Miracle-dz/UniFAR-FTUs huggingface dataset:
Differential responses of soil nitrogen‐oxide emissions to organic substitution for synthetic fertilizer and biochar amendment in a subtropical tea plantation

Tropical and subtropical acidic soils have been well documented as hotspots of global soil nitrogen (N) oxide (i.e., nitrous oxide (N2O) and nitric oxide (NO) emissions). While the effectiveness of possible mitigation options has been extensively examined in croplands, little is known about their effectiveness in reducing N‐oxide emissions from acidic soils of rapidly expanding tea plantations in China. Here, we conducted a 2‐year field experiment to investigate how organic substitution for synthetic fertilizer and biochar amendment affect soil N‐oxide emissions from a subtropical tea plantation. Across the 2‐year measurement period, full organic substitution for synthetic fertilizer significantly increased N2O emissions by an average of 17% while had a lower NO emission compared to synthetic fertilizer alone. Our global meta‐analysis further revealed that full or partial organic fertilizer substitution resulted in a 29% (95% confidence interval: 5%–60%) increase of N2O emissions from acidic soils. In contrast, irrespective of fertilizer type, biochar amendment significantly reduced N2O emissions by 14% in the first but not second experimental year, suggesting a transient effect. The trade‐off effect of full organic substitution on N2O and NO emissions may be attributed to the favorable conditions for N2O production due to the stimulated activity of nitrifiers and denitrifiers. The suppression of N2O emission following biochar amendment was probably due to promoted further reduction of N2O to dinitrogen. The fertilizer‐induced emission factor (EF) of N2O (2.1%) in the tea plantation was greater than the current IPCC default value, but the EF of NO (0.8%) was comparable to the global estimate. Taken together, while biochar amendment could have mitigation potential, cautions are needed when applying organic substitution for synthetic fertilizer as mitigation options for acidic soils as hotspots of N‐oxide emissions.
FaceCaps for facial expression recognition

Facial expression recognition (FER) is a significant research task in the computer vision field. In this paper, we present a novel network FaceCaps for facial expression recognition with the following novel characteristics: an embedding structure based on a Capsule network which encodes relative spatial relationships between features; incorporates the feature polymerization property of FaceNet, thus offering a more efficient approach to discriminate complex facial expressions; a target reconstruction loss as a better regularization term for Capsule networks. Experimental results on both lab‐controlled datasets (CK+) and real‐world databases (RAF‐DB and SFEW 2.0) demonstrate that the method significantly outperforms the state‐of‐the‐art.
Comparison-based Conversational Recommender System with Relative Bandit Feedback

With the recent advances of conversational recommendations, the recommender system is able to actively and dynamically elicit user preference via conversational interactions. To achieve this, the system periodically queries users' preference on attributes and collects their feedback. However, most existing conversational recommender systems only enable the user to provide absolute feedback to the attributes. In practice, the absolute feedback is usually limited, as the users tend to provide biased feedback when expressing the preference. Instead, the user is often more inclined to express comparative preferences, since user preferences are inherently relative. To enable users to provide comparative preferences during conversational interactions, we propose a novel comparison-based conversational recommender system. The relative feedback, though more practical, is not easy to be incorporated since its feedback scale is always mismatched with users' absolute preferences. With effectively collecting and understanding the relative feedback from an interactive manner, we further propose a new bandit algorithm, which we call RelativeConUCB. The experiments on both synthetic and real-world datasets validate the advantage of our proposed method, compared to the existing bandit algorithms in the conversational recommender systems.
CapGlasses: Untethered Capacitive Sensing with Smart Glasses

Augmenting the human body using wearable technology can be particularly interesting to sense context. The user’s context includes the mental and physical state, which is inferable by detecting facial and head related gestures. For the recognition of these gestures, we propose instrumenting a pair of glasses with Capacitive Sensing (CapSense) technology. We demonstrate proximity sensing with CapSense for mobile use despite its commonly known limitations in context of mobility. Moreover, we demonstrate how to incorporate transparent sensing electrodes into the glass and copper electrodes into the frame while being potentially invisible in a future specs product. We demonstrate an untethered battery-powered glasses prototype, CapGlasses, to sense facial expressions and head gestures. We selected a set of 12 gestures and ran a study with 12 users. We obtained an average accuracy of 89.6% by a user-dependent machine learning model. We focused on providing clear documentation to enable a straightforward replication of our technology.
Virtual Whiskers: Spatial Directional Guidance using Cheek Haptic Stimulation in a Virtual Environment

