Miracle-dz/UniFAR-FTUs huggingface dataset:
Analyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected?

Several studies have identified discrepancies between the popularity of items in user profiles and the corresponding recommendation lists. Such behavior, which concerns a variety of recommendation algorithms, is referred to as popularity bias. Existing work predominantly adopts simple statistical measures, such as the difference of mean or median popularity, to quantify popularity bias. Moreover, it does so irrespective of user characteristics other than the inclination to popular content. In this work, in contrast, we propose to investigate popularity differences (between the user profile and recommendation list) in terms of median, a variety of statistical moments, as well as similarity measures that consider the entire popularity distributions (Kullback-Leibler divergence and Kendall’s τ rank-order correlation). This results in a more detailed picture of the characteristics of popularity bias. Furthermore, we investigate whether such algorithmic popularity bias affects users of different genders in the same way. We focus on music recommendation and conduct experiments on the recently released standardized LFM-2b dataset, containing listening profiles of Last.fm users. We investigate the algorithmic popularity bias of seven common recommendation algorithms (five collaborative filtering and two baselines). Our experiments show that (1) the studied metrics provide novel insights into popularity bias in comparison with only using average differences, (2) algorithms less inclined towards popularity bias amplification do not necessarily perform worse in terms of utility (NDCG), (3) the majority of the investigated recommenders intensify the popularity bias of the female users.
Persistence in a large network of locally interacting neurons

This article presents a biological neural network model driven by inhomogeneous Poisson processes accounting for the intrinsic randomness of synapses. The main novelty is the introduction of local interactions: each firing neuron triggers an instantaneous increase in electric potential to a fixed number of randomly chosen neurons. We prove that, as the number of neurons approaches infinity, the finite network converges to a nonlinear meanfield process characterised by a jump-type stochastic differential equation. We show that this process displays a phase transition: the activity of a typical neuron in the infinite network either rapidly dies out, or persists forever, depending on the global parameters describing the intensity of interconnection. This provides a way to understand the emergence of persistent activity triggered by weak input signals in large neural networks.
Active Assessment of Prediction Services as Accuracy Surface Over Attribute Combinations

Our goal is to evaluate the accuracy of a black-box classification model, not as a single aggregate on a given test data distribution, but as a surface over a large number of combinations of attributes characterizing multiple test data distributions. Such attributed accuracy measures become important as machine learning models get deployed as a service, where the training data distribution is hidden from clients, and different clients may be interested in diverse regions of the data distribution. We present Attributed Accuracy Assay (AAA)--a Gaussian Process (GP)--based probabilistic estimator for such an accuracy surface. Each attribute combination, called an 'arm', is associated with a Beta density from which the service's accuracy is sampled. We expect the GP to smooth the parameters of the Beta density over related arms to mitigate sparsity. We show that obvious application of GPs cannot address the challenge of heteroscedastic uncertainty over a huge attribute space that is sparsely and unevenly populated. In response, we present two enhancements: pooling sparse observations, and regularizing the scale parameter of the Beta densities. After introducing these innovations, we establish the effectiveness of AAA in terms of both its estimation accuracy and exploration efficiency, through extensive experiments and analysis.
LayerPipe: Accelerating Deep Neural Network Training by Intra-Layer and Inter-Layer Gradient Pipelining and Multiprocessor Scheduling

The time required for training the neural networks increases with size, complexity, and depth. Training model parameters by backpropagation inherently creates feedback loops. These loops hinder efficient pipelining and scheduling of the tasks within the layer and between consecutive layers. Prior approaches, such as PipeDream, have exploited the use of delayed gradient to achieve inter-layer pipelining. However, these approaches treat the entire backpropagation as a single task; this leads to an increase in computation time and processor underutilization. This paper presents novel optimization approaches where the gradient computations with respect to the weights and the activation functions are considered independently; therefore, these can be computed in parallel. This is referred to as intra-layer optimization. Additionally, the gradient computation with respect to the activation function is further divided into two parts and distributed to two consecutive layers. This leads to balanced scheduling where the computation time of each layer is the same. This is referred to as inter-layer optimization. The proposed system, referred to as LayerPipe, reduces the number of clock cycles required for training while maximizing processor utilization with minimal inter-processor communication overhead. LayerPipe achieves an average speedup of 25 % and upwards of 80% with 7 to 9 processors with less communication overhead when compared to PipeDream.
Unveiling and engineering of third-order optical nonlinearities in NiCo2O4 nanoflowers.

