity. Understanding the musculoskeletal shoulder range of motion (ROM) and strength patterns in the youth sports of baseball, softball, and tennis could assist injury prevention screening and further the development of conditioning and rehabilitation programs. Purpose To generate a descriptive profile of shoulder musculoskeletal characteristics and determine whether bilateral differences in shoulder ROM exist in youth baseball, softball, and tennis athletes. A secondary aim was to determine whether shoulder rotational adaptations are correlated with playing position, sport, or years of experience. Study Design Descriptive Laboratory. Methods A total of 136 competitive youth overhead athletes (baseball: n = 51,12.8 ± 0.9yrs; softball: n = 63,12.3 ± 1.1yrs; and tennis: n = 22,12.5 ± 0.9yrs) participated. Bilateral shoulder internal (IR) and external (ER) passive ROM and external rotation strength were measured using an inclinometer and handheld dynamometer. Results Significant differences (p<.001) in bilateral shoulder ROM and ER strength were found between the athletes in the three sports. Post-hoc test revealed tennis athletes had greater bilateral shoulder ROM than both baseball and softball athletes, but baseball and softball athletes had greater bilateral ER strength than tennis athletes. There were no differences between baseball and softball athletes. Additionally, tennis athletes had greater bilateral internal rotation and total ROM but less ER strength than baseball pitchers, baseball positional athletes, softball pitchers, and softball positional athletes. There were no significant differences between positions and baseball and softball athletes. There were no significant correlations between playing position, sport, or years of experience. Conclusion The results of this study showed differences in shoulder passive ROM and strength adaptations between youth tennis, baseball, and softball athletes. The descriptive nature of this study is impactful as it presents specific ROM adaptions seen in this population. Future research is needed to further evaluate if the "at risk" ROM identified in older populations holds true in the youth population. Level of Evidence Diagnosis, Level 3b.
Neurophysiological assessment in a patient affected by Marfan syndrome

Background Marfan syndrome (MS) is a hereditary connective tissue disorder characterized by different multiorgan patterns. The guidelines for MS diagnosis do not highlight the usefulness—or even the use—of any neurophysiological techniques for diagnosing this disease. Moreover, few neurophysiological studies assessing the central and peripheral nervous systems in MS subjects have been reported to date. Case presentation: We describe a male patient affected by MS. To assess sensory and nociceptive pathways in this patient, a neurophysiological assessment was performed using electroencephalogram, nerve conduction studies, and somatosensory and laser-evoked potentials. To the best of our knowledge, this is the first published case report to evaluate the role of evoked potential assessments for the study of sensory and nociceptive pathways in MS. Conclusion Future studies should investigate the use of a complete neurophysiological approach for the clinical and therapeutic management of MS patients in a large sample.
The Diagnostic Value of Apparent Diffusion Coefficient and Proton Magnetic Resonance Spectroscopy in the Grading of Pediatric Gliomas

Objective The aims of this retrospective study were to assess the value of the quantitative analysis of apparent diffusion coefficient (ADC) and proton magnetic resonance spectroscopy (1H-MRS) metabolites in differentiating grades of pediatric gliomas. Patients and Methods Two hundred and nine pathology-confirmed pediatric gliomas (143 low-grade gliomas [LGGs] and 66 high-grade gliomas [HGGs]) were retrospectively analyzed on preoperative diffusion-weighted magnetic resonance imaging, of which 84 also underwent 1H-MRS. The mean tumor ADC (ADCmean), minimum tumor ADC (ADCmin), tumor/normal brain ADC ratio (ADC ratio), and metabolites (choline/creatine ratio [Cho/Cr], N-acetylaspartate/creatine ratio [NAA/Cr], N-acetylaspartate/choline ratio [NAA/Cho], presence of lactate and lipid peaks) between LGGs and HGGs were analyzed. Results There were significant negative correlations between the ADC values and glioma grade. Receiver operating characteristic analysis showed that the cutoff ADCmean value of 1.192 × 10−3 mm2/s for the differentiation between low- and high-grade pediatric gliomas provided a sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of 77.6%, 80.3%, 78.5%, 89.5% and 62.4%, respectively; the cutoff ADCmin value of 0.973 × 10−3 mm2/s resulted in a sensitivity, specificity, accuracy, PPV, and NPV of 86.0%, 90.9%, 87.6%, 95.3%, and 75.0%, respectively; the cutoff ADC ratio value of 1.384 resulted in a sensitivity, specificity, accuracy, PPV, and NPV of 73.4%, 87.9%, 78.0%, 92.9%, and 60.4%, respectively. A tendency for a positive correlation was found between Cho/Cr and glioma grade. A negative correlation was demonstrated between NAA/Cr or NAA/Cho and glioma grade. Statistical analysis demonstrated a threshold value of 2.601 for Cho/Cr to provide a sensitivity, specificity, accuracy, PPV, and NPV of 81.8%, 51.7%, 71.4%, 76.3%, and 60.0%, respectively, in dividing LGGs and HGGs; a threshold value of 0.705 for NAA/Cr to provide a sensitivity, specificity, accuracy, PPV, and NPV of 76.4%, 75.9%, 76.2%, 85.7%, and 62.9%, respectively; a threshold value of 0.349 for NAA/Cho to provide a sensitivity, specificity, accuracy, PPV, and NPV of 87.3%, 86.2%, 86.9%, 92.3%, and 78.1%, respectively. Conclusions The ADC values and metabolites appeared to be significantly correlated to grade in pediatric gliomas. The predictive values may be helpful for preoperative diagnostic predictions.