Spatial cues are an important element of navigating people in physical/virtual spaces. In terms of spatial navigation, integrating vision with other modalities, such as haptics, can guide users more effectively. Haptic cues are presented on the body parts that are sensitive to stimuli such as hands and a head. The head is reported to be superior to the body for spatial directional perception. In this paper, we propose Virtual Whiskers, a spatial directional guidance technique by haptic stimulation of the cheeks using tiny robot arms attached to a Head-Mounted Display (HMD). We deploy photo reflective sensors attached to the tip of 2 robotic arms to detect the distance between the tip and the cheek surface. Using the robot arms, we stimulate a point on the cheek obtained by calculating an intersection between the cheek surface and the target direction. We experimentally investigated how accurately participants identify the target direction provided by our guidance method. We evaluated an error between the actual target direction and the participant’s pointed direction. The experimental result shows that our method achieves the average absolute directional error of 2.76 degrees in the azimuthal plane and 7.32 degrees in the elevation plane. We also conducted a spatial guidance experiment to evaluate task performance in a target search task. We compared the condition of only vision and vision with haptics for task completion time. The average of task completion time in visual-only condition was M=12.45 s, SD=14.51 s, and visual with haptic condition resulted in M=6.91 s, SD=5.48 s. Statistical test revealed a significant difference in task completion time between the visual condition and the visual+haptic condition.
Underestimation Refinement: A General Enhancement Strategy for Exploration in Recommendation Systems

Click-through rate (CTR) prediction based on deep neural networks has made significant progress in recommendation systems. However, these methods often suffer from CTR underestimation due to insufficient impressions for long-tail items. When formalizing CTR prediction as a contextual bandit problem, exploration methods provide a natural solution addressing this issue. In this paper, we first benchmark state-of-the-art exploration methods in the recommendation system setting. We find that the combination of gradient-based uncertainty modeling and Thompson Sampling achieves a significant advantage. On the basis of the benchmark, we further propose a general enhancement strategy, Underestimation Refinement (UR), which explicitly incorporates the prior knowledge that insufficient impressions likely leads to CTR underestimation. This strategy is applicable to almost all the existing exploration methods. Experimental results validate UR's effectiveness, achieving consistent improvement across all baseline exploration methods.
Enhancing of the Composting Rate of Spent Mushroom Substrate by Rock Dust

The composting of spent mushroom substrate yields a product that is valuable for agricultural utilization and soil reclamation. Experiments were conducted to determine if the inclusion of rock dust, a mineral waste, could accelerate the composting process. The composting model system consisted of a reactor with an integrated control system that controlled the temperature and oxygen concentration of the composting mass. The addition of rock dust increased indicators of microbial activity in the start-up period of the composting process, the mesophilic phase, resulting in increased temperature and protein, and higher oxygen requirements.
Reducing Muscle Activity when Playing Tremolo by Using Electrical Muscle Stimulation

When beginners play the piano, the activity of the forearm muscles tends to be greater than that of experts because beginners move their fingers with more force than necessary. Reducing forearm muscle activity is important for pianists to prevent fatigue and injury. However, it is difficult for beginners to learn how to do so. In this paper, we propose using electrical muscle stimulation (EMS) to teach beginners how to reduce this muscle activity while playing a tremolo. Since experts use wrist rotation efficiently when playing tremolos, we apply EMS not to muscles that are relevant to moving the fingers but to the supinator muscle and the pronator teres muscle, which are involved in wrist rotation. We conducted a user study with eight beginners to investigate how the forearm muscle activity changed through intervention with the EMS support system. After practicing with EMS, the activity level of the extensor pollicis longus muscle and extensor digitorum muscle on the forearm was significantly lower, and the participants felt less fatigue when playing tremolos. The results suggest that an EMS support system can reduce target muscle activity by applying EMS to other muscles to teach how to move limbs efficiently.
Lighter and Better: Low-Rank Decomposed Self-Attention Networks for Next-Item Recommendation

Self-attention networks (SANs) have been intensively applied for sequential recommenders, but they are limited due to: (1) the quadratic complexity and vulnerability to over-parameterization in self-attention; (2) inaccurate modeling of sequential relations between items due to the implicit position encoding. In this work, we propose the low-rank decomposed self-attention networks (LightSANs) to overcome these problems. Particularly, we introduce the low-rank decomposed self-attention, which projects user's historical items into a small constant number of latent interests and leverages item-to-interest interaction to generate the context-aware representation. It scales linearly w.r. t. the user's historical sequence length in terms of time and space, and is more resilient to over-parameterization. Besides, we design the decoupled position encoding, which models the sequential relations between items more precisely. Extensive experimental studies are carried out on three real-world datasets, where LightSANs outperform the existing SANs-based recommenders in terms of both effectiveness and efficiency.
Composite Code Sparse Autoencoders for First Stage Retrieval