In this Letter, we demonstrate for the first time, to the best of our knowledge, NiCo2O4 (NCO) as a novel nonlinear optical material with straightforward potential applications in optical limiting. For the 532 nm nanosecond laser, excited state absorption (ESA) and free-carrier absorption give rise to large ESA coefficient (βESA) and positive nonlinear n2. On the other hand, when excited with the 800 nm femtosecond laser, two-photon absorption (TPA) takes place, and bound carriers induce strong negative n2. The values of β and n2 obtained for NCO are found to be higher compared to other conventional transition metal oxides and, therefore, are promising for optics and other photonics applications.
Implicitly Regularized RL with Implicit Q-Values

The $Q$-function is a central quantity in many Reinforcement Learning (RL) algorithms for which RL agents behave following a (soft)-greedy policy w.r. t. to $Q$. It is a powerful tool that allows action selection without a model of the environment and even without explicitly modeling the policy. Yet, this scheme can only be used in discrete action tasks, with small numbers of actions, as the softmax cannot be computed exactly otherwise. Especially the usage of function approximation, to deal with continuous action spaces in modern actor-critic architectures, intrinsically prevents the exact computation of a softmax. We propose to alleviate this issue by parametrizing the $Q$-function implicitly, as the sum of a log-policy and of a value function. We use the resulting parametrization to derive a practical off-policy deep RL algorithm, suitable for large action spaces, and that enforces the softmax relation between the policy and the $Q$-value. We provide a theoretical analysis of our algorithm: from an Approximate Dynamic Programming perspective, we show its equivalence to a regularized version of value iteration, accounting for both entropy and Kullback-Leibler regularization, and that enjoys beneficial error propagation results. We then evaluate our algorithm on classic control tasks, where its results compete with state-of-the-art methods.
Integrated Sensing and Communications: Toward Dual-Functional Wireless Networks for 6G and Beyond

As the standardization of 5G solidifies, researchers are speculating what 6G will be. The integration of sensing functionality is emerging as a key feature of the 6G Radio Access Network (RAN), allowing for the exploitation of dense cell infrastructures to construct a perceptive network. In this IEEE Journal on Selected Areas in Communications (JSAC) Special Issue overview, we provide a comprehensive review on the background, range of key applications and state-of-the-art approaches of Integrated Sensing and Communications (ISAC). We commence by discussing the interplay between sensing and communications (S&C) from a historical point of view, and then consider the multiple facets of ISAC and the resulting performance gains. By introducing both ongoing and potential use cases, we shed light on the industrial progress and standardization activities related to ISAC. We analyze a number of performance tradeoffs between S&C, spanning from information theoretical limits to physical layer performance tradeoffs, and the cross-layer design tradeoffs. Next, we discuss the signal processing aspects of ISAC, namely ISAC waveform design and receive signal processing. As a step further, we provide our vision on the deeper integration between S&C within the framework of perceptive networks, where the two functionalities are expected to mutually assist each other, i.e., via communication-assisted sensing and sensing-assisted communications. Finally, we identify the potential integration of ISAC with other emerging communication technologies, and their positive impacts on the future of wireless networks.
An Effective Non-Autoregressive Model for Spoken Language Understanding

Spoken Language Understanding (SLU), a core component of the task-oriented dialogue system, expects a shorter inference latency due to the impatience of humans. Non-autoregressive SLU models clearly increase the inference speed but suffer uncoordinated-slot problems caused by the lack of sequential dependency information among each slot chunk. To gap this shortcoming, in this paper, we propose a novel non-autoregressive SLU model named Layered-Refine Transformer, which contains a Slot Label Generation (SLG) task and a Layered Refine Mechanism (LRM). SLG is defined as generating the next slot label with the token sequence and generated slot labels. With SLG, the non-autoregressive model can efficiently obtain dependency information during training and spend no extra time in inference. LRM predicts the preliminary SLU results from Transformer's middle states and utilizes them to guide the final prediction. Experiments on two public datasets indicate that our model significantly improves SLU performance (1.5% on Overall accuracy) while substantially speed up (more than 10 times) the inference process over the state-of-the-art baseline.
Unifying Futures and Spot Market: Overbooking-Enabled Resource Trading in Mobile Edge Networks

Securing necessary resources for edge computing processes via effective resource trading becomes a critical technique in supporting computation-intensive mobile applications. Conventional onsite spot trading could facilitate this paradigm with proper incentives, which, however, incurs excessive decision-making latency/energy consumption, and further leads to underutilization of dynamic resources. Motivated by this, a hybrid market unifying futures and spot is proposed to facilitate resource trading among an edge server (seller) and multiple smart devices (buyers) by encouraging some buyers to sign a forward contract with seller in advance, while leaving the remaining buyers to compete for available resources with spot trading. Specifically, overbooking is adopted to achieve substantial utilization and profit advantages owing to dynamic resource demands. By integrating overbooking into futures market, mutually beneficial and risk-tolerable forward contracts with appropriate overbooking rate can be achieved relying on analyzing historical statistics associated with future resource demand and communication quality, which are determined by an alternative optimization-based negotiation scheme. Besides, spot trading problem is studied via considering uniform/differential pricing rules, for which two bilateral negotiation schemes are proposed by addressing both non-convex optimization and knapsack problems. Experimental results demonstrate that the proposed mechanism achieves mutually beneficial player’s utilities, while outperforming baseline methods on critical indicators, e.g., decision-making latency, resource usage, etc.
Photoemission Spectra from the Extended Koopman’s Theorem, Revisited