We present a Composite Code Sparse Autoencoder (CCSA) approach for Approximate Nearest Neighbor (ANN) search of document representations based on Siamese-BERT models. In Information Retrieval (IR), the ranking pipeline is generally decomposed in two stages: the first stage focuses on retrieving a candidate set from the whole collection. The second stage re-ranks the candidates by relying on more complex models. Recently, Siamese-BERT models have been used as first stage rankers to replace or complement the traditional bag-of-words models. However, indexing and searching a large document collection requires efficient similarity search on dense vectors and this is why ANN techniques come into play. Since composite codes are naturally sparse, we show how CCSA can learn efficient parallel inverted index thanks to an uniformity regularizer. Our experiments on MS MARCO reveal that for the same quantization budget and recall@1000 targets, CCSA is able to outperform IVF (inverted-index file) with product quantization on both
Counterfactual Data-Augmented Sequential Recommendation

Sequential recommendation aims at predicting users' preferences based on their historical behaviors. However, this recommendation strategy may not perform well in practice due to the sparsity of the real-world data. In this paper, we propose a novel counterfactual data augmentation framework to mitigate the impact of the imperfect training data and empower sequential recommendation models. Our framework is composed of a sampler model and an anchor model. The sampler model aims to generate new user behavior sequences based on the observed ones, while the anchor model is leveraged to provide the final recommendation list, which is trained based on both observed and generated sequences. We design the sampler model to answer the key counterfactual question: "what would a user like to buy if her previously purchased items had been different? ". Beyond heuristic intervention methods, we leverage two learning-based methods to implement the sampler model, and thus, improve the quality of the generated sequences when training the anchor model. Additionally, we analyze the influence of the generated sequences on the anchor model in theory and achieve a trade-off between the information and the noise introduced by the generated sequences. Experiments on nine real-world datasets demonstrate our framework's effectiveness and generality.
Podcast Metadata and Content: Episode Relevance and Attractiveness in Ad Hoc Search

Rapidly growing online podcast archives contain diverse content on a wide range of topics. These archives form an important resource for entertainment and professional use, but their value can only be realized if users can rapidly and reliably locate content of interest. Search for relevant content can be based on metadata provided by content creators, but also on transcripts of the spoken content itself. Excavating relevant content from deep within these audio streams for diverse types of information needs requires varying the approach to systems prototyping. We describe a set of diverse podcast information needs and different approaches to assessing retrieved content for relevance. We use these information needs in an investigation of the utility and effectiveness of these information sources. Based on our analysis, we recommend approaches for indexing and retrieving podcast content for ad hoc search.
Make It Easy: An Effective End-to-End Entity Alignment Framework

Entity alignment (EA) is a prerequisite for enlarging the coverage of a unified knowledge graph. Previous EA approaches either restrain the performance due to inadequate information utilization or need labor-intensive pre-processing to get external or reliable information to perform the EA task. This paper proposes EASY, an effective end-to-end EA framework, which is able to (i) remove the labor-intensive pre-processing by fully discovering the name information provided by the entities themselves; and (ii) jointly fuse the features captured by the names of entities and the structural information of the graph to improve the EA results. Specifically, EASY first introduces NEAP, a highly effective name-based entity alignment procedure, to obtain an initial alignment that has reasonable accuracy and meanwhile does not require much memory consumption or any complex training process. Then, EASY invokes SRS, a novel structure-based refinement strategy, to iteratively correct the misaligned entities generated by NEAP to further enhance the entity alignment. Extensive experiments demonstrate the superiority of our proposed EASY with significant improvement against 13 existing state-of-the-art competitors.
Safety and Efficacy of Intravitreal Chemotherapy (Melphalan) to Treat Vitreous Seeds in Retinoblastoma

Background: Active vitreous seeds in eyes with retinoblastoma (Rb) adversely affects the treatment outcome. This study aimed to investigate the safety and efficacy of intravitreal melphalan chemotherapy (IViC) as a treatment for recurrent and refractory vitreous seeds in patients with Rb. Methods: We used a retrospective non-comparative study of patients with intraocular Rb who had vitreous seeds and were treated by IViC (20–30 μg of melphalan) using the safety-enhanced anti-reflux technique. Tumor response, ocular toxicity, demographics, clinical features, and survival were analyzed. Results: In total, 27 eyes were treated with 108 injections for recurrent (16 eyes) or refractory (11 eyes) vitreous seeds after failed systemic chemotherapy. A total of 15 (56%) were males, and 20 (74%) had bilateral disease. At diagnosis, the majority (n = 21) of the injected eyes were group D, and n = 6 were group C. Vitreous seeds showed complete regression in 21 (78%) eyes; 100% (n = 10) for eyes with focal seeds; 65% (n = 11/17 eyes) for eyes with diffuse seeds (p = 0.04); 7 (64%) eyes with refractory seeds; and 14 (87%) eyes with recurrent seeds showed complete response (p = 0.37). In total, 16 (59%) eyes developed side effects: retinal toxicity (48%), pupillary synechiae (15%), cataracts (30%), iris atrophy (7%), and retinal and optic atrophy (4%). Only one child was lost to follow-up whose family refused enucleation and none developed orbital tumor recurrence or distant metastasis. Conclusion: IViC with melphalan is effective (more for focal than diffuse seeding) and a relatively safe treatment modality for Rb that can improve the outcomes of eye salvage procedures. However, unexpected toxicity can occur even with the standard dose of 20–30 μg.
Effect of Intermittent Fasting on Non-Alcoholic Fatty Liver Disease: Systematic Review and Meta-Analysis

Background: Weight loss by lifestyle modification is the cornerstone therapy of non-alcoholic fatty liver disease (NAFLD). Intermittent fasting has shown favorable effects on body weight (BW) and relevant indicators of NAFLD in several reports. Objective: To estimate the effects of intermittent fasting on adults with NAFLD. Materials and methods: Literature searches were conducted on PubMed, EMBASE, Web of Science, Cochrane Library, and ClinicalTrials.gov from inception to May 10, 2021. Results: A total of six studies involving 417 patients with NAFLD were included. In the meta-analysis, there were significant differences in BW, body mass index (BMI), alanine aminotransferase (ALT), and aspartate transaminase (AST) between the control and fasting group. Up to now, there is no significant difference in triglycerides (TG), total cholesterol (TC), and other metabolic parameters between the two groups. Conclusions: Intermittent fasting is beneficial for weight management and liver enzyme improvement, but long-term feasibility and safety of intermittent fasting should be conducted in further studies.
The Risk of Immune-Related Thyroid Dysfunction Induced by PD-1/PD-L1 Inhibitors in Cancer Patients: An Updated Systematic Review and Meta-Analysis

Background Thyroid dysfunction is common for cancer patients receiving PD-1/PD-L1 inhibitor therapies. To clarify the incidence risk of thyroid dysfunction would be important for guiding anti-PD-1 and anti-PD-L1 immunotherapy. Therefore, the updated meta-analysis was conducted to evaluate the incidence risk of thyroid dysfunction caused by PD-1/PD-L1 inhibitors. Methods PD-1/PD-L1 inhibitor related clinical trials were collected by a systematic search of the PubMed. Some relevant studies were identified by a manual search. The incidence risk of all grades and grades 3-5 was analyzed and evaluated by random effect model. The Newcastle Ottawa Scale was used for the quality assessment of all clinical trials. Results Forty-three clinical trials were collected. Compared with chemotherapy, the risk of hypothyroidism of all grades was significantly higher (OR=7.15, 95%CI:[4.85, 10.55], I2 = 40%, Z=9.91(P <0.00001)) in PD-1/PD-L1 group. Similar results could also be noted, when the control group was placebo or CTLA-4. When PD-1/PD-L1 was combined with other treatments for cancer patients, the risk of hypothyroidism of all grades was also significantly increased. Similar to the analysis results of hypothyroidism, PD-1/PD-L1 inhibitors played the same role in increasing the risk of hyperthyroidism and thyroiditis. Few significant analysis results was noted, when the risk of thyroid dysfunction of grades 3-5 was assessed. Conclusion Whether used alone or in combination with other anti-tumor drugs, PD-1/PD-L1 inhibitors increased the risk of thyroid dysfunction, especially for hypothyroidism. Furthermore, PD-1/PD-L1 was better than chemotherapy and CTLA-4 in increasing the risk of thyroid dysfunction.
Functional Connectivity Density for Radiation Encephalopathy Prediction in Nasopharyngeal Carcinoma

The diagnostic efficiency of radiation encephalopathy (RE) remains heterogeneous, and prediction of RE is difficult at the pre-symptomatic stage. We aimed to analyze the whole-brain resting-state functional connectivity density (FCD) of individuals with pre-symptomatic RE using multivariate pattern analysis (MVPA) and explore its prediction efficiency. Resting data from NPC patients with nasopharyngeal carcinoma (NPC; consisting of 20 pre-symptomatic RE subjects and 26 non-RE controls) were collected in this study. We used MVPA to classify pre-symptomatic RE subjects from non-RE controls based on FCD maps. Classifier performances were evaluated by accuracy, sensitivity, specificity, and area under the characteristic operator curve. Permutation tests and leave-one-out cross-validation were applied for assessing classifier performance. MVPA was able to differentiate pre-symptomatic RE subjects from non-RE controls using global FCD as a feature, with a total accuracy of 89.13%. The temporal lobe as well as regions involved in the visual processing system, the somatosensory system, and the default mode network (DMN) revealed robust discrimination during classification. Our findings suggest a good classification efficiency of global FCD for the individual prediction of RE at a pre-symptomatic stage. Moreover, the discriminating regions may contribute to the underlying mechanisms of sensory and cognitive disturbances in RE.