The Extended Koopman’s Theorem (EKT) provides a straightforward way to compute charged excitations from any level of theory. In this work we make the link with the many-body effective energy theory (MEET) that we derived to calculate the spectral function, which is directly related to photoemission spectra. In particular, we show that at its lowest level of approximation the MEET removal and addition energies correspond to the so-called diagonal approximation of the EKT. Thanks to this link, the EKT and the MEET can benefit from mutual insight. In particular, one can readily extend the EKT to calculate the full spectral function, and choose a more optimal basis set for the MEET by solving the EKT secular equation. We illustrate these findings with the examples of the Hubbard dimer and bulk silicon.
Better GPU Hash Tables

We revisit the problem of building static hash tables on the GPU and design and build three bucketed hash tables that use different probing schemes. Our implementations are lock-free and offer efficient memory access patterns; thus, only the probing scheme is the factor affecting the performance of the hash table's different operations. Our results show that a bucketed cuckoo hash table that uses three hash functions (BCHT) outperforms alternative methods that use power-of-two choices, iceberg hashing, and a cuckoo hash table that uses a bucket size one. At high load factors as high as 0.99, BCHT enjoys an average probe count of 1.43 during insertion. Using three hash functions only, positive and negative queries require at most 1.39 and 2.8 average probes per key, respectively.
Autocomplete Repetitive Stroking with Image Guidance

Image-guided drawing can compensate for the lack of skills but often requires a significant number of repetitive strokes to create textures. Existing automatic stroke synthesis methods are usually limited to predefined styles or require indirect manipulation that may break the spontaneous flow of drawing. We present a method to autocomplete repetitive short strokes during users’ normal drawing process. Users can draw over a reference image as usual. At the same time, our system silently analyzes the input strokes and the reference to infer strokes that follow users’ input style when certain repetition is detected. Our key idea is to jointly analyze image regions and operation history for detecting and predicting repetitions. The proposed system can reduce tedious repetitive inputs while being fully under user control.
Sums of squares, Hankel index, and almost real rank

The Hankel index of a real variety $X$ is an invariant that quantifies the difference between nonnegative quadrics and sums of squares on $X$. In [5], the authors proved an intriguing bound on the Hankel index in terms of the Green-Lazarsfeld index, which measures the "linearity" of the minimal free resolution of the ideal of $X$. In all previously known cases this bound was tight. We provide the first class of examples where the bound is not tight; in fact the difference between Hankel index and Green-Lazarsfeld index can be arbitrarily large. Our examples are outer projections of rational normal curves, where we identify the center of projection with a binary form $F$. The Green-Lazarsfeld index of the projected curve is given by the complex Waring border rank of $F$ [15]. We show that the Hankel index is given by the "almost real" rank of $F$, which is a new notion that comes from decomposing $F$ as a sum of powers of almost real forms. We determine the range of possible and typical almost real ranks for binary forms.
Reassessing the Limitations of CNN Methods for Camera Pose Regression

In this paper, we address the problem of camera pose estimation in outdoor and indoor scenarios. In comparison to the currently top-performing methods that rely on 2D to 3D matching, we propose a model that can directly regress the camera pose from images with significantly higher accuracy than existing methods of the same class. We first analyse why regression methods are still behind the state-of-the-art, and we bridge the performance gap with our new approach. Specifically, we propose a way to overcome the biased training data by a novel training technique, which generates poses guided by a probability distribution from the training set for synthesising new training views. Lastly, we evaluate our approach on two widely used benchmarks and show that it achieves significantly improved performance compared to prior regression-based methods, retrieval techniques as well as 3D pipelines with local feature matching.
Degradation of Carbon Tetrachloride by nanoscale Zero‐Valent Iron @ magnetic Fe3O4: Impact of reaction condition, Kinetics, Thermodynamics and Mechanism

Nano-scale zero-valent Iron (nZVI) attached on the Fe3O4 nanoparticles were prepared and creatively applied in the reductive dechlorination of Carbon Tetrachloride (CT). The characterization results of the synthesized composite indicated a main component of nZVI particles assembled on the surface of Fe3O4 with a layer of iron-oxide film on the periphery, of which the dispersibility was better and the specific surface area was larger. The effects of different reaction conditions like temperature, initial pH values, Fe0@Fe3O4 dosage and initial CT concentrations on the removal of CT were evaluated. Under the optimum conditions, the Fe0@Fe3O4 composites showed a CT removal efficiency of 89.1% in 60 min, which was much greater than that of nZVI (61.7%) and Fe3O4 particles (14.3%). The removal process obeyed the pseudo-first-order kinetic model. Synergy effects of the constituents in the composite which can promote the relative rates of mass transfer to reactive sites were proposed to be existed and the magnetism of Fe3O4 could help to overcome the aggregation and surface passivation problem of nZVI. Thus, Fe0@Fe3O4 nanoparticles in our study can effectively complete the reductive dechlorination of CT and an improved nZVI catalyst is provided for the remediation of chlorinated organic compounds.
C4Tech: Virtual connections between the classrooms, clinicians, and community clinics to bridge the gap between research and practice

Abstract Aim E-learning technologies are becoming vital components of medical and health professions education, as highlighted during the current coronavirus disease (COVID-19) pandemic. The National Academy of Medicine (NAM) considers education technologies essential to forming connections between education and healthcare delivery systems, which promote evidence-based practice and continuous learning and quality improvement in healthcare. There is a lack of evidence-based models to guide the integration of technology in medical and health profession education, in particular models that form synergistic linkages between healthcare education and delivery systems. This paper presents the evaluation of an innovative blended learning model, which leverages virtual technology to connect students in the classroom with clinicians in community clinics (C4Tech) for authentic learning related to quality improvement (QI) and social determinants of health (SDH). Method This study applied a case study approach to evaluate the efficacy of the C4Tech model in supporting learning outcomes and assessed how virtual collaboration influenced the process of learning. Results This study contributes to a more comprehensive understanding of how to design effective blended courses that connect the healthcare education and delivery systems through virtual technology. It also demonstrates how to connect students and practicing clinicians virtually to design evidence-based quality improvement projects.
Retrospective cohort study of changes in estimated glomerular filtration rate for patients prescribed a low carb diet

Supplemental Digital Content is available in the text Purpose of review Obesity and diabetes contribute to chronic kidney disease (CKD) and accelerate the loss of kidney function. Low carbohydrate diets (LCDs) are associated with weight loss and improved diabetes control. Compared to the typical Western diet, LCDs contain more protein, so individuals with CKD are not included in studies of LCDs. Therefore, there are no studies of LCDs for weight loss and their effects on kidney function. Recent findings Obesity, hyperglycemia, and hyperinsulinemia can be detrimental to kidney function. LCDs may improve kidney function in patients with obesity and diabetes because they are associated with weight loss, improve blood sugar control, and decrease endogenous insulin production and exogenous insulin requirements. Summary In this study, for patients with mildly reduced and moderately to severely reduced kidney function who were prescribed an LCD, their estimated glomerular filtration rate (eGFR) was either unchanged or improved. For those with normal or elevated eGFR, their kidney function was slightly decreased. For those without diabetes, greater weight loss was associated with improved eGFR. Future studies should prospectively measure low carbohydrate dietary adherence and physical activity and directly measure changes in GFR and albuminuria for participants with CKD before and during that diet.
The COVID-19 pandemic altered the modality, but not the frequency, of formal cognitive assessment

Abstract Purpose We investigated the impacts of the COVID-19 pandemic on the modality of formal cognitive assessments (in-person versus remote assessments). Materials and methods We created a web-based survey with 34 items and collected data from 114 respondents from a range of health care professions and settings. We established the proportion of cognitive assessments which were face-to-face or via video or telephone conferencing, both pre- and post-March 2020. Further, we asked respondents about the assessment tools used and perceived barriers, challenges, and facilitators for the remote assessment of cognition. In addition, we asked questions specifically about the use of the Oxford Cognitive Screen. Results We found that the frequency of assessing cognition was stable compared to pre-pandemic levels. Use of telephone and video conferencing cognitive assessments increased by 10% and 18% respectively. Remote assessment increased accessibility to participants and safety but made observing the subtleties of behaviour during test administration difficult. The respondents called for an increase in the availability of standardised, validated, and normed assessments. Conclusions We conclude that the pandemic has not been detrimental to the frequency of cognitive assessments. In addition, a shift in clinical practice to include remote cognitive assessments is clear and wider availability of validated and standardised remote assessments is necessary. IMPLICATIONS FOR REHABILITATION We caution the wider use and interpretation of remote formal cognitive assessments due to lack of validated, standardised, and normed assessments in a remote format. Clinicians should seek out the latest validation and normative data papers to ensure they are using the most up to date tests and respective cut offs. Support is needed for individuals who lack knowledge/have anxiety over the use of technology in formal cognitive assessments